diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240.dat new file mode 100644 index 000000000..94b36abc4 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0240 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0240_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3120 0.0100 -1 + -160 0.6240 0.0706 -1 + -150 0.9360 0.2428 -1 + -140 0.7699 0.4549 -1 + -130 0.6312 0.6818 -1 + -120 0.4861 0.8973 -1 + -110 0.3275 1.0763 -1 + -100 0.1610 1.1983 -1 + -90 0.0000 1.2500 -1 + -80 -0.1610 1.1983 -1 + -70 -0.3275 1.0763 -1 + -60 -0.4861 0.8973 -1 + -50 -0.6312 0.6818 -1 + -40 -0.7699 0.4549 -1 + -30 -0.9360 0.2428 -1 + -20 -0.6782 0.1306 -1 + -10 -0.6006 0.0183 -2.3658 + -9 -0.5703 0.0150 -2.1354 + -8 -0.5401 0.0124 -1.9128 + -7 -0.4863 0.0111 -1.6564 + -6 -0.3911 0.0101 -1.4075 + -5 -0.2829 0.0093 -1.2098 + -4 -0.1699 0.0086 -1.0246 + -3 -0.0547 0.0076 -0.9055 + -2 0.0671 0.0074 -0.9047 + -1 0.1884 0.0073 -0.9888 + 0 0.3092 0.0074 -1.0777 + 1 0.4307 0.0074 -1.1694 + 2 0.5503 0.0076 -1.2608 + 3 0.6677 0.0078 -1.3557 + 4 0.7807 0.0082 -1.4512 + 5 0.8921 0.0086 -1.5561 + 6 0.9830 0.0097 -1.6648 + 7 1.0196 0.0114 -1.7468 + 8 1.0527 0.0137 -1.8733 + 9 1.0932 0.0166 -2.0257 + 10 1.1352 0.0200 -2.1849 + 11 1.1432 0.0259 -2.3027 + 12 1.1730 0.0313 -2.5537 + 13 1.1826 0.0392 -2.7674 + 14 1.2087 0.0464 -3.0323 + 15 1.2272 0.0550 -3.2908 + 16 1.2557 0.0634 -3.5959 + 17 1.2806 0.0725 -3.9100 + 18 1.3037 0.0825 -4.2419 + 19 1.3252 0.0930 -4.5743 + 20 1.3439 0.1043 -4.9173 + 21 1.3613 0.1159 -5.2685 + 22 1.3809 0.1271 -5.6456 + 23 1.3894 0.1401 -5.9876 + 24 1.3961 0.1532 -6.3248 + 25 1.3970 0.1669 -6.6429 + 26 1.3922 0.1814 -6.9151 + 28 1.3673 0.2112 -7.3108 + 29 1.3488 0.2278 -7.4112 + 30 1.3371 0.2428 -7.5348 + 40 1.0999 0.4549 -1 + 50 0.9017 0.6818 -1 + 60 0.6944 0.8973 -1 + 70 0.4678 1.0763 -1 + 80 0.2300 1.1983 -1 + 90 0.0000 1.2500 -1 + 100 -0.1610 1.1983 -1 + 110 -0.3275 1.0763 -1 + 120 -0.4861 0.8973 -1 + 130 -0.6312 0.6818 -1 + 140 -0.7699 0.4549 -1 + 150 -0.9360 0.2428 -1 + 160 -0.6240 0.0706 -1 + 170 -0.3120 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3270 0.0100 -1 + -160 0.6541 0.0587 -1 + -150 0.9811 0.2318 -1 + -140 0.7974 0.4451 -1 + -130 0.6474 0.6736 -1 + -120 0.4948 0.8909 -1 + -110 0.3312 1.0719 -1 + -100 0.1619 1.1961 -1 + -90 0.0000 1.2500 -1 + -80 -0.1619 1.1961 -1 + -70 -0.3312 1.0719 -1 + -60 -0.4948 0.8909 -1 + -50 -0.6474 0.6736 -1 + -40 -0.7974 0.4451 -1 + -30 -0.9811 0.2318 -1 + -20 -0.7169 0.1237 -1 + -10 -0.6467 0.0156 -2.4629 + -9 -0.5996 0.0132 -2.1912 + -8 -0.5801 0.0111 -1.9816 + -7 -0.4950 0.0100 -1.6720 + -6 -0.3910 0.0092 -1.4078 + -5 -0.2810 0.0085 -1.2080 + -4 -0.1639 0.0080 -1.0184 + -3 -0.0454 0.0073 -0.8991 + -1 0.1982 0.0065 -0.9945 + 0 0.3213 0.0064 -1.0853 + 1 0.4423 0.0066 -1.1768 + 2 0.5645 0.0066 -1.2701 + 5 0.8977 0.0083 -1.5589 + 6 0.9769 0.0097 -1.6615 + 7 1.0311 0.0109 -1.7560 + 9 1.1327 0.0147 -2.0745 + 10 1.1526 0.0188 -2.2074 + 11 1.1873 0.0230 -2.3877 + 12 1.2028 0.0291 -2.6164 + 13 1.2313 0.0351 -2.8768 + 14 1.2601 0.0417 -3.1622 + 15 1.2941 0.0485 -3.4718 + 16 1.3216 0.0564 -3.7920 + 17 1.3498 0.0647 -4.1401 + 18 1.3733 0.0740 -4.4868 + 19 1.3966 0.0838 -4.8430 + 20 1.4115 0.0951 -5.1959 + 21 1.4270 0.1066 -5.5559 + 22 1.4404 0.1187 -5.9301 + 23 1.4449 0.1319 -6.2704 + 24 1.4587 0.1437 -6.6837 + 25 1.4594 0.1573 -7.0264 + 26 1.4601 0.1706 -7.3616 + 27 1.4562 0.1845 -7.6585 + 28 1.4482 0.1989 -7.9116 + 29 1.4181 0.2163 -7.9793 + 30 1.4016 0.2318 -8.1104 + 40 1.1391 0.4451 -1 + 50 0.9249 0.6736 -1 + 60 0.7068 0.8909 -1 + 70 0.4731 1.0719 -1 + 80 0.2313 1.1961 -1 + 90 0.0000 1.2500 -1 + 100 -0.1619 1.1961 -1 + 110 -0.3312 1.0719 -1 + 120 -0.4948 0.8909 -1 + 130 -0.6474 0.6736 -1 + 140 -0.7974 0.4451 -1 + 150 -0.9811 0.2318 -1 + 160 -0.6541 0.0587 -1 + 170 -0.3270 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3392 0.0100 -1 + -160 0.6784 0.0496 -1 + -150 1.0176 0.2235 -1 + -140 0.8196 0.4377 -1 + -130 0.6605 0.6674 -1 + -120 0.5018 0.8861 -1 + -110 0.3342 1.0686 -1 + -100 0.1627 1.1945 -1 + -90 0.0000 1.2500 -1 + -80 -0.1627 1.1945 -1 + -70 -0.3342 1.0686 -1 + -60 -0.5018 0.8861 -1 + -50 -0.6605 0.6674 -1 + -40 -0.8196 0.4377 -1 + -30 -1.0176 0.2235 -1 + -20 -0.7445 0.1188 -1 + -10 -0.6733 0.0141 -2.5188 + -9 -0.6311 0.0119 -2.2492 + -8 -0.5908 0.0105 -2.0003 + -7 -0.4967 0.0096 -1.6748 + -6 -0.3927 0.0088 -1.4104 + -5 -0.2789 0.0081 -1.2057 + -4 -0.1611 0.0077 -1.0152 + -3 -0.0419 0.0072 -0.8970 + -2 0.0793 0.0064 -0.9117 + -1 0.2025 0.0061 -0.9971 + 0 0.3254 0.0061 -1.0876 + 1 0.4490 0.0061 -1.1811 + 2 0.5682 0.0064 -1.2727 + 4 0.7966 0.0073 -1.4660 + 5 0.8946 0.0084 -1.5586 + 6 0.9801 0.0096 -1.6568 + 7 1.0446 0.0105 -1.7694 + 8 1.1065 0.0117 -1.9317 + 9 1.1411 0.0142 -2.0844 + 10 1.1776 0.0175 -2.2415 + 11 1.2014 0.0220 -2.4154 + 12 1.2315 0.0270 -2.6770 + 13 1.2585 0.0329 -2.9389 + 14 1.2967 0.0387 -3.2543 + 15 1.3269 0.0455 -3.5636 + 16 1.3566 0.0529 -3.8959 + 17 1.3838 0.0611 -4.2519 + 19 1.4278 0.0800 -4.9627 + 20 1.4607 0.0886 -5.3962 + 21 1.4730 0.1002 -5.7535 + 22 1.4807 0.1128 -6.1201 + 23 1.4853 0.1259 -6.4786 + 24 1.4917 0.1388 -6.8685 + 25 1.4944 0.1520 -7.2354 + 26 1.4918 0.1657 -7.5648 + 27 1.4848 0.1800 -7.8540 + 28 1.4765 0.1943 -8.1190 + 29 1.4665 0.2087 -8.3538 + 30 1.4537 0.2235 -8.5446 + 40 1.1708 0.4377 -1 + 50 0.9436 0.6674 -1 + 60 0.7169 0.8861 -1 + 70 0.4775 1.0686 -1 + 80 0.2324 1.1945 -1 + 90 0.0000 1.2500 -1 + 100 -0.1627 1.1945 -1 + 110 -0.3342 1.0686 -1 + 120 -0.5018 0.8861 -1 + 130 -0.6605 0.6674 -1 + 140 -0.8196 0.4377 -1 + 150 -1.0176 0.2235 -1 + 160 -0.6784 0.0496 -1 + 170 -0.3392 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.4065 0.0100 -1 + -160 0.8130 0.0656 -1 + -150 0.9585 0.2382 -1 + -140 0.7836 0.4508 -1 + -130 0.6393 0.6784 -1 + -120 0.4904 0.8946 -1 + -110 0.3293 1.0744 -1 + -100 0.1615 1.1974 -1 + -90 0.0000 1.2500 -1 + -80 -0.1615 1.1974 -1 + -70 -0.3293 1.0744 -1 + -60 -0.4904 0.8946 -1 + -50 -0.6393 0.6784 -1 + -40 -0.7836 0.4508 -1 + -30 -0.9585 0.2382 -1 + -20 -0.8400 0.1070 -1 + -10 -0.6835 0.0136 -2.5481 + -9 -0.6551 0.0110 -2.2945 + -8 -0.5990 0.0100 -2.0149 + -5 -0.2785 0.0079 -1.2051 + -4 -0.1605 0.0074 -1.0146 + -3 -0.0396 0.0070 -0.8858 + -2 0.0815 0.0064 -0.9130 + -1 0.2046 0.0060 -0.9981 + 0 0.3288 0.0059 -1.0898 + 1 0.4505 0.0059 -1.1819 + 2 0.5696 0.0062 -1.2742 + 3 0.6869 0.0066 -1.3692 + 4 0.7958 0.0074 -1.4563 + 6 0.9864 0.0094 -1.6557 + 7 1.0636 0.0101 -1.7862 + 8 1.1217 0.0112 -1.9495 + 9 1.1532 0.0137 -2.0998 + 11 1.2140 0.0213 -2.4399 + 12 1.2522 0.0256 -2.7207 + 13 1.2922 0.0304 -3.0194 + 15 1.3624 0.0423 -3.6630 + 16 1.3922 0.0494 -4.0053 + 17 1.4225 0.0570 -4.3795 + 18 1.4431 0.0660 -4.7296 + 19 1.4663 0.0753 -5.1108 + 20 1.4819 0.0860 -5.4818 + 22 1.4994 0.1103 -6.2066 + 25 1.5113 0.1495 -7.3351 + 26 1.5098 0.1631 -7.6774 + 30 1.3693 0.2382 -6.1797 + 40 1.1195 0.4508 -1 + 50 0.9133 0.6784 -1 + 60 0.7006 0.8946 -1 + 70 0.4705 1.0744 -1 + 80 0.2307 1.1974 -1 + 90 0.0000 1.2500 -1 + 100 -0.1615 1.1974 -1 + 110 -0.3293 1.0744 -1 + 120 -0.4904 0.8946 -1 + 130 -0.6393 0.6784 -1 + 140 -0.7836 0.4508 -1 + 150 -0.9585 0.2382 -1 + 160 -0.8130 0.0656 -1 + 170 -0.4065 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3508 0.0100 -1 + -160 0.7017 0.0400 -1 + -150 1.0525 0.2147 -1 + -140 0.8408 0.4299 -1 + -130 0.6731 0.6609 -1 + -120 0.5085 0.8810 -1 + -110 0.3371 1.0651 -1 + -100 0.1634 1.1927 -1 + -90 0.0000 1.2500 -1 + -80 -0.1634 1.1927 -1 + -70 -0.3371 1.0651 -1 + -60 -0.5085 0.8810 -1 + -50 -0.6731 0.6609 -1 + -40 -0.8408 0.4299 -1 + -30 -1.0525 0.2147 -1 + -20 -0.7646 0.1141 -1 + -10 -0.6811 0.0135 -2.5243 + -9 -0.6851 0.0106 -2.3544 + -8 -0.6061 0.0097 -2.0276 + -6 -0.3941 0.0082 -1.4146 + -4 -0.1596 0.0072 -1.0137 + -3 -0.0388 0.0069 -0.8871 + -2 0.0830 0.0064 -0.9138 + -1 0.2067 0.0059 -0.9994 + 0 0.3302 0.0058 -1.0906 + 1 0.4512 0.0059 -1.1825 + 2 0.5700 0.0062 -1.2762 + 4 0.7938 0.0075 -1.4517 + 5 0.8930 0.0085 -1.5475 + 6 0.9896 0.0093 -1.6620 + 7 1.0843 0.0099 -1.8045 + 8 1.1285 0.0111 -1.9571 + 9 1.1695 0.0131 -2.1205 + 10 1.1873 0.0169 -2.2387 + 11 1.2351 0.0201 -2.4820 + 12 1.2707 0.0244 -2.7598 + 13 1.3094 0.0292 -3.0601 + 14 1.3479 0.0345 -3.3831 + 16 1.4132 0.0475 -4.0705 + 17 1.4381 0.0554 -4.4302 + 18 1.4631 0.0639 -4.7988 + 19 1.4825 0.0734 -5.1723 + 20 1.4971 0.0841 -5.5428 + 21 1.5047 0.0960 -5.8916 + 24 1.5435 0.1310 -7.1532 + 25 1.5472 0.1439 -7.5449 + 26 1.5476 0.1572 -7.9128 + 27 1.5436 0.1707 -8.246 + 28 1.5338 0.1849 -8.5292 + 29 1.5197 0.1997 -8.7629 + 30 1.5036 0.2147 -8.9588 + 40 1.2012 0.4299 -1 + 50 0.9615 0.6609 -1 + 60 0.7265 0.8810 -1 + 70 0.4816 1.0651 -1 + 80 0.2334 1.1927 -1 + 90 0.0000 1.2500 -1 + 100 -0.1634 1.1927 -1 + 110 -0.3371 1.0651 -1 + 120 -0.5085 0.8810 -1 + 130 -0.6731 0.6609 -1 + 140 -0.8408 0.4299 -1 + 150 -1.0525 0.2147 -1 + 160 -0.7017 0.0400 -1 + 170 -0.3508 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3695 0.0100 -1 + -160 0.7389 0.0433 -1 + -150 1.0447 0.2176 -1 + -140 0.8361 0.4326 -1 + -130 0.6703 0.6631 -1 + -120 0.5070 0.8827 -1 + -110 0.3365 1.0663 -1 + -100 0.1632 1.1933 -1 + -90 0.0000 1.2500 -1 + -80 -0.1632 1.1933 -1 + -70 -0.3365 1.0663 -1 + -60 -0.5070 0.8827 -1 + -50 -0.6703 0.6631 -1 + -40 -0.8361 0.4326 -1 + -30 -1.0447 0.2176 -1 + -20 -0.7942 0.1103 -1 + -10 -0.6944 0.0129 -2.5538 + -7 -0.5051 0.0088 -1.6890 + -6 -0.3931 0.0082 -1.4033 + -5 -0.2781 0.0075 -1.2051 + -4 -0.1595 0.0071 -1.0133 + -3 -0.0379 0.0068 -0.8899 + -2 0.0846 0.0064 -0.9146 + -1 0.2078 0.0058 -1.0002 + 0 0.3310 0.0058 -1.0908 + 1 0.4519 0.0058 -1.1833 + 2 0.5708 0.0061 -1.2722 + 3 0.6863 0.0066 -1.3593 + 4 0.7943 0.0074 -1.4554 + 5 0.8937 0.0084 -1.5503 + 6 0.9965 0.0090 -1.6685 + 7 1.0919 0.0097 -1.8120 + 8 1.1354 0.0109 -1.9652 + 10 1.2027 0.0161 -2.2646 + 11 1.2478 0.0194 -2.5076 + 12 1.2911 0.0232 -2.8034 + 13 1.3319 0.0276 -3.1135 + 14 1.3618 0.0335 -3.4179 + 15 1.3958 0.0395 -3.7559 + 17 1.4529 0.0540 -4.4787 + 18 1.4764 0.0625 -4.8444 + 19 1.4956 0.0720 -5.2221 + 20 1.5053 0.0831 -5.5750 + 22 1.5459 0.1037 -6.4227 + 23 1.5538 0.1161 -6.8324 + 24 1.5595 0.1287 -7.2408 + 25 1.5622 0.1418 -7.6314 + 26 1.5612 0.1552 -7.9964 + 27 1.5539 0.1691 -8.3139 + 28 1.5433 0.1835 -8.595 + 29 1.5306 0.1980 -8.8443 + 30 1.4925 0.2176 -8.363771554 + 40 1.1944 0.4326 -1 + 50 0.9575 0.6631 -1 + 60 0.7243 0.8827 -1 + 70 0.4807 1.0663 -1 + 80 0.2332 1.1933 -1 + 90 0.0000 1.2500 -1 + 100 -0.1632 1.1933 -1 + 110 -0.3365 1.0663 -1 + 120 -0.5070 0.8827 -1 + 130 -0.6703 0.6631 -1 + 140 -0.8361 0.4326 -1 + 150 -1.0447 0.2176 -1 + 160 -0.7389 0.0433 -1 + 170 -0.3695 0.0100 -1 + 180 0.0000 0.0100 -1 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180 0.0000 0.0100 -1 + -170 0.3561 0.0100 -1 + -160 0.7121 0.0361 -1 + -150 1.0682 0.2110 -1 + -140 0.8504 0.4267 -1 + -130 0.6787 0.6582 -1 + -120 0.5115 0.8789 -1 + -110 0.3384 1.0637 -1 + -100 0.1637 1.1919 -1 + -90 0.0000 1.2500 -1 + -80 -0.1637 1.1919 -1 + -70 -0.3384 1.0637 -1 + -60 -0.5115 0.8789 -1 + -50 -0.6787 0.6582 -1 + -40 -0.8504 0.4267 -1 + -30 -1.0682 0.2110 -1 + -20 -0.7818 0.1117 -1 + -10 -0.7078 0.0124 -2.5820 + -9 -0.6882 0.0105 -2.3471 + -8 -0.6127 0.0092 -2.0403 + -6 -0.3930 0.0081 -1.4009 + -5 -0.2780 0.0074 -1.2048 + -4 -0.1594 0.0069 -1.0132 + -3 -0.0371 0.0067 -0.8903 + -2 0.0857 0.0063 -0.9153 + -1 0.2083 0.0058 -1.0004 + 0 0.3320 0.0057 -1.0916 + 1 0.4508 0.0059 -1.1835 + 2 0.5707 0.0061 -1.2714 + 3 0.6849 0.0067 -1.3575 + 5 0.8968 0.0083 -1.5536 + 6 0.9995 0.0090 -1.6719 + 7 1.0994 0.0095 -1.8194 + 8 1.1396 0.0107 -1.9697 + 11 1.2595 0.0188 -2.5308 + 12 1.3027 0.0225 -2.8278 + 14 1.3829 0.0318 -3.4708 + 16 1.4381 0.0453 -4.1475 + 18 1.4876 0.0613 -4.8828 + 21 1.5477 0.0904 -6.0795 + 22 1.5578 0.1021 -6.4768 + 23 1.5668 0.1143 -6.8990 + 24 1.5715 0.1270 -7.3058 + 25 1.5731 0.1402 -7.6939 + 26 1.5686 0.1540 -8.0423 + 27 1.5615 0.1680 -8.3631 + 28 1.5521 0.1821 -8.6566 + 29 1.5406 0.1964 -8.9183 + 30 1.5260 0.2110 -9.1387 + 40 1.2148 0.4267 -1 + 50 0.9696 0.6582 -1 + 60 0.7308 0.8789 -1 + 70 0.4835 1.0637 -1 + 80 0.2339 1.1919 -1 + 90 0.0000 1.2500 -1 + 100 -0.1637 1.1919 -1 + 110 -0.3384 1.0637 -1 + 120 -0.5115 0.8789 -1 + 130 -0.6787 0.6582 -1 + 140 -0.8504 0.4267 -1 + 150 -1.0682 0.2110 -1 + 160 -0.7121 0.0361 -1 + 170 -0.3561 0.0100 -1 + 180 0.0000 0.0100 -1 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240_coords.txt new file mode 100644 index 000000000..99568655a --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0240_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0240 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.001740 0.009990 + 0.009990 0.025540 + 0.026840 0.043840 + 0.085380 0.081830 + 0.126020 0.098890 + 0.173070 0.113700 + 0.225630 0.125450 + 0.343100 0.136890 + 0.405650 0.135010 + 0.469030 0.128320 + 0.532130 0.117850 + 0.653880 0.089820 + 0.711010 0.074320 + 0.764750 0.059150 + 0.814500 0.045100 + 0.899560 0.022380 + 0.933650 0.014140 + 0.961390 0.007980 + 0.982280 0.002440 + 0.982280 0.001830 + 0.961390 0.002090 + 0.933510 0.000850 + 0.859090 -0.008040 + 0.813820 -0.016050 + 0.764090 -0.026350 + 0.710600 -0.038510 + 0.594970 -0.065500 + 0.534160 -0.078430 + 0.472340 -0.089680 + 0.410300 -0.098140 + 0.289530 -0.102840 + 0.233070 -0.098960 + 0.180640 -0.091900 + 0.133180 -0.082080 + 0.056790 -0.055970 + 0.029750 -0.040380 + 0.011270 -0.024430 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247.dat new file mode 100644 index 000000000..116e81c94 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0247 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0247_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0127 -1.0184 + -170.000 0.3091 0.0127 -1.0184 + -160.000 0.6183 0.0727 -1.0184 + -150.000 0.9274 0.2433 -1.0184 + -140.000 0.7628 0.4535 -1.0184 + -130.000 0.6254 0.6783 -1.0184 + -120.000 0.4816 0.8918 -1.0184 + -110.000 0.3245 1.0691 -1.0184 + -100.000 0.1595 1.1900 -1.0184 + -90.000 0.0000 1.2413 -1.0184 + -80.000 -0.1595 1.1900 -1.0184 + -70.000 -0.3245 1.0691 -1.0184 + -60.000 -0.4816 0.8918 -1.0184 + -50.000 -0.6254 0.6783 -1.0184 + -40.000 -0.7628 0.4535 -1.0184 + -30.000 -0.9274 0.2433 -1.0184 + -20.000 -0.6720 0.1322 -1.0184 + -10.000 -0.5951 0.0209 -2.3716 + -9.000 -0.5650 0.0176 -2.1434 + -8.000 -0.5351 0.0150 -1.9228 + -7.000 -0.4818 0.0138 -1.6688 + -6.000 -0.3875 0.0128 -1.4222 + -5.000 -0.2803 0.0120 -1.2263 + -4.000 -0.1683 0.0113 -1.0428 + -3.000 -0.0542 0.0103 -0.9248 + -2.000 0.0665 0.0101 -0.9240 + -1.000 0.1867 0.0100 -1.0073 + 0.000 0.3064 0.0101 -1.0954 + 1.000 0.4267 0.0101 -1.1863 + 2.000 0.5452 0.0103 -1.2768 + 3.000 0.6616 0.0105 -1.3708 + 4.000 0.7735 0.0109 -1.4655 + 5.000 0.8839 0.0113 -1.5694 + 6.000 0.9739 0.0124 -1.6771 + 7.000 1.0102 0.0141 -1.7583 + 8.000 1.0430 0.0163 -1.8837 + 9.000 1.0831 0.0192 -2.0347 + 10.000 1.1247 0.0226 -2.1924 + 11.000 1.1327 0.0284 -2.3091 + 12.000 1.1622 0.0338 -2.5578 + 13.000 1.1717 0.0416 -2.7695 + 14.000 1.1976 0.0487 -3.0320 + 15.000 1.2159 0.0573 -3.2881 + 16.000 1.2441 0.0656 -3.5904 + 17.000 1.2688 0.0746 -3.9016 + 18.000 1.2917 0.0845 -4.2305 + 19.000 1.3130 0.0949 -4.5598 + 20.000 1.3315 0.1061 -4.8996 + 21.000 1.3488 0.1176 -5.2476 + 22.000 1.3682 0.1287 -5.6212 + 23.000 1.3766 0.1416 -5.9601 + 24.000 1.3832 0.1546 -6.2942 + 25.000 1.3841 0.1681 -6.6093 + 26.000 1.3794 0.1825 -6.8790 + 28.000 1.3547 0.2120 -7.2711 + 29.000 1.3364 0.2285 -7.3706 + 30.000 1.3248 0.2433 -7.4930 + 40.000 1.0898 0.4535 -1.0184 + 50.000 0.8934 0.6783 -1.0184 + 60.000 0.6880 0.8918 -1.0184 + 70.000 0.4635 1.0691 -1.0184 + 80.000 0.2279 1.1900 -1.0184 + 90.000 0.0000 1.2413 -1.0184 + 100.000 -0.1595 1.1900 -1.0184 + 110.000 -0.3245 1.0691 -1.0184 + 120.000 -0.4816 0.8918 -1.0184 + 130.000 -0.6254 0.6783 -1.0184 + 140.000 -0.7628 0.4535 -1.0184 + 150.000 -0.9274 0.2433 -1.0184 + 160.000 -0.6183 0.0727 -1.0184 + 170.000 -0.3091 0.0127 -1.0184 + 180.000 0.0000 0.0127 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0161 -1.0184 + -170.000 0.3240 0.0161 -1.0184 + -160.000 0.6481 0.0643 -1.0184 + -150.000 0.9721 0.2358 -1.0184 + -140.000 0.7901 0.4472 -1.0184 + -130.000 0.6414 0.6736 -1.0184 + -120.000 0.4902 0.8889 -1.0184 + -110.000 0.3281 1.0682 -1.0184 + -100.000 0.1604 1.1913 -1.0184 + -90.000 0.0000 1.2447 -1.0184 + -80.000 -0.1604 1.1913 -1.0184 + -70.000 -0.3281 1.0682 -1.0184 + -60.000 -0.4902 0.8889 -1.0184 + -50.000 -0.6414 0.6736 -1.0184 + -40.000 -0.7901 0.4472 -1.0184 + -30.000 -0.9721 0.2358 -1.0184 + -20.000 -0.7103 0.1287 -1.0184 + -10.000 -0.6407 0.0216 -2.4678 + -9.000 -0.5941 0.0192 -2.1986 + -8.000 -0.5748 0.0172 -1.9910 + -7.000 -0.4904 0.0161 -1.6842 + -6.000 -0.3874 0.0153 -1.4225 + -5.000 -0.2784 0.0146 -1.2245 + -4.000 -0.1624 0.0141 -1.0367 + -3.000 -0.0450 0.0134 -0.9185 + -1.000 0.1964 0.0126 -1.0130 + 0.000 0.3183 0.0125 -1.1029 + 1.000 0.4382 0.0127 -1.1936 + 2.000 0.5593 0.0127 -1.2860 + 5.000 0.8894 0.0144 -1.5722 + 6.000 0.9679 0.0158 -1.6738 + 7.000 1.0216 0.0170 -1.7675 + 9.000 1.1223 0.0207 -2.0830 + 10.000 1.1420 0.0248 -2.2147 + 11.000 1.1764 0.0290 -2.3933 + 12.000 1.1917 0.0350 -2.6199 + 13.000 1.2200 0.0409 -2.8779 + 14.000 1.2485 0.0475 -3.1607 + 15.000 1.2822 0.0542 -3.4675 + 16.000 1.3094 0.0621 -3.7847 + 17.000 1.3374 0.0703 -4.1296 + 18.000 1.3607 0.0795 -4.4731 + 19.000 1.3837 0.0892 -4.8260 + 20.000 1.3985 0.1004 -5.1757 + 21.000 1.4139 0.1118 -5.5324 + 22.000 1.4271 0.1238 -5.9031 + 23.000 1.4316 0.1369 -6.2403 + 24.000 1.4453 0.1485 -6.6498 + 25.000 1.4460 0.1620 -6.9893 + 26.000 1.4467 0.1752 -7.3214 + 27.000 1.4428 0.1890 -7.6156 + 28.000 1.4349 0.2032 -7.8664 + 29.000 1.4050 0.2205 -7.9334 + 30.000 1.3887 0.2358 -8.0633 + 40.000 1.1286 0.4472 -1.0184 + 50.000 0.9164 0.6736 -1.0184 + 60.000 0.7003 0.8889 -1.0184 + 70.000 0.4687 1.0682 -1.0184 + 80.000 0.2292 1.1913 -1.0184 + 90.000 0.0000 1.2447 -1.0184 + 100.000 -0.1604 1.1913 -1.0184 + 110.000 -0.3281 1.0682 -1.0184 + 120.000 -0.4902 0.8889 -1.0184 + 130.000 -0.6414 0.6736 -1.0184 + 140.000 -0.7901 0.4472 -1.0184 + 150.000 -0.9721 0.2358 -1.0184 + 160.000 -0.6481 0.0643 -1.0184 + 170.000 -0.3240 0.0161 -1.0184 + 180.000 0.0000 0.0161 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0164 -1.0184 + -170.000 0.3361 0.0164 -1.0184 + -160.000 0.6722 0.0556 -1.0184 + -150.000 1.0082 0.2279 -1.0184 + -140.000 0.8121 0.4401 -1.0184 + -130.000 0.6544 0.6677 -1.0184 + -120.000 0.4972 0.8844 -1.0184 + -110.000 0.3311 1.0652 -1.0184 + -100.000 0.1612 1.1899 -1.0184 + -90.000 0.0000 1.2449 -1.0184 + -80.000 -0.1612 1.1899 -1.0184 + -70.000 -0.3311 1.0652 -1.0184 + -60.000 -0.4972 0.8844 -1.0184 + -50.000 -0.6544 0.6677 -1.0184 + -40.000 -0.8121 0.4401 -1.0184 + -30.000 -1.0082 0.2279 -1.0184 + -20.000 -0.7376 0.1242 -1.0184 + -10.000 -0.6671 0.0204 -2.5232 + -9.000 -0.6253 0.0182 -2.2561 + -8.000 -0.5854 0.0169 -2.0095 + -7.000 -0.4921 0.0160 -1.6870 + -6.000 -0.3891 0.0152 -1.4250 + -5.000 -0.2763 0.0145 -1.2222 + -4.000 -0.1596 0.0141 -1.0335 + -3.000 -0.0415 0.0136 -0.9164 + -2.000 0.0786 0.0128 -0.9309 + -1.000 0.2006 0.0125 -1.0155 + 0.000 0.3224 0.0125 -1.1052 + 1.000 0.4449 0.0125 -1.1979 + 2.000 0.5630 0.0128 -1.2886 + 4.000 0.7893 0.0137 -1.4801 + 5.000 0.8864 0.0148 -1.5719 + 6.000 0.9711 0.0160 -1.6692 + 7.000 1.0350 0.0169 -1.7807 + 8.000 1.0963 0.0180 -1.9415 + 9.000 1.1306 0.0205 -2.0928 + 10.000 1.1668 0.0238 -2.2485 + 11.000 1.1903 0.0282 -2.4208 + 12.000 1.2202 0.0332 -2.6800 + 13.000 1.2469 0.0390 -2.9395 + 14.000 1.2848 0.0448 -3.2520 + 15.000 1.3147 0.0515 -3.5584 + 16.000 1.3441 0.0589 -3.8876 + 17.000 1.3711 0.0670 -4.2404 + 19.000 1.4146 0.0857 -4.9446 + 20.000 1.4472 0.0942 -5.3741 + 21.000 1.4594 0.1057 -5.7281 + 22.000 1.4671 0.1182 -6.0914 + 23.000 1.4716 0.1312 -6.4466 + 24.000 1.4780 0.1440 -6.8329 + 25.000 1.4806 0.1570 -7.1964 + 26.000 1.4781 0.1706 -7.5228 + 27.000 1.4711 0.1848 -7.8093 + 28.000 1.4629 0.1990 -8.0719 + 29.000 1.4530 0.2132 -8.3045 + 30.000 1.4403 0.2279 -8.4935 + 40.000 1.1600 0.4401 -1.0184 + 50.000 0.9349 0.6677 -1.0184 + 60.000 0.7103 0.8844 -1.0184 + 70.000 0.4731 1.0652 -1.0184 + 80.000 0.2303 1.1899 -1.0184 + 90.000 0.0000 1.2449 -1.0184 + 100.000 -0.1612 1.1899 -1.0184 + 110.000 -0.3311 1.0652 -1.0184 + 120.000 -0.4972 0.8844 -1.0184 + 130.000 -0.6544 0.6677 -1.0184 + 140.000 -0.8121 0.4401 -1.0184 + 150.000 -1.0082 0.2279 -1.0184 + 160.000 -0.6722 0.0556 -1.0184 + 170.000 -0.3361 0.0164 -1.0184 + 180.000 0.0000 0.0164 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0164 -1.0184 + -170.000 0.4028 0.0164 -1.0184 + -160.000 0.8055 0.0714 -1.0184 + -150.000 0.9497 0.2425 -1.0184 + -140.000 0.7764 0.4531 -1.0184 + -130.000 0.6334 0.6786 -1.0184 + -120.000 0.4859 0.8928 -1.0184 + -110.000 0.3263 1.0710 -1.0184 + -100.000 0.1600 1.1928 -1.0184 + -90.000 0.0000 1.2449 -1.0184 + -80.000 -0.1600 1.1928 -1.0184 + -70.000 -0.3263 1.0710 -1.0184 + -60.000 -0.4859 0.8928 -1.0184 + -50.000 -0.6334 0.6786 -1.0184 + -40.000 -0.7764 0.4531 -1.0184 + -30.000 -0.9497 0.2425 -1.0184 + -20.000 -0.8323 0.1125 -1.0184 + -10.000 -0.6772 0.0199 -2.5523 + -9.000 -0.6491 0.0173 -2.3010 + -8.000 -0.5935 0.0164 -2.0240 + -5.000 -0.2759 0.0143 -1.2216 + -4.000 -0.1590 0.0138 -1.0329 + -3.000 -0.0392 0.0134 -0.9053 + -2.000 0.0807 0.0128 -0.9322 + -1.000 0.2027 0.0124 -1.0165 + 0.000 0.3258 0.0123 -1.1074 + 1.000 0.4464 0.0123 -1.1986 + 2.000 0.5644 0.0126 -1.2901 + 3.000 0.6806 0.0130 -1.3842 + 4.000 0.7885 0.0138 -1.4705 + 6.000 0.9773 0.0158 -1.6681 + 7.000 1.0538 0.0165 -1.7974 + 8.000 1.1114 0.0175 -1.9592 + 9.000 1.1426 0.0200 -2.1081 + 11.000 1.2028 0.0276 -2.4451 + 12.000 1.2407 0.0318 -2.7233 + 13.000 1.2803 0.0366 -3.0192 + 15.000 1.3499 0.0484 -3.6569 + 16.000 1.3794 0.0554 -3.9960 + 17.000 1.4094 0.0629 -4.3668 + 18.000 1.4298 0.0718 -4.7137 + 19.000 1.4528 0.0811 -5.0914 + 20.000 1.4683 0.0917 -5.4589 + 22.000 1.4856 0.1157 -6.1771 + 25.000 1.4974 0.1546 -7.2952 + 26.000 1.4959 0.1680 -7.6343 + 30.000 1.3567 0.2425 -6.1504 + 40.000 1.1092 0.4531 -1.0184 + 50.000 0.9049 0.6786 -1.0184 + 60.000 0.6941 0.8928 -1.0184 + 70.000 0.4662 1.0710 -1.0184 + 80.000 0.2286 1.1928 -1.0184 + 90.000 0.0000 1.2449 -1.0184 + 100.000 -0.1600 1.1928 -1.0184 + 110.000 -0.3263 1.0710 -1.0184 + 120.000 -0.4859 0.8928 -1.0184 + 130.000 -0.6334 0.6786 -1.0184 + 140.000 -0.7764 0.4531 -1.0184 + 150.000 -0.9497 0.2425 -1.0184 + 160.000 -0.8055 0.0714 -1.0184 + 170.000 -0.4028 0.0164 -1.0184 + 180.000 0.0000 0.0164 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0164 -1.0184 + -170.000 0.3476 0.0164 -1.0184 + -160.000 0.6952 0.0461 -1.0184 + -150.000 1.0428 0.2192 -1.0184 + -140.000 0.8331 0.4324 -1.0184 + -130.000 0.6669 0.6613 -1.0184 + -120.000 0.5038 0.8793 -1.0184 + -110.000 0.3340 1.0617 -1.0184 + -100.000 0.1619 1.1882 -1.0184 + -90.000 0.0000 1.2449 -1.0184 + -80.000 -0.1619 1.1882 -1.0184 + -70.000 -0.3340 1.0617 -1.0184 + -60.000 -0.5038 0.8793 -1.0184 + -50.000 -0.6669 0.6613 -1.0184 + -40.000 -0.8331 0.4324 -1.0184 + -30.000 -1.0428 0.2192 -1.0184 + -20.000 -0.7576 0.1195 -1.0184 + -10.000 -0.6748 0.0198 -2.5287 + -9.000 -0.6788 0.0169 -2.3603 + -8.000 -0.6005 0.0161 -2.0366 + -6.000 -0.3905 0.0146 -1.4292 + -4.000 -0.1581 0.0136 -1.0320 + -3.000 -0.0384 0.0133 -0.9066 + -2.000 0.0822 0.0128 -0.9330 + -1.000 0.2048 0.0123 -1.0178 + 0.000 0.3272 0.0122 -1.1082 + 1.000 0.4470 0.0123 -1.1992 + 2.000 0.5647 0.0126 -1.2921 + 4.000 0.7865 0.0139 -1.4660 + 5.000 0.8848 0.0149 -1.5609 + 6.000 0.9805 0.0157 -1.6743 + 7.000 1.0743 0.0163 -1.8155 + 8.000 1.1181 0.0174 -1.9667 + 9.000 1.1587 0.0194 -2.1286 + 10.000 1.1764 0.0232 -2.2457 + 11.000 1.2237 0.0264 -2.4868 + 12.000 1.2590 0.0306 -2.7620 + 13.000 1.2973 0.0354 -3.0595 + 14.000 1.3355 0.0406 -3.3796 + 16.000 1.4002 0.0535 -4.0606 + 17.000 1.4249 0.0613 -4.4170 + 18.000 1.4496 0.0698 -4.7822 + 19.000 1.4688 0.0792 -5.1523 + 20.000 1.4833 0.0898 -5.5194 + 21.000 1.4908 0.1016 -5.8650 + 24.000 1.5293 0.1362 -7.1149 + 25.000 1.5329 0.1490 -7.5030 + 26.000 1.5333 0.1622 -7.8676 + 27.000 1.5294 0.1756 -8.1977 + 28.000 1.5197 0.1896 -8.4783 + 29.000 1.5057 0.2043 -8.7098 + 30.000 1.4898 0.2192 -8.9039 + 40.000 1.1901 0.4324 -1.0184 + 50.000 0.9526 0.6613 -1.0184 + 60.000 0.7198 0.8793 -1.0184 + 70.000 0.4772 1.0617 -1.0184 + 80.000 0.2313 1.1882 -1.0184 + 90.000 0.0000 1.2449 -1.0184 + 100.000 -0.1619 1.1882 -1.0184 + 110.000 -0.3340 1.0617 -1.0184 + 120.000 -0.5038 0.8793 -1.0184 + 130.000 -0.6669 0.6613 -1.0184 + 140.000 -0.8331 0.4324 -1.0184 + 150.000 -1.0428 0.2192 -1.0184 + 160.000 -0.6952 0.0461 -1.0184 + 170.000 -0.3476 0.0164 -1.0184 + 180.000 0.0000 0.0164 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0164 -1.0184 + -170.000 0.3661 0.0164 -1.0184 + -160.000 0.7321 0.0493 -1.0184 + -150.000 1.0351 0.2220 -1.0184 + -140.000 0.8284 0.4351 -1.0184 + -130.000 0.6641 0.6634 -1.0184 + -120.000 0.5023 0.8810 -1.0184 + -110.000 0.3334 1.0629 -1.0184 + -100.000 0.1617 1.1888 -1.0184 + -90.000 0.0000 1.2449 -1.0184 + -80.000 -0.1617 1.1888 -1.0184 + -70.000 -0.3334 1.0629 -1.0184 + -60.000 -0.5023 0.8810 -1.0184 + -50.000 -0.6641 0.6634 -1.0184 + -40.000 -0.8284 0.4351 -1.0184 + -30.000 -1.0351 0.2220 -1.0184 + -20.000 -0.7869 0.1157 -1.0184 + -10.000 -0.6880 0.0192 -2.5579 + -7.000 -0.5004 0.0152 -1.7011 + -6.000 -0.3895 0.0146 -1.4180 + -5.000 -0.2755 0.0139 -1.2216 + -4.000 -0.1580 0.0135 -1.0316 + -3.000 -0.0376 0.0132 -0.9093 + -2.000 0.0838 0.0128 -0.9338 + -1.000 0.2059 0.0122 -1.0186 + 0.000 0.3280 0.0122 -1.1084 + 1.000 0.4477 0.0122 -1.2000 + 2.000 0.5655 0.0125 -1.2881 + 3.000 0.6800 0.0130 -1.3744 + 4.000 0.7870 0.0138 -1.4696 + 5.000 0.8855 0.0148 -1.5637 + 6.000 0.9873 0.0154 -1.6808 + 7.000 1.0818 0.0161 -1.8229 + 8.000 1.1249 0.0172 -1.9747 + 10.000 1.1916 0.0224 -2.2714 + 11.000 1.2363 0.0257 -2.5121 + 12.000 1.2792 0.0294 -2.8052 + 13.000 1.3196 0.0338 -3.1125 + 14.000 1.3493 0.0396 -3.4141 + 15.000 1.3829 0.0456 -3.7489 + 17.000 1.4395 0.0600 -4.4651 + 18.000 1.4628 0.0684 -4.8274 + 19.000 1.4818 0.0778 -5.2016 + 20.000 1.4914 0.0888 -5.5513 + 22.000 1.5317 0.1092 -6.3912 + 23.000 1.5395 0.1215 -6.7971 + 24.000 1.5451 0.1340 -7.2017 + 25.000 1.5478 0.1469 -7.5887 + 26.000 1.5468 0.1602 -7.9504 + 27.000 1.5396 0.1740 -8.2650 + 28.000 1.5291 0.1883 -8.5435 + 29.000 1.5165 0.2026 -8.7905 + 30.000 1.4788 0.2220 -8.3144 + 40.000 1.1834 0.4351 -1.0184 + 50.000 0.9487 0.6634 -1.0184 + 60.000 0.7176 0.8810 -1.0184 + 70.000 0.4763 1.0629 -1.0184 + 80.000 0.2311 1.1888 -1.0184 + 90.000 0.0000 1.2449 -1.0184 + 100.000 -0.1617 1.1888 -1.0184 + 110.000 -0.3334 1.0629 -1.0184 + 120.000 -0.5023 0.8810 -1.0184 + 130.000 -0.6641 0.6634 -1.0184 + 140.000 -0.8284 0.4351 -1.0184 + 150.000 -1.0351 0.2220 -1.0184 + 160.000 -0.7321 0.0493 -1.0184 + 170.000 -0.3661 0.0164 -1.0184 + 180.000 0.0000 0.0164 -1.0184 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0164 -1.0184 + -170.000 0.3528 0.0164 -1.0184 + -160.000 0.7055 0.0422 -1.0184 + -150.000 1.0584 0.2155 -1.0184 + -140.000 0.8426 0.4292 -1.0184 + -130.000 0.6724 0.6586 -1.0184 + -120.000 0.5068 0.8773 -1.0184 + -110.000 0.3353 1.0604 -1.0184 + -100.000 0.1622 1.1874 -1.0184 + -90.000 0.0000 1.2449 -1.0184 + -80.000 -0.1622 1.1874 -1.0184 + -70.000 -0.3353 1.0604 -1.0184 + -60.000 -0.5068 0.8773 -1.0184 + -50.000 -0.6724 0.6586 -1.0184 + -40.000 -0.8426 0.4292 -1.0184 + -30.000 -1.0584 0.2155 -1.0184 + -20.000 -0.7746 0.1171 -1.0184 + -10.000 -0.7013 0.0187 -2.5858 + -9.000 -0.6819 0.0169 -2.3531 + -8.000 -0.6071 0.0156 -2.0491 + -6.000 -0.3894 0.0145 -1.4156 + -5.000 -0.2754 0.0138 -1.2213 + -4.000 -0.1579 0.0133 -1.0315 + -3.000 -0.0368 0.0131 -0.9097 + -2.000 0.0849 0.0127 -0.9345 + -1.000 0.2064 0.0122 -1.0188 + 0.000 0.3289 0.0121 -1.1092 + 1.000 0.4466 0.0123 -1.2002 + 2.000 0.5654 0.0125 -1.2873 + 3.000 0.6786 0.0131 -1.3726 + 5.000 0.8885 0.0147 -1.5669 + 6.000 0.9903 0.0154 -1.6841 + 7.000 1.0893 0.0159 -1.8303 + 8.000 1.1291 0.0170 -1.9792 + 11.000 1.2479 0.0251 -2.5351 + 12.000 1.2907 0.0287 -2.8294 + 14.000 1.3702 0.0380 -3.4665 + 16.000 1.4249 0.0513 -4.1369 + 18.000 1.4739 0.0672 -4.8655 + 21.000 1.5334 0.0960 -6.0511 + 22.000 1.5435 0.1076 -6.4448 + 23.000 1.5524 0.1197 -6.8631 + 24.000 1.5570 0.1323 -7.2661 + 25.000 1.5586 0.1454 -7.6507 + 26.000 1.5542 0.1590 -7.9959 + 27.000 1.5471 0.1729 -8.3137 + 28.000 1.5378 0.1869 -8.6045 + 29.000 1.5264 0.2010 -8.8638 + 30.000 1.5119 0.2155 -9.0822 + 40.000 1.2036 0.4292 -1.0184 + 50.000 0.9607 0.6586 -1.0184 + 60.000 0.7241 0.8773 -1.0184 + 70.000 0.4790 1.0604 -1.0184 + 80.000 0.2317 1.1874 -1.0184 + 90.000 0.0000 1.2449 -1.0184 + 100.000 -0.1622 1.1874 -1.0184 + 110.000 -0.3353 1.0604 -1.0184 + 120.000 -0.5068 0.8773 -1.0184 + 130.000 -0.6724 0.6586 -1.0184 + 140.000 -0.8426 0.4292 -1.0184 + 150.000 -1.0584 0.2155 -1.0184 + 160.000 -0.7055 0.0422 -1.0184 + 170.000 -0.3528 0.0164 -1.0184 + 180.000 0.0000 0.0164 -1.0184 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247_coords.txt new file mode 100644 index 000000000..76becdc13 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0247_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0247 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.021573 + 0.027091 0.045571 + 0.060263 0.070519 + 0.105430 0.093593 + 0.161359 0.113695 + 0.226526 0.129332 + 0.299152 0.139084 + 0.377257 0.140958 + 0.458710 0.134375 + 0.541290 0.120926 + 0.622743 0.102688 + 0.700848 0.081979 + 0.773474 0.061268 + 0.838641 0.042604 + 0.894570 0.027238 + 0.939737 0.015604 + 0.972909 0.007552 + 0.993181 0.002511 + 1.000000 0.000000 + 0.993181 -0.000009 + 0.972909 0.000021 + 0.939737 -0.001786 + 0.894570 -0.006851 + 0.838641 -0.015998 + 0.773474 -0.029320 + 0.700848 -0.046093 + 0.622743 -0.064559 + 0.541290 -0.082348 + 0.458710 -0.096978 + 0.377257 -0.106009 + 0.299152 -0.107583 + 0.226526 -0.102345 + 0.161359 -0.091881 + 0.105430 -0.077436 + 0.060263 -0.059906 + 0.027091 -0.040088 + 0.006819 -0.019333 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259.dat new file mode 100644 index 000000000..2122f1397 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0259 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0259_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0173 -1.0500 + -170.000 0.3042 0.0173 -1.0500 + -160.000 0.6084 0.0763 -1.0500 + -150.000 0.9126 0.2442 -1.0500 + -140.000 0.7507 0.4510 -1.0500 + -130.000 0.6154 0.6723 -1.0500 + -120.000 0.4739 0.8824 -1.0500 + -110.000 0.3193 1.0569 -1.0500 + -100.000 0.1570 1.1758 -1.0500 + -90.000 0.0000 1.2263 -1.0500 + -80.000 -0.1570 1.1758 -1.0500 + -70.000 -0.3193 1.0569 -1.0500 + -60.000 -0.4739 0.8824 -1.0500 + -50.000 -0.6154 0.6723 -1.0500 + -40.000 -0.7507 0.4510 -1.0500 + -30.000 -0.9126 0.2442 -1.0500 + -20.000 -0.6612 0.1348 -1.0500 + -10.000 -0.5856 0.0253 -2.3817 + -9.000 -0.5560 0.0221 -2.1570 + -8.000 -0.5266 0.0196 -1.9400 + -7.000 -0.4741 0.0183 -1.6900 + -6.000 -0.3813 0.0173 -1.4473 + -5.000 -0.2758 0.0166 -1.2546 + -4.000 -0.1657 0.0159 -1.0740 + -3.000 -0.0533 0.0149 -0.9579 + -2.000 0.0654 0.0147 -0.9571 + -1.000 0.1837 0.0146 -1.0391 + 0.000 0.3015 0.0147 -1.1258 + 1.000 0.4199 0.0147 -1.2152 + 2.000 0.5365 0.0149 -1.3043 + 3.000 0.6510 0.0151 -1.3968 + 4.000 0.7612 0.0155 -1.4899 + 5.000 0.8698 0.0159 -1.5922 + 6.000 0.9584 0.0170 -1.6982 + 7.000 0.9941 0.0186 -1.7781 + 8.000 1.0264 0.0209 -1.9015 + 9.000 1.0659 0.0237 -2.0501 + 10.000 1.1068 0.0270 -2.2053 + 11.000 1.1146 0.0328 -2.3201 + 12.000 1.1437 0.0380 -2.5649 + 13.000 1.1530 0.0457 -2.7732 + 14.000 1.1785 0.0527 -3.0315 + 15.000 1.1965 0.0611 -3.2835 + 16.000 1.2243 0.0693 -3.5810 + 17.000 1.2486 0.0782 -3.8873 + 18.000 1.2711 0.0879 -4.2109 + 19.000 1.2921 0.0982 -4.5349 + 20.000 1.3103 0.1092 -4.8694 + 21.000 1.3273 0.1205 -5.2118 + 22.000 1.3464 0.1314 -5.5795 + 23.000 1.3547 0.1441 -5.9129 + 24.000 1.3612 0.1569 -6.2417 + 25.000 1.3621 0.1702 -6.5518 + 26.000 1.3574 0.1844 -6.8172 + 28.000 1.3331 0.2134 -7.2030 + 29.000 1.3151 0.2296 -7.3009 + 30.000 1.3037 0.2442 -7.4214 + 40.000 1.0724 0.4510 -1.0500 + 50.000 0.8792 0.6723 -1.0500 + 60.000 0.6770 0.8824 -1.0500 + 70.000 0.4561 1.0569 -1.0500 + 80.000 0.2243 1.1758 -1.0500 + 90.000 0.0000 1.2263 -1.0500 + 100.000 -0.1570 1.1758 -1.0500 + 110.000 -0.3193 1.0569 -1.0500 + 120.000 -0.4739 0.8824 -1.0500 + 130.000 -0.6154 0.6723 -1.0500 + 140.000 -0.7507 0.4510 -1.0500 + 150.000 -0.9126 0.2442 -1.0500 + 160.000 -0.6084 0.0763 -1.0500 + 170.000 -0.3042 0.0173 -1.0500 + 180.000 0.0000 0.0173 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0265 -1.0500 + -170.000 0.3188 0.0265 -1.0500 + -160.000 0.6377 0.0740 -1.0500 + -150.000 0.9566 0.2428 -1.0500 + -140.000 0.7775 0.4507 -1.0500 + -130.000 0.6312 0.6735 -1.0500 + -120.000 0.4824 0.8854 -1.0500 + -110.000 0.3229 1.0619 -1.0500 + -100.000 0.1579 1.1829 -1.0500 + -90.000 0.0000 1.2355 -1.0500 + -80.000 -0.1579 1.1829 -1.0500 + -70.000 -0.3229 1.0619 -1.0500 + -60.000 -0.4824 0.8854 -1.0500 + -50.000 -0.6312 0.6735 -1.0500 + -40.000 -0.7775 0.4507 -1.0500 + -30.000 -0.9566 0.2428 -1.0500 + -20.000 -0.6990 0.1374 -1.0500 + -10.000 -0.6305 0.0320 -2.4763 + -9.000 -0.5846 0.0296 -2.2114 + -8.000 -0.5656 0.0276 -2.0071 + -7.000 -0.4826 0.0265 -1.7052 + -6.000 -0.3812 0.0257 -1.4476 + -5.000 -0.2740 0.0250 -1.2528 + -4.000 -0.1598 0.0246 -1.0679 + -3.000 -0.0443 0.0239 -0.9516 + -1.000 0.1932 0.0231 -1.0446 + 0.000 0.3133 0.0230 -1.1332 + 1.000 0.4312 0.0232 -1.2224 + 2.000 0.5504 0.0232 -1.3133 + 5.000 0.8753 0.0248 -1.5949 + 6.000 0.9525 0.0262 -1.6950 + 7.000 1.0053 0.0274 -1.7871 + 9.000 1.1044 0.0311 -2.0976 + 10.000 1.1238 0.0351 -2.2272 + 11.000 1.1576 0.0392 -2.4030 + 12.000 1.1727 0.0451 -2.6260 + 13.000 1.2005 0.0510 -2.8799 + 14.000 1.2286 0.0574 -3.1581 + 15.000 1.2617 0.0640 -3.4600 + 16.000 1.2886 0.0717 -3.7722 + 17.000 1.3161 0.0798 -4.1116 + 18.000 1.3390 0.0889 -4.4496 + 19.000 1.3617 0.0985 -4.7969 + 20.000 1.3762 0.1095 -5.1410 + 21.000 1.3913 0.1207 -5.4920 + 22.000 1.4044 0.1325 -5.8568 + 23.000 1.4088 0.1454 -6.1886 + 24.000 1.4222 0.1569 -6.5916 + 25.000 1.4229 0.1701 -6.9257 + 26.000 1.4236 0.1831 -7.2526 + 27.000 1.4198 0.1966 -7.5420 + 28.000 1.4120 0.2107 -7.7888 + 29.000 1.3826 0.2276 -7.8548 + 30.000 1.3666 0.2428 -7.9826 + 40.000 1.1106 0.4507 -1.0500 + 50.000 0.9018 0.6735 -1.0500 + 60.000 0.6891 0.8854 -1.0500 + 70.000 0.4613 1.0619 -1.0500 + 80.000 0.2255 1.1829 -1.0500 + 90.000 0.0000 1.2355 -1.0500 + 100.000 -0.1579 1.1829 -1.0500 + 110.000 -0.3229 1.0619 -1.0500 + 120.000 -0.4824 0.8854 -1.0500 + 130.000 -0.6312 0.6735 -1.0500 + 140.000 -0.7775 0.4507 -1.0500 + 150.000 -0.9566 0.2428 -1.0500 + 160.000 -0.6377 0.0740 -1.0500 + 170.000 -0.3188 0.0265 -1.0500 + 180.000 0.0000 0.0265 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0273 -1.0500 + -170.000 0.3307 0.0273 -1.0500 + -160.000 0.6614 0.0659 -1.0500 + -150.000 0.9922 0.2354 -1.0500 + -140.000 0.7991 0.4443 -1.0500 + -130.000 0.6440 0.6682 -1.0500 + -120.000 0.4893 0.8814 -1.0500 + -110.000 0.3258 1.0594 -1.0500 + -100.000 0.1586 1.1821 -1.0500 + -90.000 0.0000 1.2363 -1.0500 + -80.000 -0.1586 1.1821 -1.0500 + -70.000 -0.3258 1.0594 -1.0500 + -60.000 -0.4893 0.8814 -1.0500 + -50.000 -0.6440 0.6682 -1.0500 + -40.000 -0.7991 0.4443 -1.0500 + -30.000 -0.9922 0.2354 -1.0500 + -20.000 -0.7259 0.1333 -1.0500 + -10.000 -0.6565 0.0312 -2.5308 + -9.000 -0.6153 0.0291 -2.2680 + -8.000 -0.5760 0.0277 -2.0253 + -7.000 -0.4843 0.0269 -1.7079 + -6.000 -0.3829 0.0261 -1.4501 + -5.000 -0.2719 0.0254 -1.2506 + -4.000 -0.1571 0.0250 -1.0648 + -3.000 -0.0409 0.0245 -0.9496 + -2.000 0.0773 0.0237 -0.9639 + -1.000 0.1974 0.0234 -1.0472 + 0.000 0.3173 0.0234 -1.1354 + 1.000 0.4378 0.0234 -1.2266 + 2.000 0.5540 0.0237 -1.3159 + 4.000 0.7767 0.0246 -1.5044 + 5.000 0.8722 0.0257 -1.5946 + 6.000 0.9556 0.0269 -1.6904 + 7.000 1.0185 0.0277 -1.8002 + 8.000 1.0788 0.0289 -1.9584 + 9.000 1.1126 0.0313 -2.1073 + 10.000 1.1482 0.0346 -2.2605 + 11.000 1.1714 0.0390 -2.4300 + 12.000 1.2007 0.0438 -2.6851 + 13.000 1.2270 0.0496 -2.9404 + 14.000 1.2643 0.0552 -3.2479 + 15.000 1.2937 0.0619 -3.5495 + 16.000 1.3227 0.0691 -3.8735 + 17.000 1.3492 0.0771 -4.2206 + 19.000 1.3921 0.0955 -4.9136 + 20.000 1.4242 0.1039 -5.3363 + 21.000 1.4362 0.1152 -5.6847 + 22.000 1.4437 0.1275 -6.0421 + 23.000 1.4482 0.1403 -6.3916 + 24.000 1.4544 0.1528 -6.7718 + 25.000 1.4570 0.1657 -7.1295 + 26.000 1.4545 0.1791 -7.4507 + 27.000 1.4477 0.1930 -7.7327 + 28.000 1.4396 0.2069 -7.9910 + 29.000 1.4298 0.2210 -8.2200 + 30.000 1.4174 0.2354 -8.4060 + 40.000 1.1415 0.4443 -1.0500 + 50.000 0.9200 0.6682 -1.0500 + 60.000 0.6990 0.8814 -1.0500 + 70.000 0.4656 1.0594 -1.0500 + 80.000 0.2266 1.1821 -1.0500 + 90.000 0.0000 1.2363 -1.0500 + 100.000 -0.1586 1.1821 -1.0500 + 110.000 -0.3258 1.0594 -1.0500 + 120.000 -0.4893 0.8814 -1.0500 + 130.000 -0.6440 0.6682 -1.0500 + 140.000 -0.7991 0.4443 -1.0500 + 150.000 -0.9922 0.2354 -1.0500 + 160.000 -0.6614 0.0659 -1.0500 + 170.000 -0.3307 0.0273 -1.0500 + 180.000 0.0000 0.0273 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0273 -1.0500 + -170.000 0.3963 0.0273 -1.0500 + -160.000 0.7927 0.0815 -1.0500 + -150.000 0.9345 0.2497 -1.0500 + -140.000 0.7640 0.4570 -1.0500 + -130.000 0.6233 0.6789 -1.0500 + -120.000 0.4781 0.8897 -1.0500 + -110.000 0.3211 1.0650 -1.0500 + -100.000 0.1575 1.1850 -1.0500 + -90.000 0.0000 1.2363 -1.0500 + -80.000 -0.1575 1.1850 -1.0500 + -70.000 -0.3211 1.0650 -1.0500 + -60.000 -0.4781 0.8897 -1.0500 + -50.000 -0.6233 0.6789 -1.0500 + -40.000 -0.7640 0.4570 -1.0500 + -30.000 -0.9345 0.2497 -1.0500 + -20.000 -0.8190 0.1218 -1.0500 + -10.000 -0.6664 0.0308 -2.5594 + -9.000 -0.6387 0.0282 -2.3121 + -8.000 -0.5840 0.0273 -2.0395 + -5.000 -0.2715 0.0252 -1.2500 + -4.000 -0.1565 0.0247 -1.0642 + -3.000 -0.0386 0.0243 -0.9387 + -2.000 0.0795 0.0237 -0.9652 + -1.000 0.1995 0.0234 -1.0481 + 0.000 0.3206 0.0233 -1.1376 + 1.000 0.4392 0.0233 -1.2274 + 2.000 0.5554 0.0235 -1.3173 + 3.000 0.6697 0.0239 -1.4100 + 4.000 0.7759 0.0247 -1.4949 + 6.000 0.9617 0.0267 -1.6893 + 7.000 1.0370 0.0273 -1.8165 + 8.000 1.0937 0.0284 -1.9758 + 9.000 1.1244 0.0309 -2.1223 + 11.000 1.1836 0.0383 -2.4539 + 12.000 1.2209 0.0425 -2.7277 + 13.000 1.2599 0.0471 -3.0189 + 15.000 1.3283 0.0587 -3.6464 + 16.000 1.3574 0.0657 -3.9802 + 17.000 1.3869 0.0731 -4.3450 + 18.000 1.4070 0.0819 -4.6864 + 19.000 1.4296 0.0909 -5.0580 + 20.000 1.4449 0.1014 -5.4198 + 22.000 1.4619 0.1250 -6.1264 + 25.000 1.4735 0.1633 -7.2267 + 26.000 1.4721 0.1765 -7.5605 + 30.000 1.3351 0.2497 -6.1002 + 40.000 1.0915 0.4570 -1.0500 + 50.000 0.8905 0.6789 -1.0500 + 60.000 0.6831 0.8897 -1.0500 + 70.000 0.4587 1.0650 -1.0500 + 80.000 0.2249 1.1850 -1.0500 + 90.000 0.0000 1.2363 -1.0500 + 100.000 -0.1575 1.1850 -1.0500 + 110.000 -0.3211 1.0650 -1.0500 + 120.000 -0.4781 0.8897 -1.0500 + 130.000 -0.6233 0.6789 -1.0500 + 140.000 -0.7640 0.4570 -1.0500 + 150.000 -0.9345 0.2497 -1.0500 + 160.000 -0.7927 0.0815 -1.0500 + 170.000 -0.3963 0.0273 -1.0500 + 180.000 0.0000 0.0273 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0273 -1.0500 + -170.000 0.3420 0.0273 -1.0500 + -160.000 0.6842 0.0565 -1.0500 + -150.000 1.0262 0.2268 -1.0500 + -140.000 0.8198 0.4367 -1.0500 + -130.000 0.6563 0.6619 -1.0500 + -120.000 0.4958 0.8765 -1.0500 + -110.000 0.3287 1.0560 -1.0500 + -100.000 0.1593 1.1804 -1.0500 + -90.000 0.0000 1.2363 -1.0500 + -80.000 -0.1593 1.1804 -1.0500 + -70.000 -0.3287 1.0560 -1.0500 + -60.000 -0.4958 0.8765 -1.0500 + -50.000 -0.6563 0.6619 -1.0500 + -40.000 -0.8198 0.4367 -1.0500 + -30.000 -1.0262 0.2268 -1.0500 + -20.000 -0.7455 0.1287 -1.0500 + -10.000 -0.6641 0.0307 -2.5362 + -9.000 -0.6680 0.0278 -2.3705 + -8.000 -0.5909 0.0270 -2.0519 + -6.000 -0.3842 0.0255 -1.4542 + -4.000 -0.1556 0.0245 -1.0634 + -3.000 -0.0378 0.0242 -0.9399 + -2.000 0.0809 0.0237 -0.9660 + -1.000 0.2015 0.0233 -1.0494 + 0.000 0.3219 0.0232 -1.1383 + 1.000 0.4399 0.0233 -1.2279 + 2.000 0.5557 0.0235 -1.3193 + 4.000 0.7740 0.0248 -1.4904 + 5.000 0.8707 0.0258 -1.5838 + 6.000 0.9649 0.0266 -1.6955 + 7.000 1.0572 0.0272 -1.8344 + 8.000 1.1003 0.0283 -1.9832 + 9.000 1.1403 0.0303 -2.1425 + 10.000 1.1576 0.0340 -2.2577 + 11.000 1.2042 0.0371 -2.4950 + 12.000 1.2389 0.0413 -2.7658 + 13.000 1.2767 0.0460 -3.0586 + 14.000 1.3142 0.0511 -3.3735 + 16.000 1.3779 0.0638 -4.0437 + 17.000 1.4021 0.0715 -4.3944 + 18.000 1.4265 0.0798 -4.7538 + 19.000 1.4454 0.0891 -5.1180 + 20.000 1.4597 0.0995 -5.4792 + 21.000 1.4671 0.1111 -5.8193 + 24.000 1.5049 0.1452 -7.0494 + 25.000 1.5085 0.1578 -7.4313 + 26.000 1.5089 0.1708 -7.7900 + 27.000 1.5050 0.1839 -8.1148 + 28.000 1.4955 0.1978 -8.3910 + 29.000 1.4817 0.2122 -8.6188 + 30.000 1.4660 0.2268 -8.8098 + 40.000 1.1712 0.4367 -1.0500 + 50.000 0.9375 0.6619 -1.0500 + 60.000 0.7083 0.8765 -1.0500 + 70.000 0.4696 1.0560 -1.0500 + 80.000 0.2276 1.1804 -1.0500 + 90.000 0.0000 1.2363 -1.0500 + 100.000 -0.1593 1.1804 -1.0500 + 110.000 -0.3287 1.0560 -1.0500 + 120.000 -0.4958 0.8765 -1.0500 + 130.000 -0.6563 0.6619 -1.0500 + 140.000 -0.8198 0.4367 -1.0500 + 150.000 -1.0262 0.2268 -1.0500 + 160.000 -0.6842 0.0565 -1.0500 + 170.000 -0.3420 0.0273 -1.0500 + 180.000 0.0000 0.0273 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0273 -1.0500 + -170.000 0.3603 0.0273 -1.0500 + -160.000 0.7204 0.0597 -1.0500 + -150.000 1.0186 0.2297 -1.0500 + -140.000 0.8152 0.4393 -1.0500 + -130.000 0.6535 0.6640 -1.0500 + -120.000 0.4943 0.8781 -1.0500 + -110.000 0.3281 1.0571 -1.0500 + -100.000 0.1591 1.1810 -1.0500 + -90.000 0.0000 1.2363 -1.0500 + -80.000 -0.1591 1.1810 -1.0500 + -70.000 -0.3281 1.0571 -1.0500 + -60.000 -0.4943 0.8781 -1.0500 + -50.000 -0.6535 0.6640 -1.0500 + -40.000 -0.8152 0.4393 -1.0500 + -30.000 -1.0186 0.2297 -1.0500 + -20.000 -0.7743 0.1250 -1.0500 + -10.000 -0.6770 0.0301 -2.5650 + -7.000 -0.4925 0.0261 -1.7218 + -6.000 -0.3833 0.0255 -1.4432 + -5.000 -0.2711 0.0248 -1.2500 + -4.000 -0.1555 0.0244 -1.0630 + -3.000 -0.0370 0.0241 -0.9427 + -2.000 0.0825 0.0237 -0.9667 + -1.000 0.2026 0.0232 -1.0502 + 0.000 0.3227 0.0232 -1.1385 + 1.000 0.4406 0.0232 -1.2287 + 2.000 0.5565 0.0234 -1.3154 + 3.000 0.6691 0.0239 -1.4003 + 4.000 0.7744 0.0247 -1.4940 + 5.000 0.8714 0.0257 -1.5865 + 6.000 0.9716 0.0263 -1.7018 + 7.000 1.0646 0.0270 -1.8417 + 8.000 1.1070 0.0281 -1.9911 + 10.000 1.1726 0.0332 -2.2830 + 11.000 1.2166 0.0364 -2.5199 + 12.000 1.2588 0.0401 -2.8083 + 13.000 1.2986 0.0444 -3.1107 + 14.000 1.3278 0.0502 -3.4075 + 15.000 1.3609 0.0560 -3.7370 + 17.000 1.4166 0.0702 -4.4417 + 18.000 1.4395 0.0784 -4.7983 + 19.000 1.4582 0.0877 -5.1665 + 20.000 1.4677 0.0985 -5.5106 + 22.000 1.5073 0.1186 -6.3371 + 23.000 1.5150 0.1307 -6.7366 + 24.000 1.5205 0.1430 -7.1348 + 25.000 1.5231 0.1558 -7.5156 + 26.000 1.5222 0.1688 -7.8715 + 27.000 1.5151 0.1824 -8.1811 + 28.000 1.5047 0.1964 -8.4551 + 29.000 1.4923 0.2106 -8.6982 + 30.000 1.4552 0.2297 -8.2297 + 40.000 1.1645 0.4393 -1.0500 + 50.000 0.9336 0.6640 -1.0500 + 60.000 0.7062 0.8781 -1.0500 + 70.000 0.4687 1.0571 -1.0500 + 80.000 0.2274 1.1810 -1.0500 + 90.000 0.0000 1.2363 -1.0500 + 100.000 -0.1591 1.1810 -1.0500 + 110.000 -0.3281 1.0571 -1.0500 + 120.000 -0.4943 0.8781 -1.0500 + 130.000 -0.6535 0.6640 -1.0500 + 140.000 -0.8152 0.4393 -1.0500 + 150.000 -1.0186 0.2297 -1.0500 + 160.000 -0.7204 0.0597 -1.0500 + 170.000 -0.3603 0.0273 -1.0500 + 180.000 0.0000 0.0273 -1.0500 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0273 -1.0500 + -170.000 0.3472 0.0273 -1.0500 + -160.000 0.6943 0.0527 -1.0500 + -150.000 1.0415 0.2232 -1.0500 + -140.000 0.8291 0.4335 -1.0500 + -130.000 0.6617 0.6592 -1.0500 + -120.000 0.4987 0.8744 -1.0500 + -110.000 0.3299 1.0546 -1.0500 + -100.000 0.1596 1.1796 -1.0500 + -90.000 0.0000 1.2363 -1.0500 + -80.000 -0.1596 1.1796 -1.0500 + -70.000 -0.3299 1.0546 -1.0500 + -60.000 -0.4987 0.8744 -1.0500 + -50.000 -0.6617 0.6592 -1.0500 + -40.000 -0.8291 0.4335 -1.0500 + -30.000 -1.0415 0.2232 -1.0500 + -20.000 -0.7623 0.1264 -1.0500 + -10.000 -0.6901 0.0296 -2.5924 + -9.000 -0.6710 0.0277 -2.3634 + -8.000 -0.5974 0.0265 -2.0643 + -6.000 -0.3832 0.0254 -1.4409 + -5.000 -0.2711 0.0247 -1.2497 + -4.000 -0.1554 0.0242 -1.0629 + -3.000 -0.0362 0.0240 -0.9430 + -2.000 0.0836 0.0236 -0.9674 + -1.000 0.2031 0.0232 -1.0504 + 0.000 0.3237 0.0231 -1.1393 + 1.000 0.4395 0.0233 -1.2289 + 2.000 0.5564 0.0234 -1.3146 + 3.000 0.6678 0.0240 -1.3986 + 5.000 0.8744 0.0256 -1.5898 + 6.000 0.9745 0.0263 -1.7051 + 7.000 1.0719 0.0268 -1.8489 + 8.000 1.1111 0.0279 -1.9955 + 11.000 1.2280 0.0358 -2.5425 + 12.000 1.2701 0.0394 -2.8321 + 14.000 1.3483 0.0485 -3.4590 + 16.000 1.4021 0.0617 -4.1188 + 18.000 1.4504 0.0773 -4.8357 + 21.000 1.5090 0.1056 -6.0025 + 22.000 1.5189 0.1170 -6.3899 + 23.000 1.5276 0.1289 -6.8015 + 24.000 1.5322 0.1413 -7.1982 + 25.000 1.5338 0.1542 -7.5766 + 26.000 1.5294 0.1677 -7.9162 + 27.000 1.5225 0.1813 -8.2290 + 28.000 1.5133 0.1950 -8.5152 + 29.000 1.5021 0.2090 -8.7703 + 30.000 1.4878 0.2232 -8.9852 + 40.000 1.1844 0.4335 -1.0500 + 50.000 0.9454 0.6592 -1.0500 + 60.000 0.7125 0.8744 -1.0500 + 70.000 0.4714 1.0546 -1.0500 + 80.000 0.2281 1.1796 -1.0500 + 90.000 0.0000 1.2363 -1.0500 + 100.000 -0.1596 1.1796 -1.0500 + 110.000 -0.3299 1.0546 -1.0500 + 120.000 -0.4987 0.8744 -1.0500 + 130.000 -0.6617 0.6592 -1.0500 + 140.000 -0.8291 0.4335 -1.0500 + 150.000 -1.0415 0.2232 -1.0500 + 160.000 -0.6943 0.0527 -1.0500 + 170.000 -0.3472 0.0273 -1.0500 + 180.000 0.0000 0.0273 -1.0500 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259_coords.txt new file mode 100644 index 000000000..94bdcf9ee --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0259_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0259 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.022584 + 0.027091 0.047510 + 0.060263 0.073301 + 0.105430 0.097140 + 0.161359 0.117917 + 0.226526 0.134137 + 0.299152 0.144379 + 0.377257 0.146668 + 0.458710 0.140419 + 0.541290 0.127194 + 0.622743 0.109034 + 0.700848 0.088223 + 0.773474 0.067204 + 0.838641 0.048007 + 0.894570 0.031887 + 0.939737 0.019298 + 0.972909 0.010124 + 0.993181 0.003839 + 1.000000 0.000000 + 0.993181 -0.001378 + 0.972909 -0.002676 + 0.939737 -0.005710 + 0.894570 -0.011838 + 0.838641 -0.021843 + 0.773474 -0.035786 + 0.700848 -0.052933 + 0.622743 -0.071538 + 0.541290 -0.089257 + 0.458710 -0.103644 + 0.377257 -0.112299 + 0.299152 -0.113402 + 0.226526 -0.107598 + 0.161359 -0.096465 + 0.105430 -0.081252 + 0.060263 -0.062864 + 0.027091 -0.042119 + 0.006819 -0.020381 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276.dat new file mode 100644 index 000000000..056449260 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0276 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0276_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0237 -1.0947 + -170.000 0.2972 0.0237 -1.0947 + -160.000 0.5944 0.0815 -1.0947 + -150.000 0.8917 0.2455 -1.0947 + -140.000 0.7334 0.4476 -1.0947 + -130.000 0.6013 0.6637 -1.0947 + -120.000 0.4631 0.8690 -1.0947 + -110.000 0.3120 1.0395 -1.0947 + -100.000 0.1534 1.1557 -1.0947 + -90.000 0.0000 1.2050 -1.0947 + -80.000 -0.1534 1.1557 -1.0947 + -70.000 -0.3120 1.0395 -1.0947 + -60.000 -0.4631 0.8690 -1.0947 + -50.000 -0.6013 0.6637 -1.0947 + -40.000 -0.7334 0.4476 -1.0947 + -30.000 -0.8917 0.2455 -1.0947 + -20.000 -0.6461 0.1386 -1.0947 + -10.000 -0.5722 0.0316 -2.3958 + -9.000 -0.5433 0.0285 -2.1764 + -8.000 -0.5145 0.0260 -1.9643 + -7.000 -0.4633 0.0248 -1.7200 + -6.000 -0.3726 0.0238 -1.4829 + -5.000 -0.2695 0.0231 -1.2946 + -4.000 -0.1619 0.0224 -1.1182 + -3.000 -0.0521 0.0215 -1.0047 + -2.000 0.0639 0.0213 -1.0040 + -1.000 0.1795 0.0212 -1.0841 + 0.000 0.2946 0.0213 -1.1688 + 1.000 0.4103 0.0213 -1.2561 + 2.000 0.5242 0.0215 -1.3432 + 3.000 0.6361 0.0216 -1.4336 + 4.000 0.7437 0.0220 -1.5246 + 5.000 0.8498 0.0224 -1.6245 + 6.000 0.9364 0.0235 -1.7280 + 7.000 0.9713 0.0251 -1.8062 + 8.000 1.0028 0.0273 -1.9267 + 9.000 1.0414 0.0300 -2.0719 + 10.000 1.0814 0.0333 -2.2235 + 11.000 1.0890 0.0389 -2.3357 + 12.000 1.1174 0.0440 -2.5748 + 13.000 1.1266 0.0516 -2.7784 + 14.000 1.1514 0.0584 -3.0308 + 15.000 1.1691 0.0666 -3.2770 + 16.000 1.1962 0.0746 -3.5677 + 17.000 1.2199 0.0833 -3.8669 + 18.000 1.2419 0.0928 -4.1831 + 19.000 1.2624 0.1028 -4.4997 + 20.000 1.2802 0.1136 -4.8265 + 21.000 1.2968 0.1246 -5.1610 + 22.000 1.3155 0.1353 -5.5203 + 23.000 1.3236 0.1477 -5.8461 + 24.000 1.3300 0.1602 -6.1673 + 25.000 1.3308 0.1732 -6.4703 + 26.000 1.3263 0.1870 -6.7296 + 28.000 1.3025 0.2154 -7.1066 + 29.000 1.2849 0.2312 -7.2022 + 30.000 1.2738 0.2455 -7.3200 + 40.000 1.0478 0.4476 -1.0947 + 50.000 0.8590 0.6637 -1.0947 + 60.000 0.6615 0.8690 -1.0947 + 70.000 0.4456 1.0395 -1.0947 + 80.000 0.2191 1.1557 -1.0947 + 90.000 0.0000 1.2050 -1.0947 + 100.000 -0.1534 1.1557 -1.0947 + 110.000 -0.3120 1.0395 -1.0947 + 120.000 -0.4631 0.8690 -1.0947 + 130.000 -0.6013 0.6637 -1.0947 + 140.000 -0.7334 0.4476 -1.0947 + 150.000 -0.8917 0.2455 -1.0947 + 160.000 -0.5944 0.0815 -1.0947 + 170.000 -0.2972 0.0237 -1.0947 + 180.000 0.0000 0.0237 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0413 -1.0947 + -170.000 0.3115 0.0413 -1.0947 + -160.000 0.6231 0.0877 -1.0947 + -150.000 0.9346 0.2526 -1.0947 + -140.000 0.7596 0.4558 -1.0947 + -130.000 0.6167 0.6734 -1.0947 + -120.000 0.4714 0.8804 -1.0947 + -110.000 0.3155 1.0529 -1.0947 + -100.000 0.1542 1.1712 -1.0947 + -90.000 0.0000 1.2225 -1.0947 + -80.000 -0.1542 1.1712 -1.0947 + -70.000 -0.3155 1.0529 -1.0947 + -60.000 -0.4714 0.8804 -1.0947 + -50.000 -0.6167 0.6734 -1.0947 + -40.000 -0.7596 0.4558 -1.0947 + -30.000 -0.9346 0.2526 -1.0947 + -20.000 -0.6829 0.1496 -1.0947 + -10.000 -0.6161 0.0466 -2.4883 + -9.000 -0.5712 0.0443 -2.2295 + -8.000 -0.5526 0.0423 -2.0298 + -7.000 -0.4716 0.0413 -1.7349 + -6.000 -0.3725 0.0405 -1.4832 + -5.000 -0.2677 0.0398 -1.2929 + -4.000 -0.1561 0.0394 -1.1123 + -3.000 -0.0432 0.0387 -0.9986 + -1.000 0.1888 0.0379 -1.0895 + 0.000 0.3061 0.0378 -1.1760 + 1.000 0.4213 0.0380 -1.2632 + 2.000 0.5378 0.0380 -1.3520 + 5.000 0.8552 0.0396 -1.6272 + 6.000 0.9306 0.0410 -1.7249 + 7.000 0.9823 0.0421 -1.8149 + 9.000 1.0790 0.0457 -2.1183 + 10.000 1.0980 0.0496 -2.2449 + 11.000 1.1311 0.0536 -2.4167 + 12.000 1.1458 0.0595 -2.6346 + 13.000 1.1730 0.0652 -2.8826 + 14.000 1.2004 0.0715 -3.1545 + 15.000 1.2328 0.0779 -3.4495 + 16.000 1.2590 0.0855 -3.7545 + 17.000 1.2859 0.0934 -4.0861 + 18.000 1.3082 0.1022 -4.4164 + 19.000 1.3304 0.1116 -4.7557 + 20.000 1.3446 0.1223 -5.0919 + 21.000 1.3594 0.1333 -5.4348 + 22.000 1.3722 0.1448 -5.7913 + 23.000 1.3765 0.1574 -6.1155 + 24.000 1.3896 0.1686 -6.5092 + 25.000 1.3903 0.1816 -6.8357 + 26.000 1.3909 0.1943 -7.1550 + 27.000 1.3872 0.2075 -7.4378 + 28.000 1.3796 0.2212 -7.6789 + 29.000 1.3509 0.2378 -7.7434 + 30.000 1.3352 0.2526 -7.8683 + 40.000 1.0851 0.4558 -1.0947 + 50.000 0.8811 0.6734 -1.0947 + 60.000 0.6733 0.8804 -1.0947 + 70.000 0.4507 1.0529 -1.0947 + 80.000 0.2203 1.1712 -1.0947 + 90.000 0.0000 1.2225 -1.0947 + 100.000 -0.1542 1.1712 -1.0947 + 110.000 -0.3155 1.0529 -1.0947 + 120.000 -0.4714 0.8804 -1.0947 + 130.000 -0.6167 0.6734 -1.0947 + 140.000 -0.7596 0.4558 -1.0947 + 150.000 -0.9346 0.2526 -1.0947 + 160.000 -0.6231 0.0877 -1.0947 + 170.000 -0.3115 0.0413 -1.0947 + 180.000 0.0000 0.0413 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0427 -1.0947 + -170.000 0.3231 0.0427 -1.0947 + -160.000 0.6463 0.0804 -1.0947 + -150.000 0.9694 0.2461 -1.0947 + -140.000 0.7808 0.4501 -1.0947 + -130.000 0.6292 0.6689 -1.0947 + -120.000 0.4780 0.8773 -1.0947 + -110.000 0.3184 1.0511 -1.0947 + -100.000 0.1550 1.1711 -1.0947 + -90.000 0.0000 1.2239 -1.0947 + -80.000 -0.1550 1.1711 -1.0947 + -70.000 -0.3184 1.0511 -1.0947 + -60.000 -0.4780 0.8773 -1.0947 + -50.000 -0.6292 0.6689 -1.0947 + -40.000 -0.7808 0.4501 -1.0947 + -30.000 -0.9694 0.2461 -1.0947 + -20.000 -0.7092 0.1463 -1.0947 + -10.000 -0.6414 0.0466 -2.5416 + -9.000 -0.6012 0.0445 -2.2848 + -8.000 -0.5628 0.0432 -2.0477 + -7.000 -0.4732 0.0423 -1.7376 + -6.000 -0.3741 0.0415 -1.4857 + -5.000 -0.2657 0.0409 -1.2907 + -4.000 -0.1535 0.0405 -1.1092 + -3.000 -0.0399 0.0400 -0.9966 + -2.000 0.0755 0.0393 -1.0106 + -1.000 0.1929 0.0390 -1.0920 + 0.000 0.3100 0.0390 -1.1782 + 1.000 0.4277 0.0390 -1.2673 + 2.000 0.5413 0.0393 -1.3545 + 4.000 0.7589 0.0401 -1.5387 + 5.000 0.8522 0.0412 -1.6269 + 6.000 0.9337 0.0423 -1.7204 + 7.000 0.9951 0.0432 -1.8277 + 8.000 1.0541 0.0443 -1.9823 + 9.000 1.0870 0.0467 -2.1278 + 10.000 1.1218 0.0498 -2.2774 + 11.000 1.1445 0.0541 -2.4431 + 12.000 1.1732 0.0589 -2.6923 + 13.000 1.1989 0.0645 -2.9418 + 14.000 1.2353 0.0700 -3.2423 + 15.000 1.2640 0.0765 -3.5369 + 16.000 1.2923 0.0836 -3.8535 + 17.000 1.3183 0.0914 -4.1926 + 19.000 1.3602 0.1094 -4.8697 + 20.000 1.3915 0.1176 -5.2827 + 21.000 1.4032 0.1286 -5.6231 + 22.000 1.4106 0.1406 -5.9723 + 23.000 1.4149 0.1531 -6.3138 + 24.000 1.4210 0.1654 -6.6853 + 25.000 1.4236 0.1780 -7.0348 + 26.000 1.4211 0.1910 -7.3486 + 27.000 1.4145 0.2046 -7.6241 + 28.000 1.4066 0.2183 -7.8765 + 29.000 1.3970 0.2320 -8.1002 + 30.000 1.3848 0.2461 -8.2820 + 40.000 1.1153 0.4501 -1.0947 + 50.000 0.8989 0.6689 -1.0947 + 60.000 0.6829 0.8773 -1.0947 + 70.000 0.4549 1.0511 -1.0947 + 80.000 0.2214 1.1711 -1.0947 + 90.000 0.0000 1.2239 -1.0947 + 100.000 -0.1550 1.1711 -1.0947 + 110.000 -0.3184 1.0511 -1.0947 + 120.000 -0.4780 0.8773 -1.0947 + 130.000 -0.6292 0.6689 -1.0947 + 140.000 -0.7808 0.4501 -1.0947 + 150.000 -0.9694 0.2461 -1.0947 + 160.000 -0.6463 0.0804 -1.0947 + 170.000 -0.3231 0.0427 -1.0947 + 180.000 0.0000 0.0427 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0427 -1.0947 + -170.000 0.3872 0.0427 -1.0947 + -160.000 0.7745 0.0957 -1.0947 + -150.000 0.9131 0.2601 -1.0947 + -140.000 0.7465 0.4626 -1.0947 + -130.000 0.6090 0.6794 -1.0947 + -120.000 0.4672 0.8854 -1.0947 + -110.000 0.3137 1.0567 -1.0947 + -100.000 0.1539 1.1738 -1.0947 + -90.000 0.0000 1.2239 -1.0947 + -80.000 -0.1539 1.1738 -1.0947 + -70.000 -0.3137 1.0567 -1.0947 + -60.000 -0.4672 0.8854 -1.0947 + -50.000 -0.6090 0.6794 -1.0947 + -40.000 -0.7465 0.4626 -1.0947 + -30.000 -0.9131 0.2601 -1.0947 + -20.000 -0.8002 0.1351 -1.0947 + -10.000 -0.6511 0.0461 -2.5695 + -9.000 -0.6241 0.0436 -2.3279 + -8.000 -0.5706 0.0427 -2.0616 + -5.000 -0.2653 0.0407 -1.2901 + -4.000 -0.1529 0.0402 -1.1086 + -3.000 -0.0377 0.0398 -0.9859 + -2.000 0.0776 0.0393 -1.0119 + -1.000 0.1949 0.0389 -1.0929 + 0.000 0.3132 0.0388 -1.1803 + 1.000 0.4292 0.0388 -1.2680 + 2.000 0.5426 0.0391 -1.3559 + 3.000 0.6544 0.0394 -1.4464 + 4.000 0.7581 0.0402 -1.5294 + 6.000 0.9397 0.0421 -1.7194 + 7.000 1.0132 0.0428 -1.8437 + 8.000 1.0686 0.0438 -1.9993 + 9.000 1.0986 0.0462 -2.1424 + 11.000 1.1565 0.0534 -2.4664 + 12.000 1.1929 0.0575 -2.7339 + 13.000 1.2310 0.0621 -3.0185 + 15.000 1.2979 0.0735 -3.6316 + 16.000 1.3263 0.0802 -3.9577 + 17.000 1.3551 0.0875 -4.3142 + 18.000 1.3747 0.0960 -4.6477 + 19.000 1.3968 0.1049 -5.0108 + 20.000 1.4117 0.1151 -5.3642 + 22.000 1.4284 0.1382 -6.0547 + 25.000 1.4397 0.1756 -7.1298 + 26.000 1.4383 0.1885 -7.4558 + 30.000 1.3044 0.2601 -6.0291 + 40.000 1.0665 0.4626 -1.0947 + 50.000 0.8700 0.6794 -1.0947 + 60.000 0.6674 0.8854 -1.0947 + 70.000 0.4482 1.0567 -1.0947 + 80.000 0.2198 1.1738 -1.0947 + 90.000 0.0000 1.2239 -1.0947 + 100.000 -0.1539 1.1738 -1.0947 + 110.000 -0.3137 1.0567 -1.0947 + 120.000 -0.4672 0.8854 -1.0947 + 130.000 -0.6090 0.6794 -1.0947 + 140.000 -0.7465 0.4626 -1.0947 + 150.000 -0.9131 0.2601 -1.0947 + 160.000 -0.7745 0.0957 -1.0947 + 170.000 -0.3872 0.0427 -1.0947 + 180.000 0.0000 0.0427 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0427 -1.0947 + -170.000 0.3342 0.0427 -1.0947 + -160.000 0.6685 0.0713 -1.0947 + -150.000 1.0026 0.2377 -1.0947 + -140.000 0.8010 0.4427 -1.0947 + -130.000 0.6412 0.6628 -1.0947 + -120.000 0.4844 0.8724 -1.0947 + -110.000 0.3211 1.0478 -1.0947 + -100.000 0.1557 1.1694 -1.0947 + -90.000 0.0000 1.2239 -1.0947 + -80.000 -0.1557 1.1694 -1.0947 + -70.000 -0.3211 1.0478 -1.0947 + -60.000 -0.4844 0.8724 -1.0947 + -50.000 -0.6412 0.6628 -1.0947 + -40.000 -0.8010 0.4427 -1.0947 + -30.000 -1.0026 0.2377 -1.0947 + -20.000 -0.7284 0.1419 -1.0947 + -10.000 -0.6488 0.0460 -2.5468 + -9.000 -0.6526 0.0433 -2.3850 + -8.000 -0.5774 0.0424 -2.0737 + -6.000 -0.3754 0.0410 -1.4897 + -4.000 -0.1520 0.0400 -1.1078 + -3.000 -0.0370 0.0397 -0.9872 + -2.000 0.0791 0.0393 -1.0126 + -1.000 0.1969 0.0388 -1.0942 + 0.000 0.3146 0.0387 -1.1810 + 1.000 0.4298 0.0388 -1.2686 + 2.000 0.5430 0.0391 -1.3579 + 4.000 0.7562 0.0403 -1.5250 + 5.000 0.8507 0.0413 -1.6163 + 6.000 0.9427 0.0420 -1.7254 + 7.000 1.0329 0.0426 -1.8611 + 8.000 1.0750 0.0437 -2.0065 + 9.000 1.1141 0.0456 -2.1622 + 10.000 1.1311 0.0493 -2.2748 + 11.000 1.1766 0.0523 -2.5065 + 12.000 1.2105 0.0564 -2.7712 + 13.000 1.2474 0.0610 -3.0573 + 14.000 1.2841 0.0660 -3.3650 + 16.000 1.3463 0.0784 -4.0198 + 17.000 1.3700 0.0859 -4.3625 + 18.000 1.3938 0.0940 -4.7136 + 19.000 1.4123 0.1031 -5.0694 + 20.000 1.4262 0.1133 -5.4224 + 21.000 1.4334 0.1246 -5.7546 + 24.000 1.4704 0.1580 -6.9565 + 25.000 1.4739 0.1702 -7.3296 + 26.000 1.4743 0.1829 -7.6801 + 27.000 1.4705 0.1958 -7.9975 + 28.000 1.4611 0.2093 -8.2673 + 29.000 1.4477 0.2234 -8.4899 + 30.000 1.4324 0.2377 -8.6765 + 40.000 1.1443 0.4427 -1.0947 + 50.000 0.9160 0.6628 -1.0947 + 60.000 0.6921 0.8724 -1.0947 + 70.000 0.4588 1.0478 -1.0947 + 80.000 0.2223 1.1694 -1.0947 + 90.000 0.0000 1.2239 -1.0947 + 100.000 -0.1557 1.1694 -1.0947 + 110.000 -0.3211 1.0478 -1.0947 + 120.000 -0.4844 0.8724 -1.0947 + 130.000 -0.6412 0.6628 -1.0947 + 140.000 -0.8010 0.4427 -1.0947 + 150.000 -1.0026 0.2377 -1.0947 + 160.000 -0.6685 0.0713 -1.0947 + 170.000 -0.3342 0.0427 -1.0947 + 180.000 0.0000 0.0427 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0427 -1.0947 + -170.000 0.3520 0.0427 -1.0947 + -160.000 0.7039 0.0744 -1.0947 + -150.000 0.9952 0.2405 -1.0947 + -140.000 0.7965 0.4453 -1.0947 + -130.000 0.6385 0.6648 -1.0947 + -120.000 0.4830 0.8740 -1.0947 + -110.000 0.3206 1.0489 -1.0947 + -100.000 0.1555 1.1699 -1.0947 + -90.000 0.0000 1.2239 -1.0947 + -80.000 -0.1555 1.1699 -1.0947 + -70.000 -0.3206 1.0489 -1.0947 + -60.000 -0.4830 0.8740 -1.0947 + -50.000 -0.6385 0.6648 -1.0947 + -40.000 -0.7965 0.4453 -1.0947 + -30.000 -0.9952 0.2405 -1.0947 + -20.000 -0.7566 0.1382 -1.0947 + -10.000 -0.6615 0.0454 -2.5749 + -7.000 -0.4812 0.0415 -1.7511 + -6.000 -0.3745 0.0410 -1.4789 + -5.000 -0.2649 0.0403 -1.2901 + -4.000 -0.1519 0.0399 -1.1074 + -3.000 -0.0361 0.0396 -0.9899 + -2.000 0.0806 0.0393 -1.0134 + -1.000 0.1980 0.0387 -1.0949 + 0.000 0.3153 0.0387 -1.1812 + 1.000 0.4305 0.0387 -1.2694 + 2.000 0.5438 0.0390 -1.3540 + 3.000 0.6538 0.0394 -1.4370 + 4.000 0.7567 0.0402 -1.5286 + 5.000 0.8514 0.0412 -1.6190 + 6.000 0.9493 0.0417 -1.7316 + 7.000 1.0402 0.0424 -1.8683 + 8.000 1.0816 0.0435 -2.0142 + 10.000 1.1457 0.0485 -2.2994 + 11.000 1.1887 0.0516 -2.5309 + 12.000 1.2299 0.0553 -2.8127 + 13.000 1.2688 0.0595 -3.1081 + 14.000 1.2973 0.0651 -3.3981 + 15.000 1.3297 0.0708 -3.7201 + 17.000 1.3841 0.0846 -4.4087 + 18.000 1.4065 0.0927 -4.7570 + 19.000 1.4248 0.1017 -5.1168 + 20.000 1.4340 0.1123 -5.4530 + 22.000 1.4727 0.1319 -6.2606 + 23.000 1.4802 0.1438 -6.6509 + 24.000 1.4856 0.1558 -7.0399 + 25.000 1.4882 0.1682 -7.4120 + 26.000 1.4872 0.1810 -7.7597 + 27.000 1.4803 0.1942 -8.0622 + 28.000 1.4702 0.2080 -8.3300 + 29.000 1.4581 0.2218 -8.5675 + 30.000 1.4218 0.2405 -8.1097 + 40.000 1.1378 0.4453 -1.0947 + 50.000 0.9121 0.6648 -1.0947 + 60.000 0.6900 0.8740 -1.0947 + 70.000 0.4579 1.0489 -1.0947 + 80.000 0.2222 1.1699 -1.0947 + 90.000 0.0000 1.2239 -1.0947 + 100.000 -0.1555 1.1699 -1.0947 + 110.000 -0.3206 1.0489 -1.0947 + 120.000 -0.4830 0.8740 -1.0947 + 130.000 -0.6385 0.6648 -1.0947 + 140.000 -0.7965 0.4453 -1.0947 + 150.000 -0.9952 0.2405 -1.0947 + 160.000 -0.7039 0.0744 -1.0947 + 170.000 -0.3520 0.0427 -1.0947 + 180.000 0.0000 0.0427 -1.0947 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0427 -1.0947 + -170.000 0.3392 0.0427 -1.0947 + -160.000 0.6784 0.0675 -1.0947 + -150.000 1.0176 0.2342 -1.0947 + -140.000 0.8101 0.4396 -1.0947 + -130.000 0.6466 0.6602 -1.0947 + -120.000 0.4873 0.8704 -1.0947 + -110.000 0.3224 1.0465 -1.0947 + -100.000 0.1559 1.1686 -1.0947 + -90.000 0.0000 1.2239 -1.0947 + -80.000 -0.1559 1.1686 -1.0947 + -70.000 -0.3224 1.0465 -1.0947 + -60.000 -0.4873 0.8704 -1.0947 + -50.000 -0.6466 0.6602 -1.0947 + -40.000 -0.8101 0.4396 -1.0947 + -30.000 -1.0176 0.2342 -1.0947 + -20.000 -0.7448 0.1396 -1.0947 + -10.000 -0.6743 0.0450 -2.6018 + -9.000 -0.6556 0.0432 -2.3780 + -8.000 -0.5837 0.0419 -2.0858 + -6.000 -0.3744 0.0409 -1.4766 + -5.000 -0.2648 0.0402 -1.2898 + -4.000 -0.1518 0.0397 -1.1073 + -3.000 -0.0353 0.0395 -0.9902 + -2.000 0.0816 0.0392 -1.0140 + -1.000 0.1984 0.0387 -1.0951 + 0.000 0.3163 0.0386 -1.1820 + 1.000 0.4294 0.0388 -1.2695 + 2.000 0.5437 0.0390 -1.3533 + 3.000 0.6525 0.0395 -1.4353 + 5.000 0.8543 0.0411 -1.6221 + 6.000 0.9522 0.0417 -1.7348 + 7.000 1.0473 0.0422 -1.8753 + 8.000 1.0856 0.0434 -2.0185 + 11.000 1.1998 0.0511 -2.5530 + 12.000 1.2410 0.0546 -2.8360 + 14.000 1.3174 0.0635 -3.4485 + 16.000 1.3700 0.0763 -4.0931 + 18.000 1.4171 0.0916 -4.7936 + 21.000 1.4744 0.1193 -5.9336 + 22.000 1.4840 0.1304 -6.3121 + 23.000 1.4926 0.1420 -6.7143 + 24.000 1.4971 0.1541 -7.1018 + 25.000 1.4986 0.1667 -7.4716 + 26.000 1.4943 0.1799 -7.8035 + 27.000 1.4875 0.1932 -8.1091 + 28.000 1.4786 0.2066 -8.3887 + 29.000 1.4676 0.2203 -8.6380 + 30.000 1.4537 0.2342 -8.8479 + 40.000 1.1573 0.4396 -1.0947 + 50.000 0.9237 0.6602 -1.0947 + 60.000 0.6962 0.8704 -1.0947 + 70.000 0.4606 1.0465 -1.0947 + 80.000 0.2228 1.1686 -1.0947 + 90.000 0.0000 1.2239 -1.0947 + 100.000 -0.1559 1.1686 -1.0947 + 110.000 -0.3224 1.0465 -1.0947 + 120.000 -0.4873 0.8704 -1.0947 + 130.000 -0.6466 0.6602 -1.0947 + 140.000 -0.8101 0.4396 -1.0947 + 150.000 -1.0176 0.2342 -1.0947 + 160.000 -0.6784 0.0675 -1.0947 + 170.000 -0.3392 0.0427 -1.0947 + 180.000 0.0000 0.0427 -1.0947 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276_coords.txt new file mode 100644 index 000000000..c25534441 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0276_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0276 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.024017 + 0.027091 0.050261 + 0.060263 0.077244 + 0.105430 0.102168 + 0.161359 0.123903 + 0.226526 0.140948 + 0.299152 0.151887 + 0.377257 0.154763 + 0.458710 0.148989 + 0.541290 0.136081 + 0.622743 0.118030 + 0.700848 0.097075 + 0.773474 0.075618 + 0.838641 0.055666 + 0.894570 0.038477 + 0.939737 0.024535 + 0.972909 0.013769 + 0.993181 0.005721 + 1.000000 0.000000 + 0.993181 -0.003319 + 0.972909 -0.006500 + 0.939737 -0.011272 + 0.894570 -0.018909 + 0.838641 -0.030129 + 0.773474 -0.044953 + 0.700848 -0.062630 + 0.622743 -0.081432 + 0.541290 -0.099052 + 0.458710 -0.113095 + 0.377257 -0.121217 + 0.299152 -0.121652 + 0.226526 -0.115046 + 0.161359 -0.102964 + 0.105430 -0.086660 + 0.060263 -0.067057 + 0.027091 -0.044998 + 0.006819 -0.021867 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329.dat new file mode 100644 index 000000000..b975b05e4 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0329 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0329_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0440 -1.2342 + -170.000 0.2755 0.0440 -1.2342 + -160.000 0.5509 0.0975 -1.2342 + -150.000 0.8264 0.2495 -1.2342 + -140.000 0.6797 0.4368 -1.2342 + -130.000 0.5573 0.6371 -1.2342 + -120.000 0.4292 0.8274 -1.2342 + -110.000 0.2891 0.9854 -1.2342 + -100.000 0.1421 1.0931 -1.2342 + -90.000 0.0000 1.1387 -1.2342 + -80.000 -0.1421 1.0931 -1.2342 + -70.000 -0.2891 0.9854 -1.2342 + -60.000 -0.4292 0.8274 -1.2342 + -50.000 -0.5573 0.6371 -1.2342 + -40.000 -0.6797 0.4368 -1.2342 + -30.000 -0.8264 0.2495 -1.2342 + -20.000 -0.5988 0.1504 -1.2342 + -10.000 -0.5303 0.0513 -2.4401 + -9.000 -0.5035 0.0484 -2.2366 + -8.000 -0.4769 0.0461 -2.0401 + -7.000 -0.4294 0.0449 -1.8137 + -6.000 -0.3453 0.0440 -1.5940 + -5.000 -0.2498 0.0433 -1.4194 + -4.000 -0.1500 0.0427 -1.2559 + -3.000 -0.0483 0.0418 -1.1508 + -2.000 0.0592 0.0417 -1.1501 + -1.000 0.1663 0.0416 -1.2243 + 0.000 0.2730 0.0417 -1.3028 + 1.000 0.3803 0.0417 -1.3838 + 2.000 0.4859 0.0418 -1.4645 + 3.000 0.5895 0.0420 -1.5483 + 4.000 0.6893 0.0424 -1.6326 + 5.000 0.7876 0.0427 -1.7252 + 6.000 0.8679 0.0437 -1.8212 + 7.000 0.9002 0.0452 -1.8936 + 8.000 0.9294 0.0472 -2.0052 + 9.000 0.9652 0.0498 -2.1398 + 10.000 1.0023 0.0528 -2.2804 + 11.000 1.0093 0.0580 -2.3844 + 12.000 1.0356 0.0628 -2.6060 + 13.000 1.0441 0.0697 -2.7946 + 14.000 1.0672 0.0761 -3.0285 + 15.000 1.0835 0.0837 -3.2567 + 16.000 1.1087 0.0911 -3.5261 + 17.000 1.1306 0.0991 -3.8034 + 18.000 1.1510 0.1080 -4.0965 + 19.000 1.1700 0.1172 -4.3899 + 20.000 1.1865 0.1272 -4.6928 + 21.000 1.2019 0.1375 -5.0028 + 22.000 1.2192 0.1473 -5.3358 + 23.000 1.2267 0.1588 -5.6377 + 24.000 1.2326 0.1704 -5.9354 + 25.000 1.2334 0.1825 -6.2163 + 26.000 1.2292 0.1953 -6.4566 + 28.000 1.2072 0.2216 -6.8060 + 29.000 1.1908 0.2363 -6.8946 + 30.000 1.1805 0.2495 -7.0038 + 40.000 0.9711 0.4368 -1.2342 + 50.000 0.7961 0.6371 -1.2342 + 60.000 0.6131 0.8274 -1.2342 + 70.000 0.4130 0.9854 -1.2342 + 80.000 0.2031 1.0931 -1.2342 + 90.000 0.0000 1.1387 -1.2342 + 100.000 -0.1421 1.0931 -1.2342 + 110.000 -0.2891 0.9854 -1.2342 + 120.000 -0.4292 0.8274 -1.2342 + 130.000 -0.5573 0.6371 -1.2342 + 140.000 -0.6797 0.4368 -1.2342 + 150.000 -0.8264 0.2495 -1.2342 + 160.000 -0.5509 0.0975 -1.2342 + 170.000 -0.2755 0.0440 -1.2342 + 180.000 0.0000 0.0440 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0873 -1.2342 + -170.000 0.2887 0.0873 -1.2342 + -160.000 0.5775 0.1303 -1.2342 + -150.000 0.8662 0.2831 -1.2342 + -140.000 0.7040 0.4714 -1.2342 + -130.000 0.5716 0.6732 -1.2342 + -120.000 0.4369 0.8650 -1.2342 + -110.000 0.2924 1.0248 -1.2342 + -100.000 0.1429 1.1345 -1.2342 + -90.000 0.0000 1.1821 -1.2342 + -80.000 -0.1429 1.1345 -1.2342 + -70.000 -0.2924 1.0248 -1.2342 + -60.000 -0.4369 0.8650 -1.2342 + -50.000 -0.5716 0.6732 -1.2342 + -40.000 -0.7040 0.4714 -1.2342 + -30.000 -0.8662 0.2831 -1.2342 + -20.000 -0.6329 0.1877 -1.2342 + -10.000 -0.5710 0.0922 -2.5258 + -9.000 -0.5294 0.0901 -2.2859 + -8.000 -0.5122 0.0883 -2.1009 + -7.000 -0.4370 0.0873 -1.8275 + -6.000 -0.3452 0.0866 -1.5943 + -5.000 -0.2481 0.0860 -1.4179 + -4.000 -0.1447 0.0855 -1.2505 + -3.000 -0.0401 0.0849 -1.1451 + -1.000 0.1750 0.0842 -1.2294 + 0.000 0.2837 0.0841 -1.3095 + 1.000 0.3905 0.0843 -1.3903 + 2.000 0.4984 0.0843 -1.4727 + 5.000 0.7926 0.0858 -1.7277 + 6.000 0.8625 0.0870 -1.8182 + 7.000 0.9104 0.0881 -1.9017 + 9.000 1.0001 0.0914 -2.1829 + 10.000 1.0176 0.0951 -2.3002 + 11.000 1.0483 0.0988 -2.4594 + 12.000 1.0619 0.1042 -2.6613 + 13.000 1.0871 0.1095 -2.8912 + 14.000 1.1125 0.1153 -3.1432 + 15.000 1.1426 0.1213 -3.4165 + 16.000 1.1668 0.1283 -3.6993 + 17.000 1.1917 0.1356 -4.0066 + 18.000 1.2125 0.1438 -4.3127 + 19.000 1.2331 0.1524 -4.6272 + 20.000 1.2462 0.1624 -4.9387 + 21.000 1.2599 0.1726 -5.2566 + 22.000 1.2717 0.1833 -5.5870 + 23.000 1.2757 0.1949 -5.8874 + 24.000 1.2879 0.2053 -6.2523 + 25.000 1.2885 0.2173 -6.5549 + 26.000 1.2891 0.2291 -6.8508 + 27.000 1.2857 0.2414 -7.1130 + 28.000 1.2786 0.2541 -7.3364 + 29.000 1.2520 0.2694 -7.3962 + 30.000 1.2375 0.2831 -7.5119 + 40.000 1.0057 0.4714 -1.2342 + 50.000 0.8166 0.6732 -1.2342 + 60.000 0.6240 0.8650 -1.2342 + 70.000 0.4177 1.0248 -1.2342 + 80.000 0.2042 1.1345 -1.2342 + 90.000 0.0000 1.1821 -1.2342 + 100.000 -0.1429 1.1345 -1.2342 + 110.000 -0.2924 1.0248 -1.2342 + 120.000 -0.4369 0.8650 -1.2342 + 130.000 -0.5716 0.6732 -1.2342 + 140.000 -0.7040 0.4714 -1.2342 + 150.000 -0.8662 0.2831 -1.2342 + 160.000 -0.5775 0.1303 -1.2342 + 170.000 -0.2887 0.0873 -1.2342 + 180.000 0.0000 0.0873 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0908 -1.2342 + -170.000 0.2995 0.0908 -1.2342 + -160.000 0.5990 0.1258 -1.2342 + -150.000 0.8984 0.2793 -1.2342 + -140.000 0.7236 0.4684 -1.2342 + -130.000 0.5832 0.6712 -1.2342 + -120.000 0.4430 0.8643 -1.2342 + -110.000 0.2951 1.0254 -1.2342 + -100.000 0.1436 1.1366 -1.2342 + -90.000 0.0000 1.1856 -1.2342 + -80.000 -0.1436 1.1366 -1.2342 + -70.000 -0.2951 1.0254 -1.2342 + -60.000 -0.4430 0.8643 -1.2342 + -50.000 -0.5832 0.6712 -1.2342 + -40.000 -0.7236 0.4684 -1.2342 + -30.000 -0.8984 0.2793 -1.2342 + -20.000 -0.6573 0.1869 -1.2342 + -10.000 -0.5945 0.0944 -2.5752 + -9.000 -0.5572 0.0925 -2.3371 + -8.000 -0.5216 0.0912 -2.1174 + -7.000 -0.4385 0.0904 -1.8300 + -6.000 -0.3467 0.0897 -1.5966 + -5.000 -0.2462 0.0891 -1.4158 + -4.000 -0.1422 0.0888 -1.2476 + -3.000 -0.0370 0.0883 -1.1433 + -2.000 0.0700 0.0876 -1.1563 + -1.000 0.1788 0.0874 -1.2317 + 0.000 0.2873 0.0874 -1.3116 + 1.000 0.3964 0.0874 -1.3941 + 2.000 0.5017 0.0876 -1.4750 + 4.000 0.7033 0.0884 -1.6456 + 5.000 0.7898 0.0894 -1.7274 + 6.000 0.8653 0.0904 -1.8141 + 7.000 0.9223 0.0912 -1.9135 + 8.000 0.9769 0.0923 -2.0568 + 9.000 1.0075 0.0945 -2.1916 + 10.000 1.0397 0.0974 -2.3303 + 11.000 1.0607 0.1014 -2.4839 + 12.000 1.0873 0.1058 -2.7148 + 13.000 1.1111 0.1110 -2.9461 + 14.000 1.1448 0.1161 -3.2245 + 15.000 1.1715 0.1221 -3.4976 + 16.000 1.1977 0.1287 -3.7910 + 17.000 1.2217 0.1359 -4.1053 + 19.000 1.2606 0.1526 -4.7329 + 20.000 1.2896 0.1602 -5.1156 + 21.000 1.3005 0.1704 -5.4311 + 22.000 1.3073 0.1816 -5.7547 + 23.000 1.3114 0.1931 -6.0712 + 24.000 1.3170 0.2045 -6.4155 + 25.000 1.3194 0.2162 -6.7394 + 26.000 1.3171 0.2283 -7.0302 + 27.000 1.3109 0.2409 -7.2856 + 28.000 1.3036 0.2535 -7.5195 + 29.000 1.2948 0.2662 -7.7268 + 30.000 1.2835 0.2793 -7.8953 + 40.000 1.0337 0.4684 -1.2342 + 50.000 0.8331 0.6712 -1.2342 + 60.000 0.6329 0.8643 -1.2342 + 70.000 0.4216 1.0254 -1.2342 + 80.000 0.2052 1.1366 -1.2342 + 90.000 0.0000 1.1856 -1.2342 + 100.000 -0.1436 1.1366 -1.2342 + 110.000 -0.2951 1.0254 -1.2342 + 120.000 -0.4430 0.8643 -1.2342 + 130.000 -0.5832 0.6712 -1.2342 + 140.000 -0.7236 0.4684 -1.2342 + 150.000 -0.8984 0.2793 -1.2342 + 160.000 -0.5990 0.1258 -1.2342 + 170.000 -0.2995 0.0908 -1.2342 + 180.000 0.0000 0.0908 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0908 -1.2342 + -170.000 0.3589 0.0908 -1.2342 + -160.000 0.7178 0.1399 -1.2342 + -150.000 0.8463 0.2923 -1.2342 + -140.000 0.6918 0.4800 -1.2342 + -130.000 0.5644 0.6809 -1.2342 + -120.000 0.4330 0.8718 -1.2342 + -110.000 0.2907 1.0306 -1.2342 + -100.000 0.1426 1.1392 -1.2342 + -90.000 0.0000 1.1856 -1.2342 + -80.000 -0.1426 1.1392 -1.2342 + -70.000 -0.2907 1.0306 -1.2342 + -60.000 -0.4330 0.8718 -1.2342 + -50.000 -0.5644 0.6809 -1.2342 + -40.000 -0.6918 0.4800 -1.2342 + -30.000 -0.8463 0.2923 -1.2342 + -20.000 -0.7416 0.1764 -1.2342 + -10.000 -0.6035 0.0940 -2.6010 + -9.000 -0.5784 0.0917 -2.3771 + -8.000 -0.5289 0.0908 -2.1303 + -5.000 -0.2459 0.0889 -1.4153 + -4.000 -0.1417 0.0885 -1.2471 + -3.000 -0.0350 0.0882 -1.1334 + -2.000 0.0720 0.0876 -1.1574 + -1.000 0.1806 0.0873 -1.2325 + 0.000 0.2903 0.0872 -1.3135 + 1.000 0.3977 0.0872 -1.3948 + 2.000 0.5029 0.0874 -1.4763 + 3.000 0.6065 0.0878 -1.5602 + 4.000 0.7026 0.0885 -1.6371 + 6.000 0.8709 0.0903 -1.8131 + 7.000 0.9390 0.0909 -1.9283 + 8.000 0.9903 0.0919 -2.0725 + 9.000 1.0182 0.0941 -2.2052 + 11.000 1.0718 0.1008 -2.5055 + 12.000 1.1056 0.1046 -2.7534 + 13.000 1.1409 0.1088 -3.0171 + 15.000 1.2029 0.1193 -3.5854 + 16.000 1.2292 0.1256 -3.8876 + 17.000 1.2559 0.1323 -4.2180 + 18.000 1.2741 0.1402 -4.5271 + 19.000 1.2946 0.1485 -4.8636 + 20.000 1.3084 0.1579 -5.1912 + 22.000 1.3238 0.1794 -5.8311 + 25.000 1.3343 0.2140 -6.8274 + 26.000 1.3330 0.2260 -7.1297 + 30.000 1.2089 0.2923 -5.8073 + 40.000 0.9884 0.4800 -1.2342 + 50.000 0.8063 0.6809 -1.2342 + 60.000 0.6186 0.8718 -1.2342 + 70.000 0.4154 1.0306 -1.2342 + 80.000 0.2037 1.1392 -1.2342 + 90.000 0.0000 1.1856 -1.2342 + 100.000 -0.1426 1.1392 -1.2342 + 110.000 -0.2907 1.0306 -1.2342 + 120.000 -0.4330 0.8718 -1.2342 + 130.000 -0.5644 0.6809 -1.2342 + 140.000 -0.6918 0.4800 -1.2342 + 150.000 -0.8463 0.2923 -1.2342 + 160.000 -0.7178 0.1399 -1.2342 + 170.000 -0.3589 0.0908 -1.2342 + 180.000 0.0000 0.0908 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0908 -1.2342 + -170.000 0.3097 0.0908 -1.2342 + -160.000 0.6195 0.1173 -1.2342 + -150.000 0.9292 0.2715 -1.2342 + -140.000 0.7423 0.4615 -1.2342 + -130.000 0.5943 0.6655 -1.2342 + -120.000 0.4490 0.8598 -1.2342 + -110.000 0.2976 1.0223 -1.2342 + -100.000 0.1443 1.1350 -1.2342 + -90.000 0.0000 1.1856 -1.2342 + -80.000 -0.1443 1.1350 -1.2342 + -70.000 -0.2976 1.0223 -1.2342 + -60.000 -0.4490 0.8598 -1.2342 + -50.000 -0.5943 0.6655 -1.2342 + -40.000 -0.7423 0.4615 -1.2342 + -30.000 -0.9292 0.2715 -1.2342 + -20.000 -0.6751 0.1827 -1.2342 + -10.000 -0.6013 0.0939 -2.5800 + -9.000 -0.6049 0.0913 -2.4300 + -8.000 -0.5351 0.0905 -2.1415 + -6.000 -0.3479 0.0892 -1.6003 + -4.000 -0.1409 0.0883 -1.2463 + -3.000 -0.0343 0.0881 -1.1345 + -2.000 0.0733 0.0876 -1.1581 + -1.000 0.1825 0.0872 -1.2337 + 0.000 0.2915 0.0871 -1.3142 + 1.000 0.3984 0.0872 -1.3953 + 2.000 0.5032 0.0874 -1.4781 + 4.000 0.7008 0.0886 -1.6330 + 5.000 0.7884 0.0895 -1.7176 + 6.000 0.8737 0.0902 -1.8187 + 7.000 0.9573 0.0907 -1.9445 + 8.000 0.9963 0.0918 -2.0792 + 9.000 1.0325 0.0935 -2.2235 + 10.000 1.0483 0.0969 -2.3279 + 11.000 1.0905 0.0997 -2.5427 + 12.000 1.1219 0.1035 -2.7879 + 13.000 1.1561 0.1078 -3.0531 + 14.000 1.1901 0.1124 -3.3382 + 16.000 1.2477 0.1239 -3.9451 + 17.000 1.2697 0.1309 -4.2627 + 18.000 1.2918 0.1384 -4.5882 + 19.000 1.3089 0.1468 -4.9179 + 20.000 1.3218 0.1562 -5.2450 + 21.000 1.3285 0.1667 -5.5530 + 24.000 1.3627 0.1976 -6.6668 + 25.000 1.3660 0.2090 -7.0127 + 26.000 1.3664 0.2208 -7.3375 + 27.000 1.3628 0.2327 -7.6317 + 28.000 1.3542 0.2452 -7.8817 + 29.000 1.3417 0.2583 -8.0880 + 30.000 1.3275 0.2715 -8.2610 + 40.000 1.0605 0.4615 -1.2342 + 50.000 0.8489 0.6655 -1.2342 + 60.000 0.6414 0.8598 -1.2342 + 70.000 0.4252 1.0223 -1.2342 + 80.000 0.2061 1.1350 -1.2342 + 90.000 0.0000 1.1856 -1.2342 + 100.000 -0.1443 1.1350 -1.2342 + 110.000 -0.2976 1.0223 -1.2342 + 120.000 -0.4490 0.8598 -1.2342 + 130.000 -0.5943 0.6655 -1.2342 + 140.000 -0.7423 0.4615 -1.2342 + 150.000 -0.9292 0.2715 -1.2342 + 160.000 -0.6195 0.1173 -1.2342 + 170.000 -0.3097 0.0908 -1.2342 + 180.000 0.0000 0.0908 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0908 -1.2342 + -170.000 0.3262 0.0908 -1.2342 + -160.000 0.6524 0.1202 -1.2342 + -150.000 0.9224 0.2741 -1.2342 + -140.000 0.7382 0.4639 -1.2342 + -130.000 0.5918 0.6674 -1.2342 + -120.000 0.4476 0.8613 -1.2342 + -110.000 0.2971 1.0234 -1.2342 + -100.000 0.1441 1.1355 -1.2342 + -90.000 0.0000 1.1856 -1.2342 + -80.000 -0.1441 1.1355 -1.2342 + -70.000 -0.2971 1.0234 -1.2342 + -60.000 -0.4476 0.8613 -1.2342 + -50.000 -0.5918 0.6674 -1.2342 + -40.000 -0.7382 0.4639 -1.2342 + -30.000 -0.9224 0.2741 -1.2342 + -20.000 -0.7012 0.1794 -1.2342 + -10.000 -0.6131 0.0934 -2.6061 + -7.000 -0.4460 0.0897 -1.8425 + -6.000 -0.3471 0.0892 -1.5903 + -5.000 -0.2455 0.0886 -1.4153 + -4.000 -0.1408 0.0882 -1.2460 + -3.000 -0.0335 0.0880 -1.1370 + -2.000 0.0747 0.0876 -1.1588 + -1.000 0.1835 0.0871 -1.2344 + 0.000 0.2922 0.0871 -1.3144 + 1.000 0.3990 0.0871 -1.3960 + 2.000 0.5040 0.0874 -1.4745 + 3.000 0.6059 0.0878 -1.5514 + 4.000 0.7013 0.0885 -1.6363 + 5.000 0.7890 0.0894 -1.7201 + 6.000 0.8798 0.0899 -1.8244 + 7.000 0.9640 0.0905 -1.9511 + 8.000 1.0024 0.0916 -2.0864 + 10.000 1.0619 0.0962 -2.3507 + 11.000 1.1017 0.0991 -2.5653 + 12.000 1.1399 0.1025 -2.8264 + 13.000 1.1759 0.1063 -3.1002 + 14.000 1.2023 0.1116 -3.3690 + 15.000 1.2323 0.1168 -3.6674 + 17.000 1.2828 0.1297 -4.3055 + 18.000 1.3035 0.1372 -4.6284 + 19.000 1.3205 0.1455 -4.9619 + 20.000 1.3290 0.1553 -5.2735 + 22.000 1.3649 0.1735 -6.0219 + 23.000 1.3718 0.1845 -6.3836 + 24.000 1.3769 0.1956 -6.7442 + 25.000 1.3793 0.2072 -7.0890 + 26.000 1.3784 0.2190 -7.4113 + 27.000 1.3719 0.2313 -7.6916 + 28.000 1.3626 0.2440 -7.9398 + 29.000 1.3514 0.2568 -8.1599 + 30.000 1.3177 0.2741 -7.7356 + 40.000 1.0545 0.4639 -1.2342 + 50.000 0.8454 0.6674 -1.2342 + 60.000 0.6395 0.8613 -1.2342 + 70.000 0.4244 1.0234 -1.2342 + 80.000 0.2059 1.1355 -1.2342 + 90.000 0.0000 1.1856 -1.2342 + 100.000 -0.1441 1.1355 -1.2342 + 110.000 -0.2971 1.0234 -1.2342 + 120.000 -0.4476 0.8613 -1.2342 + 130.000 -0.5918 0.6674 -1.2342 + 140.000 -0.7382 0.4639 -1.2342 + 150.000 -0.9224 0.2741 -1.2342 + 160.000 -0.6524 0.1202 -1.2342 + 170.000 -0.3262 0.0908 -1.2342 + 180.000 0.0000 0.0908 -1.2342 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0908 -1.2342 + -170.000 0.3144 0.0908 -1.2342 + -160.000 0.6287 0.1138 -1.2342 + -150.000 0.9431 0.2683 -1.2342 + -140.000 0.7508 0.4587 -1.2342 + -130.000 0.5992 0.6631 -1.2342 + -120.000 0.4516 0.8579 -1.2342 + -110.000 0.2988 1.0211 -1.2342 + -100.000 0.1445 1.1343 -1.2342 + -90.000 0.0000 1.1856 -1.2342 + -80.000 -0.1445 1.1343 -1.2342 + -70.000 -0.2988 1.0211 -1.2342 + -60.000 -0.4516 0.8579 -1.2342 + -50.000 -0.5992 0.6631 -1.2342 + -40.000 -0.7508 0.4587 -1.2342 + -30.000 -0.9431 0.2683 -1.2342 + -20.000 -0.6902 0.1806 -1.2342 + -10.000 -0.6249 0.0929 -2.6310 + -9.000 -0.6076 0.0912 -2.4236 + -8.000 -0.5409 0.0901 -2.1527 + -6.000 -0.3470 0.0891 -1.5882 + -5.000 -0.2454 0.0885 -1.4150 + -4.000 -0.1407 0.0881 -1.2459 + -3.000 -0.0328 0.0879 -1.1374 + -2.000 0.0757 0.0875 -1.1594 + -1.000 0.1839 0.0871 -1.2346 + 0.000 0.2931 0.0870 -1.3151 + 1.000 0.3980 0.0872 -1.3962 + 2.000 0.5039 0.0874 -1.4738 + 3.000 0.6047 0.0879 -1.5498 + 5.000 0.7918 0.0893 -1.7230 + 6.000 0.8825 0.0899 -1.8274 + 7.000 0.9707 0.0904 -1.9577 + 8.000 1.0061 0.0914 -2.0904 + 11.000 1.1120 0.0986 -2.5857 + 12.000 1.1501 0.1018 -2.8480 + 14.000 1.2210 0.1100 -3.4157 + 16.000 1.2697 0.1220 -4.0131 + 18.000 1.3134 0.1361 -4.6623 + 21.000 1.3665 0.1618 -5.7189 + 22.000 1.3754 0.1721 -6.0696 + 23.000 1.3833 0.1829 -6.4424 + 24.000 1.3875 0.1941 -6.8016 + 25.000 1.3889 0.2058 -7.1442 + 26.000 1.3849 0.2179 -7.4518 + 27.000 1.3786 0.2303 -7.7351 + 28.000 1.3703 0.2427 -7.9942 + 29.000 1.3602 0.2554 -8.2252 + 30.000 1.3473 0.2683 -8.4198 + 40.000 1.0725 0.4587 -1.2342 + 50.000 0.8561 0.6631 -1.2342 + 60.000 0.6452 0.8579 -1.2342 + 70.000 0.4269 1.0211 -1.2342 + 80.000 0.2065 1.1343 -1.2342 + 90.000 0.0000 1.1856 -1.2342 + 100.000 -0.1445 1.1343 -1.2342 + 110.000 -0.2988 1.0211 -1.2342 + 120.000 -0.4516 0.8579 -1.2342 + 130.000 -0.5992 0.6631 -1.2342 + 140.000 -0.7508 0.4587 -1.2342 + 150.000 -0.9431 0.2683 -1.2342 + 160.000 -0.6287 0.1138 -1.2342 + 170.000 -0.3144 0.0908 -1.2342 + 180.000 0.0000 0.0908 -1.2342 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329_coords.txt new file mode 100644 index 000000000..00d5ebe98 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0329_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0329 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.028484 + 0.027091 0.058831 + 0.060263 0.089532 + 0.105430 0.117836 + 0.161359 0.142554 + 0.226526 0.162174 + 0.299152 0.175281 + 0.377257 0.179988 + 0.458710 0.175693 + 0.541290 0.163772 + 0.622743 0.146064 + 0.700848 0.124660 + 0.773474 0.101839 + 0.838641 0.079535 + 0.894570 0.059015 + 0.939737 0.040853 + 0.972909 0.025130 + 0.993181 0.011587 + 1.000000 0.000000 + 0.993181 -0.009368 + 0.972909 -0.018416 + 0.939737 -0.028604 + 0.894570 -0.040942 + 0.838641 -0.055950 + 0.773474 -0.073518 + 0.700848 -0.092848 + 0.622743 -0.112264 + 0.541290 -0.129574 + 0.458710 -0.142543 + 0.377257 -0.149007 + 0.299152 -0.147357 + 0.226526 -0.138252 + 0.161359 -0.123216 + 0.105430 -0.103514 + 0.060263 -0.080124 + 0.027091 -0.053971 + 0.006819 -0.026499 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444.dat new file mode 100644 index 000000000..1957eb438 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0444 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0444_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.0878 -1.5368 + -170.000 0.2283 0.0878 -1.5368 + -160.000 0.4565 0.1322 -1.5368 + -150.000 0.6848 0.2582 -1.5368 + -140.000 0.5632 0.4133 -1.5368 + -130.000 0.4618 0.5793 -1.5368 + -120.000 0.3556 0.7370 -1.5368 + -110.000 0.2396 0.8679 -1.5368 + -100.000 0.1178 0.9572 -1.5368 + -90.000 0.0000 0.9950 -1.5368 + -80.000 -0.1178 0.9572 -1.5368 + -70.000 -0.2396 0.8679 -1.5368 + -60.000 -0.3556 0.7370 -1.5368 + -50.000 -0.4618 0.5793 -1.5368 + -40.000 -0.5632 0.4133 -1.5368 + -30.000 -0.6848 0.2582 -1.5368 + -20.000 -0.4962 0.1761 -1.5368 + -10.000 -0.4394 0.0939 -2.5360 + -9.000 -0.4172 0.0915 -2.3675 + -8.000 -0.3951 0.0896 -2.2046 + -7.000 -0.3558 0.0886 -2.0171 + -6.000 -0.2861 0.0879 -1.8350 + -5.000 -0.2070 0.0873 -1.6903 + -4.000 -0.1243 0.0868 -1.5548 + -3.000 -0.0400 0.0861 -1.4677 + -2.000 0.0491 0.0859 -1.4671 + -1.000 0.1378 0.0859 -1.5286 + 0.000 0.2262 0.0859 -1.5937 + 1.000 0.3151 0.0859 -1.6608 + 2.000 0.4026 0.0861 -1.7276 + 3.000 0.4885 0.0862 -1.7971 + 4.000 0.5711 0.0865 -1.8669 + 5.000 0.6526 0.0868 -1.9437 + 6.000 0.7191 0.0876 -2.0232 + 7.000 0.7459 0.0889 -2.0832 + 8.000 0.7701 0.0905 -2.1757 + 9.000 0.7998 0.0927 -2.2872 + 10.000 0.8305 0.0952 -2.4037 + 11.000 0.8363 0.0995 -2.4899 + 12.000 0.8581 0.1034 -2.6735 + 13.000 0.8652 0.1092 -2.8298 + 14.000 0.8843 0.1145 -3.0236 + 15.000 0.8978 0.1208 -3.2127 + 16.000 0.9186 0.1269 -3.4359 + 17.000 0.9369 0.1336 -3.6657 + 18.000 0.9538 0.1409 -3.9085 + 19.000 0.9695 0.1486 -4.1517 + 20.000 0.9832 0.1568 -4.4027 + 21.000 0.9959 0.1653 -4.6596 + 22.000 1.0102 0.1735 -4.9355 + 23.000 1.0165 0.1830 -5.1857 + 24.000 1.0214 0.1926 -5.4324 + 25.000 1.0220 0.2026 -5.6651 + 26.000 1.0185 0.2132 -5.8642 + 28.000 1.0003 0.2350 -6.1537 + 29.000 0.9868 0.2472 -6.2271 + 30.000 0.9782 0.2582 -6.3176 + 40.000 0.8047 0.4133 -1.5368 + 50.000 0.6597 0.5793 -1.5368 + 60.000 0.5080 0.7370 -1.5368 + 70.000 0.3422 0.8679 -1.5368 + 80.000 0.1683 0.9572 -1.5368 + 90.000 0.0000 0.9950 -1.5368 + 100.000 -0.1178 0.9572 -1.5368 + 110.000 -0.2396 0.8679 -1.5368 + 120.000 -0.3556 0.7370 -1.5368 + 130.000 -0.4618 0.5793 -1.5368 + 140.000 -0.5632 0.4133 -1.5368 + 150.000 -0.6848 0.2582 -1.5368 + 160.000 -0.4565 0.1322 -1.5368 + 170.000 -0.2283 0.0878 -1.5368 + 180.000 0.0000 0.0878 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1872 -1.5368 + -170.000 0.2392 0.1872 -1.5368 + -160.000 0.4785 0.2228 -1.5368 + -150.000 0.7178 0.3494 -1.5368 + -140.000 0.5834 0.5055 -1.5368 + -130.000 0.4736 0.6726 -1.5368 + -120.000 0.3620 0.8316 -1.5368 + -110.000 0.2423 0.9640 -1.5368 + -100.000 0.1184 1.0549 -1.5368 + -90.000 0.0000 1.0943 -1.5368 + -80.000 -0.1184 1.0549 -1.5368 + -70.000 -0.2423 0.9640 -1.5368 + -60.000 -0.3620 0.8316 -1.5368 + -50.000 -0.4736 0.6726 -1.5368 + -40.000 -0.5834 0.5055 -1.5368 + -30.000 -0.7178 0.3494 -1.5368 + -20.000 -0.5245 0.2703 -1.5368 + -10.000 -0.4731 0.1913 -2.6071 + -9.000 -0.4387 0.1895 -2.4083 + -8.000 -0.4244 0.1880 -2.2550 + -7.000 -0.3621 0.1872 -2.0285 + -6.000 -0.2860 0.1866 -1.8352 + -5.000 -0.2056 0.1861 -1.6890 + -4.000 -0.1199 0.1857 -1.5503 + -3.000 -0.0332 0.1852 -1.4630 + -1.000 0.1450 0.1846 -1.5328 + 0.000 0.2351 0.1845 -1.5992 + 1.000 0.3236 0.1847 -1.6662 + 2.000 0.4130 0.1847 -1.7344 + 5.000 0.6567 0.1859 -1.9457 + 6.000 0.7147 0.1869 -2.0208 + 7.000 0.7543 0.1878 -2.0899 + 9.000 0.8287 0.1906 -2.3229 + 10.000 0.8432 0.1936 -2.4202 + 11.000 0.8686 0.1967 -2.5521 + 12.000 0.8799 0.2011 -2.7194 + 13.000 0.9008 0.2055 -2.9099 + 14.000 0.9219 0.2103 -3.1187 + 15.000 0.9467 0.2153 -3.3452 + 16.000 0.9669 0.2211 -3.5794 + 17.000 0.9875 0.2272 -3.8341 + 18.000 1.0047 0.2340 -4.0877 + 19.000 1.0217 0.2411 -4.3483 + 20.000 1.0326 0.2494 -4.6065 + 21.000 1.0440 0.2578 -4.8698 + 22.000 1.0538 0.2667 -5.1436 + 23.000 1.0571 0.2763 -5.3926 + 24.000 1.0672 0.2850 -5.6949 + 25.000 1.0677 0.2949 -5.9456 + 26.000 1.0682 0.3046 -6.1909 + 27.000 1.0653 0.3148 -6.4081 + 28.000 1.0595 0.3254 -6.5932 + 29.000 1.0375 0.3381 -6.6428 + 30.000 1.0254 0.3494 -6.7387 + 40.000 0.8333 0.5055 -1.5368 + 50.000 0.6766 0.6726 -1.5368 + 60.000 0.5171 0.8316 -1.5368 + 70.000 0.3461 0.9640 -1.5368 + 80.000 0.1692 1.0549 -1.5368 + 90.000 0.0000 1.0943 -1.5368 + 100.000 -0.1184 1.0549 -1.5368 + 110.000 -0.2423 0.9640 -1.5368 + 120.000 -0.3620 0.8316 -1.5368 + 130.000 -0.4736 0.6726 -1.5368 + 140.000 -0.5834 0.5055 -1.5368 + 150.000 -0.7178 0.3494 -1.5368 + 160.000 -0.4785 0.2228 -1.5368 + 170.000 -0.2392 0.1872 -1.5368 + 180.000 0.0000 0.1872 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1952 -1.5368 + -170.000 0.2482 0.1952 -1.5368 + -160.000 0.4963 0.2242 -1.5368 + -150.000 0.7445 0.3514 -1.5368 + -140.000 0.5996 0.5081 -1.5368 + -130.000 0.4832 0.6762 -1.5368 + -120.000 0.3671 0.8361 -1.5368 + -110.000 0.2445 0.9697 -1.5368 + -100.000 0.1190 1.0618 -1.5368 + -90.000 0.0000 1.1024 -1.5368 + -80.000 -0.1190 1.0618 -1.5368 + -70.000 -0.2445 0.9697 -1.5368 + -60.000 -0.3671 0.8361 -1.5368 + -50.000 -0.4832 0.6762 -1.5368 + -40.000 -0.5996 0.5081 -1.5368 + -30.000 -0.7445 0.3514 -1.5368 + -20.000 -0.5447 0.2748 -1.5368 + -10.000 -0.4926 0.1982 -2.6480 + -9.000 -0.4617 0.1966 -2.4507 + -8.000 -0.4322 0.1956 -2.2686 + -7.000 -0.3634 0.1949 -2.0305 + -6.000 -0.2873 0.1943 -1.8371 + -5.000 -0.2040 0.1938 -1.6873 + -4.000 -0.1179 0.1935 -1.5480 + -3.000 -0.0307 0.1932 -1.4615 + -2.000 0.0580 0.1926 -1.4722 + -1.000 0.1481 0.1924 -1.5347 + 0.000 0.2381 0.1924 -1.6009 + 1.000 0.3285 0.1924 -1.6693 + 2.000 0.4157 0.1926 -1.7363 + 4.000 0.5828 0.1932 -1.8778 + 5.000 0.6545 0.1940 -1.9455 + 6.000 0.7170 0.1949 -2.0173 + 7.000 0.7642 0.1956 -2.0997 + 8.000 0.8095 0.1965 -2.2185 + 9.000 0.8348 0.1983 -2.3302 + 10.000 0.8615 0.2007 -2.4451 + 11.000 0.8789 0.2040 -2.5723 + 12.000 0.9009 0.2076 -2.7637 + 13.000 0.9207 0.2120 -2.9553 + 14.000 0.9486 0.2162 -3.1860 + 15.000 0.9707 0.2212 -3.4123 + 16.000 0.9925 0.2266 -3.6554 + 17.000 1.0124 0.2326 -3.9159 + 19.000 1.0445 0.2464 -4.4359 + 20.000 1.0686 0.2527 -4.7530 + 21.000 1.0776 0.2612 -5.0144 + 22.000 1.0832 0.2704 -5.2826 + 23.000 1.0866 0.2800 -5.5449 + 24.000 1.0913 0.2894 -5.8301 + 25.000 1.0933 0.2991 -6.0985 + 26.000 1.0914 0.3091 -6.3395 + 27.000 1.0862 0.3196 -6.5511 + 28.000 1.0802 0.3300 -6.7450 + 29.000 1.0729 0.3406 -6.9167 + 30.000 1.0635 0.3514 -7.0563 + 40.000 0.8565 0.5081 -1.5368 + 50.000 0.6903 0.6762 -1.5368 + 60.000 0.5245 0.8361 -1.5368 + 70.000 0.3493 0.9697 -1.5368 + 80.000 0.1700 1.0618 -1.5368 + 90.000 0.0000 1.1024 -1.5368 + 100.000 -0.1190 1.0618 -1.5368 + 110.000 -0.2445 0.9697 -1.5368 + 120.000 -0.3671 0.8361 -1.5368 + 130.000 -0.4832 0.6762 -1.5368 + 140.000 -0.5996 0.5081 -1.5368 + 150.000 -0.7445 0.3514 -1.5368 + 160.000 -0.4963 0.2242 -1.5368 + 170.000 -0.2482 0.1952 -1.5368 + 180.000 0.0000 0.1952 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1952 -1.5368 + -170.000 0.2974 0.1952 -1.5368 + -160.000 0.5948 0.2359 -1.5368 + -150.000 0.7012 0.3622 -1.5368 + -140.000 0.5733 0.5177 -1.5368 + -130.000 0.4677 0.6842 -1.5368 + -120.000 0.3588 0.8424 -1.5368 + -110.000 0.2409 0.9739 -1.5368 + -100.000 0.1181 1.0639 -1.5368 + -90.000 0.0000 1.1024 -1.5368 + -80.000 -0.1181 1.0639 -1.5368 + -70.000 -0.2409 0.9739 -1.5368 + -60.000 -0.3588 0.8424 -1.5368 + -50.000 -0.4677 0.6842 -1.5368 + -40.000 -0.5733 0.5177 -1.5368 + -30.000 -0.7012 0.3622 -1.5368 + -20.000 -0.6145 0.2662 -1.5368 + -10.000 -0.5000 0.1978 -2.6694 + -9.000 -0.4793 0.1959 -2.4839 + -8.000 -0.4382 0.1952 -2.2793 + -5.000 -0.2037 0.1937 -1.6869 + -4.000 -0.1174 0.1933 -1.5475 + -3.000 -0.0290 0.1930 -1.4533 + -2.000 0.0596 0.1926 -1.4732 + -1.000 0.1497 0.1923 -1.5355 + 0.000 0.2405 0.1922 -1.6025 + 1.000 0.3296 0.1922 -1.6699 + 2.000 0.4167 0.1924 -1.7374 + 3.000 0.5025 0.1927 -1.8069 + 4.000 0.5822 0.1933 -1.8707 + 6.000 0.7216 0.1948 -2.0165 + 7.000 0.7781 0.1953 -2.1120 + 8.000 0.8206 0.1961 -2.2315 + 9.000 0.8437 0.1979 -2.3414 + 11.000 0.8881 0.2035 -2.5902 + 12.000 0.9161 0.2066 -2.7957 + 13.000 0.9453 0.2101 -3.0142 + 15.000 0.9967 0.2188 -3.4850 + 16.000 1.0185 0.2240 -3.7355 + 17.000 1.0407 0.2296 -4.0092 + 18.000 1.0557 0.2362 -4.2653 + 19.000 1.0727 0.2430 -4.5442 + 20.000 1.0841 0.2508 -4.8156 + 22.000 1.0969 0.2686 -5.3459 + 25.000 1.1056 0.2973 -6.1715 + 26.000 1.1045 0.3072 -6.4219 + 30.000 1.0018 0.3622 -5.3262 + 40.000 0.8190 0.5177 -1.5368 + 50.000 0.6682 0.6842 -1.5368 + 60.000 0.5125 0.8424 -1.5368 + 70.000 0.3442 0.9739 -1.5368 + 80.000 0.1688 1.0639 -1.5368 + 90.000 0.0000 1.1024 -1.5368 + 100.000 -0.1181 1.0639 -1.5368 + 110.000 -0.2409 0.9739 -1.5368 + 120.000 -0.3588 0.8424 -1.5368 + 130.000 -0.4677 0.6842 -1.5368 + 140.000 -0.5733 0.5177 -1.5368 + 150.000 -0.7012 0.3622 -1.5368 + 160.000 -0.5948 0.2359 -1.5368 + 170.000 -0.2974 0.1952 -1.5368 + 180.000 0.0000 0.1952 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1952 -1.5368 + -170.000 0.2566 0.1952 -1.5368 + -160.000 0.5133 0.2172 -1.5368 + -150.000 0.7700 0.3450 -1.5368 + -140.000 0.6151 0.5024 -1.5368 + -130.000 0.4924 0.6714 -1.5368 + -120.000 0.3720 0.8324 -1.5368 + -110.000 0.2466 0.9671 -1.5368 + -100.000 0.1195 1.0604 -1.5368 + -90.000 0.0000 1.1024 -1.5368 + -80.000 -0.1195 1.0604 -1.5368 + -70.000 -0.2466 0.9671 -1.5368 + -60.000 -0.3720 0.8324 -1.5368 + -50.000 -0.4924 0.6714 -1.5368 + -40.000 -0.6151 0.5024 -1.5368 + -30.000 -0.7700 0.3450 -1.5368 + -20.000 -0.5594 0.2714 -1.5368 + -10.000 -0.4983 0.1978 -2.6520 + -9.000 -0.5012 0.1956 -2.5277 + -8.000 -0.4434 0.1950 -2.2886 + -6.000 -0.2883 0.1939 -1.8402 + -4.000 -0.1168 0.1932 -1.5469 + -3.000 -0.0284 0.1929 -1.4542 + -2.000 0.0607 0.1926 -1.4738 + -1.000 0.1512 0.1922 -1.5364 + 0.000 0.2416 0.1921 -1.6031 + 1.000 0.3301 0.1922 -1.6704 + 2.000 0.4170 0.1924 -1.7389 + 4.000 0.5807 0.1934 -1.8673 + 5.000 0.6533 0.1941 -1.9374 + 6.000 0.7240 0.1947 -2.0211 + 7.000 0.7933 0.1951 -2.1254 + 8.000 0.8256 0.1960 -2.2370 + 9.000 0.8556 0.1975 -2.3566 + 10.000 0.8686 0.2003 -2.4430 + 11.000 0.9036 0.2026 -2.6210 + 12.000 0.9296 0.2057 -2.8243 + 13.000 0.9579 0.2093 -3.0440 + 14.000 0.9861 0.2131 -3.2803 + 16.000 1.0339 0.2226 -3.7832 + 17.000 1.0521 0.2284 -4.0463 + 18.000 1.0704 0.2346 -4.3160 + 19.000 1.0846 0.2416 -4.5892 + 20.000 1.0952 0.2494 -4.8603 + 21.000 1.1008 0.2581 -5.1154 + 24.000 1.1292 0.2837 -6.0384 + 25.000 1.1319 0.2932 -6.3250 + 26.000 1.1322 0.3029 -6.5941 + 27.000 1.1293 0.3128 -6.8379 + 28.000 1.1221 0.3232 -7.0450 + 29.000 1.1118 0.3340 -7.2160 + 30.000 1.1000 0.3450 -7.3593 + 40.000 0.8788 0.5024 -1.5368 + 50.000 0.7034 0.6714 -1.5368 + 60.000 0.5315 0.8324 -1.5368 + 70.000 0.3523 0.9671 -1.5368 + 80.000 0.1708 1.0604 -1.5368 + 90.000 0.0000 1.1024 -1.5368 + 100.000 -0.1195 1.0604 -1.5368 + 110.000 -0.2466 0.9671 -1.5368 + 120.000 -0.3720 0.8324 -1.5368 + 130.000 -0.4924 0.6714 -1.5368 + 140.000 -0.6151 0.5024 -1.5368 + 150.000 -0.7700 0.3450 -1.5368 + 160.000 -0.5133 0.2172 -1.5368 + 170.000 -0.2566 0.1952 -1.5368 + 180.000 0.0000 0.1952 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1952 -1.5368 + -170.000 0.2703 0.1952 -1.5368 + -160.000 0.5406 0.2196 -1.5368 + -150.000 0.7643 0.3471 -1.5368 + -140.000 0.6117 0.5044 -1.5368 + -130.000 0.4904 0.6730 -1.5368 + -120.000 0.3709 0.8337 -1.5368 + -110.000 0.2462 0.9680 -1.5368 + -100.000 0.1194 1.0609 -1.5368 + -90.000 0.0000 1.1024 -1.5368 + -80.000 -0.1194 1.0609 -1.5368 + -70.000 -0.2462 0.9680 -1.5368 + -60.000 -0.3709 0.8337 -1.5368 + -50.000 -0.4904 0.6730 -1.5368 + -40.000 -0.6117 0.5044 -1.5368 + -30.000 -0.7643 0.3471 -1.5368 + -20.000 -0.5810 0.2686 -1.5368 + -10.000 -0.5080 0.1973 -2.6736 + -7.000 -0.3695 0.1943 -2.0409 + -6.000 -0.2876 0.1939 -1.8319 + -5.000 -0.2035 0.1934 -1.6869 + -4.000 -0.1167 0.1931 -1.5466 + -3.000 -0.0277 0.1929 -1.4563 + -2.000 0.0619 0.1926 -1.4744 + -1.000 0.1520 0.1921 -1.5370 + 0.000 0.2422 0.1921 -1.6033 + 1.000 0.3306 0.1921 -1.6709 + 2.000 0.4176 0.1924 -1.7360 + 3.000 0.5021 0.1927 -1.7997 + 4.000 0.5811 0.1933 -1.8700 + 5.000 0.6538 0.1940 -1.9394 + 6.000 0.7290 0.1945 -2.0259 + 7.000 0.7988 0.1950 -2.1309 + 8.000 0.8306 0.1959 -2.2430 + 10.000 0.8799 0.1997 -2.4620 + 11.000 0.9129 0.2021 -2.6398 + 12.000 0.9445 0.2049 -2.8562 + 13.000 0.9744 0.2081 -3.0830 + 14.000 0.9963 0.2124 -3.3057 + 15.000 1.0211 0.2168 -3.5530 + 17.000 1.0629 0.2274 -4.0818 + 18.000 1.0801 0.2336 -4.3493 + 19.000 1.0941 0.2406 -4.6256 + 20.000 1.1012 0.2487 -4.8838 + 22.000 1.1309 0.2638 -5.5040 + 23.000 1.1367 0.2728 -5.8037 + 24.000 1.1409 0.2820 -6.1025 + 25.000 1.1429 0.2916 -6.3882 + 26.000 1.1421 0.3014 -6.6553 + 27.000 1.1368 0.3116 -6.8875 + 28.000 1.1290 0.3221 -7.0932 + 29.000 1.1198 0.3327 -7.2756 + 30.000 1.0919 0.3471 -6.9240 + 40.000 0.8738 0.5044 -1.5368 + 50.000 0.7005 0.6730 -1.5368 + 60.000 0.5299 0.8337 -1.5368 + 70.000 0.3517 0.9680 -1.5368 + 80.000 0.1706 1.0609 -1.5368 + 90.000 0.0000 1.1024 -1.5368 + 100.000 -0.1194 1.0609 -1.5368 + 110.000 -0.2462 0.9680 -1.5368 + 120.000 -0.3709 0.8337 -1.5368 + 130.000 -0.4904 0.6730 -1.5368 + 140.000 -0.6117 0.5044 -1.5368 + 150.000 -0.7643 0.3471 -1.5368 + 160.000 -0.5406 0.2196 -1.5368 + 170.000 -0.2703 0.1952 -1.5368 + 180.000 0.0000 0.1952 -1.5368 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1952 -1.5368 + -170.000 0.2605 0.1952 -1.5368 + -160.000 0.5210 0.2143 -1.5368 + -150.000 0.7815 0.3423 -1.5368 + -140.000 0.6221 0.5001 -1.5368 + -130.000 0.4965 0.6694 -1.5368 + -120.000 0.3742 0.8309 -1.5368 + -110.000 0.2476 0.9661 -1.5368 + -100.000 0.1198 1.0599 -1.5368 + -90.000 0.0000 1.1024 -1.5368 + -80.000 -0.1198 1.0599 -1.5368 + -70.000 -0.2476 0.9661 -1.5368 + -60.000 -0.3742 0.8309 -1.5368 + -50.000 -0.4965 0.6694 -1.5368 + -40.000 -0.6221 0.5001 -1.5368 + -30.000 -0.7815 0.3423 -1.5368 + -20.000 -0.5719 0.2696 -1.5368 + -10.000 -0.5178 0.1970 -2.6942 + -9.000 -0.5035 0.1956 -2.5224 + -8.000 -0.4482 0.1946 -2.2979 + -6.000 -0.2875 0.1938 -1.8301 + -5.000 -0.2034 0.1933 -1.6867 + -4.000 -0.1166 0.1929 -1.5465 + -3.000 -0.0271 0.1928 -1.4566 + -2.000 0.0627 0.1925 -1.4749 + -1.000 0.1524 0.1921 -1.5371 + 0.000 0.2429 0.1921 -1.6039 + 1.000 0.3298 0.1922 -1.6711 + 2.000 0.4175 0.1924 -1.7354 + 3.000 0.5011 0.1928 -1.7984 + 5.000 0.6561 0.1940 -1.9418 + 6.000 0.7312 0.1945 -2.0284 + 7.000 0.8043 0.1948 -2.1363 + 8.000 0.8337 0.1957 -2.2463 + 11.000 0.9214 0.2016 -2.6567 + 12.000 0.9530 0.2044 -2.8740 + 14.000 1.0117 0.2112 -3.3444 + 16.000 1.0521 0.2210 -3.8395 + 18.000 1.0883 0.2327 -4.3774 + 21.000 1.1323 0.2540 -5.2529 + 22.000 1.1397 0.2626 -5.5436 + 23.000 1.1462 0.2715 -5.8524 + 24.000 1.1497 0.2808 -6.1500 + 25.000 1.1508 0.2905 -6.4340 + 26.000 1.1476 0.3006 -6.6888 + 27.000 1.1424 0.3108 -6.9235 + 28.000 1.1355 0.3211 -7.1382 + 29.000 1.1271 0.3316 -7.3297 + 30.000 1.1164 0.3423 -7.4909 + 40.000 0.8887 0.5001 -1.5368 + 50.000 0.7093 0.6694 -1.5368 + 60.000 0.5346 0.8309 -1.5368 + 70.000 0.3537 0.9661 -1.5368 + 80.000 0.1711 1.0599 -1.5368 + 90.000 0.0000 1.1024 -1.5368 + 100.000 -0.1198 1.0599 -1.5368 + 110.000 -0.2476 0.9661 -1.5368 + 120.000 -0.3742 0.8309 -1.5368 + 130.000 -0.4965 0.6694 -1.5368 + 140.000 -0.6221 0.5001 -1.5368 + 150.000 -0.7815 0.3423 -1.5368 + 160.000 -0.5210 0.2143 -1.5368 + 170.000 -0.2605 0.1952 -1.5368 + 180.000 0.0000 0.1952 -1.5368 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444_coords.txt new file mode 100644 index 000000000..690c1efdc --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0444_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0444 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.038173 + 0.027091 0.077420 + 0.060263 0.116187 + 0.105430 0.151821 + 0.161359 0.183011 + 0.226526 0.208215 + 0.299152 0.226025 + 0.377257 0.234704 + 0.458710 0.233617 + 0.541290 0.223836 + 0.622743 0.206871 + 0.700848 0.184493 + 0.773474 0.158713 + 0.838641 0.131307 + 0.894570 0.103562 + 0.939737 0.076248 + 0.972909 0.049770 + 0.993181 0.024310 + 1.000000 0.000000 + 0.993181 -0.022489 + 0.972909 -0.044262 + 0.939737 -0.066198 + 0.894570 -0.088734 + 0.838641 -0.111957 + 0.773474 -0.135477 + 0.700848 -0.158393 + 0.622743 -0.179140 + 0.541290 -0.195779 + 0.458710 -0.206420 + 0.377257 -0.209286 + 0.299152 -0.203115 + 0.226526 -0.188588 + 0.161359 -0.167145 + 0.105430 -0.140070 + 0.060263 -0.108468 + 0.027091 -0.073433 + 0.006819 -0.036544 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629.dat new file mode 100644 index 000000000..c6856a160 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0629 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0629_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.1584 -2.0237 + -170.000 0.1523 0.1584 -2.0237 + -160.000 0.3046 0.1880 -2.0237 + -150.000 0.4569 0.2721 -2.0237 + -140.000 0.3758 0.3756 -2.0237 + -130.000 0.3081 0.4864 -2.0237 + -120.000 0.2373 0.5916 -2.0237 + -110.000 0.1599 0.6790 -2.0237 + -100.000 0.0786 0.7385 -2.0237 + -90.000 0.0000 0.7637 -2.0237 + -80.000 -0.0786 0.7385 -2.0237 + -70.000 -0.1599 0.6790 -2.0237 + -60.000 -0.2373 0.5916 -2.0237 + -50.000 -0.3081 0.4864 -2.0237 + -40.000 -0.3758 0.3756 -2.0237 + -30.000 -0.4569 0.2721 -2.0237 + -20.000 -0.3311 0.2173 -2.0237 + -10.000 -0.2932 0.1625 -2.6904 + -9.000 -0.2784 0.1609 -2.5779 + -8.000 -0.2637 0.1596 -2.4693 + -7.000 -0.2374 0.1590 -2.3441 + -6.000 -0.1909 0.1585 -2.2226 + -5.000 -0.1381 0.1581 -2.1261 + -4.000 -0.0829 0.1578 -2.0357 + -3.000 -0.0267 0.1573 -1.9776 + -2.000 0.0328 0.1572 -1.9772 + -1.000 0.0920 0.1571 -2.0182 + 0.000 0.1509 0.1572 -2.0616 + 1.000 0.2102 0.1572 -2.1064 + 2.000 0.2686 0.1573 -2.1510 + 3.000 0.3259 0.1574 -2.1973 + 4.000 0.3811 0.1576 -2.2439 + 5.000 0.4355 0.1578 -2.2951 + 6.000 0.4799 0.1583 -2.3482 + 7.000 0.4977 0.1591 -2.3882 + 8.000 0.5139 0.1602 -2.4500 + 9.000 0.5337 0.1617 -2.5244 + 10.000 0.5542 0.1633 -2.6021 + 11.000 0.5581 0.1662 -2.6596 + 12.000 0.5726 0.1688 -2.7821 + 13.000 0.5773 0.1727 -2.8865 + 14.000 0.5900 0.1762 -3.0158 + 15.000 0.5991 0.1804 -3.1420 + 16.000 0.6130 0.1845 -3.2909 + 17.000 0.6251 0.1889 -3.4442 + 18.000 0.6364 0.1938 -3.6062 + 19.000 0.6469 0.1990 -3.7685 + 20.000 0.6560 0.2045 -3.9359 + 21.000 0.6645 0.2101 -4.1074 + 22.000 0.6741 0.2156 -4.2915 + 23.000 0.6782 0.2219 -4.4584 + 24.000 0.6815 0.2283 -4.6230 + 25.000 0.6820 0.2350 -4.7783 + 26.000 0.6796 0.2421 -4.9112 + 28.000 0.6675 0.2567 -5.1044 + 29.000 0.6584 0.2648 -5.1534 + 30.000 0.6527 0.2721 -5.2137 + 40.000 0.5369 0.3756 -2.0237 + 50.000 0.4402 0.4864 -2.0237 + 60.000 0.3390 0.5916 -2.0237 + 70.000 0.2284 0.6790 -2.0237 + 80.000 0.1123 0.7385 -2.0237 + 90.000 0.0000 0.7637 -2.0237 + 100.000 -0.0786 0.7385 -2.0237 + 110.000 -0.1599 0.6790 -2.0237 + 120.000 -0.2373 0.5916 -2.0237 + 130.000 -0.3081 0.4864 -2.0237 + 140.000 -0.3758 0.3756 -2.0237 + 150.000 -0.4569 0.2721 -2.0237 + 160.000 -0.3046 0.1880 -2.0237 + 170.000 -0.1523 0.1584 -2.0237 + 180.000 0.0000 0.1584 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3478 -2.0237 + -170.000 0.1596 0.3478 -2.0237 + -160.000 0.3193 0.3716 -2.0237 + -150.000 0.4789 0.4561 -2.0237 + -140.000 0.3893 0.5602 -2.0237 + -130.000 0.3160 0.6718 -2.0237 + -120.000 0.2415 0.7778 -2.0237 + -110.000 0.1617 0.8662 -2.0237 + -100.000 0.0790 0.9268 -2.0237 + -90.000 0.0000 0.9531 -2.0237 + -80.000 -0.0790 0.9268 -2.0237 + -70.000 -0.1617 0.8662 -2.0237 + -60.000 -0.2415 0.7778 -2.0237 + -50.000 -0.3160 0.6718 -2.0237 + -40.000 -0.3893 0.5602 -2.0237 + -30.000 -0.4789 0.4561 -2.0237 + -20.000 -0.3500 0.4033 -2.0237 + -10.000 -0.3157 0.3505 -2.7378 + -9.000 -0.2927 0.3494 -2.6052 + -8.000 -0.2832 0.3484 -2.5029 + -7.000 -0.2416 0.3478 -2.3517 + -6.000 -0.1909 0.3474 -2.2228 + -5.000 -0.1372 0.3471 -2.1252 + -4.000 -0.0800 0.3468 -2.0327 + -3.000 -0.0222 0.3465 -1.9744 + -1.000 0.0968 0.3461 -2.0210 + 0.000 0.1568 0.3461 -2.0653 + 1.000 0.2159 0.3462 -2.1100 + 2.000 0.2756 0.3462 -2.1555 + 5.000 0.4382 0.3470 -2.2965 + 6.000 0.4769 0.3477 -2.3466 + 7.000 0.5033 0.3483 -2.3927 + 9.000 0.5529 0.3501 -2.5482 + 10.000 0.5627 0.3521 -2.6131 + 11.000 0.5796 0.3542 -2.7011 + 12.000 0.5872 0.3571 -2.8127 + 13.000 0.6011 0.3601 -2.9399 + 14.000 0.6151 0.3633 -3.0792 + 15.000 0.6317 0.3666 -3.2303 + 16.000 0.6451 0.3705 -3.3866 + 17.000 0.6589 0.3745 -3.5565 + 18.000 0.6704 0.3791 -3.7258 + 19.000 0.6818 0.3838 -3.8997 + 20.000 0.6890 0.3894 -4.0719 + 21.000 0.6966 0.3950 -4.2477 + 22.000 0.7031 0.4009 -4.4304 + 23.000 0.7053 0.4073 -4.5965 + 24.000 0.7121 0.4131 -4.7982 + 25.000 0.7124 0.4197 -4.9655 + 26.000 0.7128 0.4262 -5.1291 + 27.000 0.7109 0.4330 -5.2741 + 28.000 0.7070 0.4400 -5.3976 + 29.000 0.6923 0.4485 -5.4307 + 30.000 0.6842 0.4561 -5.4947 + 40.000 0.5561 0.5602 -2.0237 + 50.000 0.4515 0.6718 -2.0237 + 60.000 0.3450 0.7778 -2.0237 + 70.000 0.2309 0.8662 -2.0237 + 80.000 0.1129 0.9268 -2.0237 + 90.000 0.0000 0.9531 -2.0237 + 100.000 -0.0790 0.9268 -2.0237 + 110.000 -0.1617 0.8662 -2.0237 + 120.000 -0.2415 0.7778 -2.0237 + 130.000 -0.3160 0.6718 -2.0237 + 140.000 -0.3893 0.5602 -2.0237 + 150.000 -0.4789 0.4561 -2.0237 + 160.000 -0.3193 0.3716 -2.0237 + 170.000 -0.1596 0.3478 -2.0237 + 180.000 0.0000 0.3478 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3632 -2.0237 + -170.000 0.1656 0.3632 -2.0237 + -160.000 0.3312 0.3825 -2.0237 + -150.000 0.4967 0.4674 -2.0237 + -140.000 0.4001 0.5720 -2.0237 + -130.000 0.3224 0.6841 -2.0237 + -120.000 0.2450 0.7908 -2.0237 + -110.000 0.1631 0.8799 -2.0237 + -100.000 0.0794 0.9414 -2.0237 + -90.000 0.0000 0.9685 -2.0237 + -80.000 -0.0794 0.9414 -2.0237 + -70.000 -0.1631 0.8799 -2.0237 + -60.000 -0.2450 0.7908 -2.0237 + -50.000 -0.3224 0.6841 -2.0237 + -40.000 -0.4001 0.5720 -2.0237 + -30.000 -0.4967 0.4674 -2.0237 + -20.000 -0.3634 0.4163 -2.0237 + -10.000 -0.3287 0.3652 -2.7651 + -9.000 -0.3081 0.3641 -2.6335 + -8.000 -0.2884 0.3634 -2.5120 + -7.000 -0.2425 0.3630 -2.3531 + -6.000 -0.1917 0.3626 -2.2240 + -5.000 -0.1361 0.3622 -2.1241 + -4.000 -0.0786 0.3620 -2.0311 + -3.000 -0.0205 0.3618 -1.9734 + -2.000 0.0387 0.3614 -1.9806 + -1.000 0.0989 0.3613 -2.0223 + 0.000 0.1588 0.3613 -2.0664 + 1.000 0.2192 0.3613 -2.1121 + 2.000 0.2774 0.3614 -2.1568 + 4.000 0.3889 0.3619 -2.2512 + 5.000 0.4367 0.3624 -2.2964 + 6.000 0.4784 0.3630 -2.3443 + 7.000 0.5099 0.3634 -2.3993 + 8.000 0.5401 0.3640 -2.4785 + 9.000 0.5570 0.3652 -2.5530 + 10.000 0.5749 0.3668 -2.6297 + 11.000 0.5865 0.3690 -2.7146 + 12.000 0.6012 0.3715 -2.8423 + 13.000 0.6143 0.3743 -2.9702 + 14.000 0.6330 0.3772 -3.1241 + 15.000 0.6477 0.3805 -3.2751 + 16.000 0.6622 0.3841 -3.4373 + 17.000 0.6755 0.3881 -3.6111 + 19.000 0.6970 0.3973 -3.9581 + 20.000 0.7131 0.4015 -4.1697 + 21.000 0.7191 0.4072 -4.3441 + 22.000 0.7228 0.4134 -4.5231 + 23.000 0.7251 0.4197 -4.6981 + 24.000 0.7282 0.4260 -4.8884 + 25.000 0.7295 0.4325 -5.0675 + 26.000 0.7282 0.4392 -5.2283 + 27.000 0.7248 0.4462 -5.3695 + 28.000 0.7208 0.4531 -5.4989 + 29.000 0.7159 0.4602 -5.6135 + 30.000 0.7096 0.4674 -5.7066 + 40.000 0.5715 0.5720 -2.0237 + 50.000 0.4606 0.6841 -2.0237 + 60.000 0.3500 0.7908 -2.0237 + 70.000 0.2331 0.8799 -2.0237 + 80.000 0.1134 0.9414 -2.0237 + 90.000 0.0000 0.9685 -2.0237 + 100.000 -0.0794 0.9414 -2.0237 + 110.000 -0.1631 0.8799 -2.0237 + 120.000 -0.2450 0.7908 -2.0237 + 130.000 -0.3224 0.6841 -2.0237 + 140.000 -0.4001 0.5720 -2.0237 + 150.000 -0.4967 0.4674 -2.0237 + 160.000 -0.3312 0.3825 -2.0237 + 170.000 -0.1656 0.3632 -2.0237 + 180.000 0.0000 0.3632 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3632 -2.0237 + -170.000 0.1984 0.3632 -2.0237 + -160.000 0.3969 0.3903 -2.0237 + -150.000 0.4679 0.4746 -2.0237 + -140.000 0.3825 0.5784 -2.0237 + -130.000 0.3121 0.6895 -2.0237 + -120.000 0.2394 0.7950 -2.0237 + -110.000 0.1608 0.8828 -2.0237 + -100.000 0.0788 0.9428 -2.0237 + -90.000 0.0000 0.9685 -2.0237 + -80.000 -0.0788 0.9428 -2.0237 + -70.000 -0.1608 0.8828 -2.0237 + -60.000 -0.2394 0.7950 -2.0237 + -50.000 -0.3121 0.6895 -2.0237 + -40.000 -0.3825 0.5784 -2.0237 + -30.000 -0.4679 0.4746 -2.0237 + -20.000 -0.4101 0.4105 -2.0237 + -10.000 -0.3337 0.3649 -2.7794 + -9.000 -0.3198 0.3637 -2.6556 + -8.000 -0.2924 0.3632 -2.5191 + -5.000 -0.1360 0.3621 -2.1238 + -4.000 -0.0783 0.3619 -2.0308 + -3.000 -0.0193 0.3617 -1.9679 + -2.000 0.0398 0.3614 -1.9812 + -1.000 0.0999 0.3612 -2.0228 + 0.000 0.1605 0.3612 -2.0675 + 1.000 0.2199 0.3612 -2.1125 + 2.000 0.2781 0.3613 -2.1575 + 3.000 0.3353 0.3615 -2.2039 + 4.000 0.3885 0.3619 -2.2464 + 6.000 0.4815 0.3629 -2.3438 + 7.000 0.5192 0.3632 -2.4075 + 8.000 0.5476 0.3638 -2.4872 + 9.000 0.5629 0.3650 -2.5606 + 11.000 0.5926 0.3687 -2.7266 + 12.000 0.6113 0.3708 -2.8637 + 13.000 0.6308 0.3731 -3.0095 + 15.000 0.6651 0.3789 -3.3236 + 16.000 0.6796 0.3824 -3.4907 + 17.000 0.6944 0.3861 -3.6734 + 18.000 0.7045 0.3905 -3.8443 + 19.000 0.7158 0.3950 -4.0304 + 20.000 0.7234 0.4003 -4.2115 + 22.000 0.7319 0.4121 -4.5653 + 25.000 0.7378 0.4313 -5.1162 + 26.000 0.7370 0.4379 -5.2833 + 30.000 0.6684 0.4746 -4.5522 + 40.000 0.5465 0.5784 -2.0237 + 50.000 0.4458 0.6895 -2.0237 + 60.000 0.3420 0.7950 -2.0237 + 70.000 0.2297 0.8828 -2.0237 + 80.000 0.1126 0.9428 -2.0237 + 90.000 0.0000 0.9685 -2.0237 + 100.000 -0.0788 0.9428 -2.0237 + 110.000 -0.1608 0.8828 -2.0237 + 120.000 -0.2394 0.7950 -2.0237 + 130.000 -0.3121 0.6895 -2.0237 + 140.000 -0.3825 0.5784 -2.0237 + 150.000 -0.4679 0.4746 -2.0237 + 160.000 -0.3969 0.3903 -2.0237 + 170.000 -0.1984 0.3632 -2.0237 + 180.000 0.0000 0.3632 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3632 -2.0237 + -170.000 0.1712 0.3632 -2.0237 + -160.000 0.3425 0.3778 -2.0237 + -150.000 0.5138 0.4631 -2.0237 + -140.000 0.4104 0.5681 -2.0237 + -130.000 0.3286 0.6809 -2.0237 + -120.000 0.2482 0.7884 -2.0237 + -110.000 0.1646 0.8782 -2.0237 + -100.000 0.0798 0.9405 -2.0237 + -90.000 0.0000 0.9685 -2.0237 + -80.000 -0.0798 0.9405 -2.0237 + -70.000 -0.1646 0.8782 -2.0237 + -60.000 -0.2482 0.7884 -2.0237 + -50.000 -0.3286 0.6809 -2.0237 + -40.000 -0.4104 0.5681 -2.0237 + -30.000 -0.5138 0.4631 -2.0237 + -20.000 -0.3732 0.4140 -2.0237 + -10.000 -0.3325 0.3649 -2.7678 + -9.000 -0.3344 0.3635 -2.6848 + -8.000 -0.2959 0.3630 -2.5253 + -6.000 -0.1924 0.3623 -2.2261 + -4.000 -0.0779 0.3618 -2.0304 + -3.000 -0.0189 0.3617 -1.9686 + -2.000 0.0405 0.3614 -1.9816 + -1.000 0.1009 0.3612 -2.0234 + 0.000 0.1612 0.3611 -2.0679 + 1.000 0.2203 0.3612 -2.1128 + 2.000 0.2782 0.3613 -2.1585 + 4.000 0.3875 0.3620 -2.2442 + 5.000 0.4359 0.3624 -2.2910 + 6.000 0.4831 0.3628 -2.3468 + 7.000 0.5293 0.3631 -2.4164 + 8.000 0.5509 0.3637 -2.4909 + 9.000 0.5709 0.3647 -2.5707 + 10.000 0.5796 0.3665 -2.6284 + 11.000 0.6029 0.3681 -2.7471 + 12.000 0.6203 0.3702 -2.8827 + 13.000 0.6392 0.3725 -3.0293 + 14.000 0.6580 0.3751 -3.1870 + 16.000 0.6899 0.3815 -3.5226 + 17.000 0.7020 0.3853 -3.6982 + 18.000 0.7142 0.3895 -3.8781 + 19.000 0.7237 0.3941 -4.0604 + 20.000 0.7308 0.3993 -4.2413 + 21.000 0.7345 0.4052 -4.4116 + 24.000 0.7535 0.4222 -5.0274 + 25.000 0.7553 0.4285 -5.2186 + 26.000 0.7555 0.4350 -5.3982 + 27.000 0.7535 0.4416 -5.5609 + 28.000 0.7487 0.4485 -5.6991 + 29.000 0.7419 0.4558 -5.8132 + 30.000 0.7340 0.4631 -5.9088 + 40.000 0.5864 0.5681 -2.0237 + 50.000 0.4694 0.6809 -2.0237 + 60.000 0.3546 0.7884 -2.0237 + 70.000 0.2351 0.8782 -2.0237 + 80.000 0.1139 0.9405 -2.0237 + 90.000 0.0000 0.9685 -2.0237 + 100.000 -0.0798 0.9405 -2.0237 + 110.000 -0.1646 0.8782 -2.0237 + 120.000 -0.2482 0.7884 -2.0237 + 130.000 -0.3286 0.6809 -2.0237 + 140.000 -0.4104 0.5681 -2.0237 + 150.000 -0.5138 0.4631 -2.0237 + 160.000 -0.3425 0.3778 -2.0237 + 170.000 -0.1712 0.3632 -2.0237 + 180.000 0.0000 0.3632 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3632 -2.0237 + -170.000 0.1804 0.3632 -2.0237 + -160.000 0.3607 0.3794 -2.0237 + -150.000 0.5100 0.4645 -2.0237 + -140.000 0.4081 0.5695 -2.0237 + -130.000 0.3272 0.6820 -2.0237 + -120.000 0.2475 0.7892 -2.0237 + -110.000 0.1643 0.8788 -2.0237 + -100.000 0.0797 0.9408 -2.0237 + -90.000 0.0000 0.9685 -2.0237 + -80.000 -0.0797 0.9408 -2.0237 + -70.000 -0.1643 0.8788 -2.0237 + -60.000 -0.2475 0.7892 -2.0237 + -50.000 -0.3272 0.6820 -2.0237 + -40.000 -0.4081 0.5695 -2.0237 + -30.000 -0.5100 0.4645 -2.0237 + -20.000 -0.3877 0.4121 -2.0237 + -10.000 -0.3390 0.3646 -2.7822 + -7.000 -0.2466 0.3626 -2.3600 + -6.000 -0.1919 0.3623 -2.2206 + -5.000 -0.1358 0.3620 -2.1238 + -4.000 -0.0779 0.3618 -2.0302 + -3.000 -0.0185 0.3616 -1.9699 + -2.000 0.0413 0.3614 -1.9820 + -1.000 0.1014 0.3611 -2.0238 + 0.000 0.1616 0.3611 -2.0680 + 1.000 0.2206 0.3611 -2.1132 + 2.000 0.2786 0.3613 -2.1566 + 3.000 0.3350 0.3615 -2.1991 + 4.000 0.3877 0.3619 -2.2460 + 5.000 0.4363 0.3624 -2.2923 + 6.000 0.4864 0.3627 -2.3500 + 7.000 0.5330 0.3630 -2.4201 + 8.000 0.5543 0.3636 -2.4949 + 10.000 0.5871 0.3661 -2.6410 + 11.000 0.6091 0.3678 -2.7596 + 12.000 0.6303 0.3696 -2.9040 + 13.000 0.6502 0.3718 -3.0554 + 14.000 0.6648 0.3746 -3.2040 + 15.000 0.6814 0.3776 -3.3690 + 17.000 0.7092 0.3846 -3.7218 + 18.000 0.7207 0.3888 -3.9004 + 19.000 0.7301 0.3934 -4.0847 + 20.000 0.7348 0.3989 -4.2570 + 22.000 0.7546 0.4089 -4.6708 + 23.000 0.7585 0.4150 -4.8708 + 24.000 0.7613 0.4211 -5.0702 + 25.000 0.7626 0.4275 -5.2609 + 26.000 0.7621 0.4341 -5.4390 + 27.000 0.7585 0.4408 -5.5940 + 28.000 0.7534 0.4479 -5.7312 + 29.000 0.7472 0.4549 -5.8529 + 30.000 0.7286 0.4645 -5.6184 + 40.000 0.5831 0.5695 -2.0237 + 50.000 0.4674 0.6820 -2.0237 + 60.000 0.3536 0.7892 -2.0237 + 70.000 0.2347 0.8788 -2.0237 + 80.000 0.1138 0.9408 -2.0237 + 90.000 0.0000 0.9685 -2.0237 + 100.000 -0.0797 0.9408 -2.0237 + 110.000 -0.1643 0.8788 -2.0237 + 120.000 -0.2475 0.7892 -2.0237 + 130.000 -0.3272 0.6820 -2.0237 + 140.000 -0.4081 0.5695 -2.0237 + 150.000 -0.5100 0.4645 -2.0237 + 160.000 -0.3607 0.3794 -2.0237 + 170.000 -0.1804 0.3632 -2.0237 + 180.000 0.0000 0.3632 -2.0237 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.3632 -2.0237 + -170.000 0.1738 0.3632 -2.0237 + -160.000 0.3476 0.3759 -2.0237 + -150.000 0.5215 0.4613 -2.0237 + -140.000 0.4151 0.5666 -2.0237 + -130.000 0.3313 0.6796 -2.0237 + -120.000 0.2497 0.7873 -2.0237 + -110.000 0.1652 0.8775 -2.0237 + -100.000 0.0799 0.9401 -2.0237 + -90.000 0.0000 0.9685 -2.0237 + -80.000 -0.0799 0.9401 -2.0237 + -70.000 -0.1652 0.8775 -2.0237 + -60.000 -0.2497 0.7873 -2.0237 + -50.000 -0.3313 0.6796 -2.0237 + -40.000 -0.4151 0.5666 -2.0237 + -30.000 -0.5215 0.4613 -2.0237 + -20.000 -0.3816 0.4128 -2.0237 + -10.000 -0.3455 0.3643 -2.7960 + -9.000 -0.3360 0.3634 -2.6813 + -8.000 -0.2991 0.3628 -2.5315 + -6.000 -0.1918 0.3622 -2.2194 + -5.000 -0.1357 0.3619 -2.1237 + -4.000 -0.0778 0.3617 -2.0301 + -3.000 -0.0181 0.3616 -1.9701 + -2.000 0.0418 0.3614 -1.9823 + -1.000 0.1017 0.3611 -2.0239 + 0.000 0.1621 0.3611 -2.0684 + 1.000 0.2201 0.3612 -2.1133 + 2.000 0.2786 0.3613 -2.1562 + 3.000 0.3343 0.3616 -2.1982 + 5.000 0.4378 0.3623 -2.2939 + 6.000 0.4879 0.3627 -2.3517 + 7.000 0.5367 0.3629 -2.4237 + 8.000 0.5563 0.3635 -2.4971 + 11.000 0.6148 0.3675 -2.7710 + 12.000 0.6359 0.3693 -2.9159 + 14.000 0.6751 0.3738 -3.2298 + 16.000 0.7020 0.3804 -3.5602 + 18.000 0.7262 0.3882 -3.9191 + 21.000 0.7555 0.4024 -4.5033 + 22.000 0.7605 0.4081 -4.6972 + 23.000 0.7648 0.4141 -4.9033 + 24.000 0.7671 0.4203 -5.1019 + 25.000 0.7679 0.4267 -5.2914 + 26.000 0.7657 0.4335 -5.4614 + 27.000 0.7623 0.4403 -5.6180 + 28.000 0.7577 0.4472 -5.7613 + 29.000 0.7521 0.4542 -5.8891 + 30.000 0.7449 0.4613 -5.9967 + 40.000 0.5930 0.5666 -2.0237 + 50.000 0.4733 0.6796 -2.0237 + 60.000 0.3567 0.7873 -2.0237 + 70.000 0.2360 0.8775 -2.0237 + 80.000 0.1142 0.9401 -2.0237 + 90.000 0.0000 0.9685 -2.0237 + 100.000 -0.0799 0.9401 -2.0237 + 110.000 -0.1652 0.8775 -2.0237 + 120.000 -0.2497 0.7873 -2.0237 + 130.000 -0.3313 0.6796 -2.0237 + 140.000 -0.4151 0.5666 -2.0237 + 150.000 -0.5215 0.4613 -2.0237 + 160.000 -0.3476 0.3759 -2.0237 + 170.000 -0.1738 0.3632 -2.0237 + 180.000 0.0000 0.3632 -2.0237 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629_coords.txt new file mode 100644 index 000000000..0384c9cbb --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0629_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0629 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.053204 + 0.027091 0.106260 + 0.060263 0.157538 + 0.105430 0.204545 + 0.161359 0.245774 + 0.226526 0.279641 + 0.299152 0.304748 + 0.377257 0.319589 + 0.458710 0.323479 + 0.541290 0.317019 + 0.622743 0.301206 + 0.700848 0.277317 + 0.773474 0.246947 + 0.838641 0.211626 + 0.894570 0.172671 + 0.939737 0.131160 + 0.972909 0.087998 + 0.993181 0.044048 + 1.000000 0.000000 + 0.993181 -0.042845 + 0.972909 -0.084360 + 0.939737 -0.124522 + 0.894570 -0.162878 + 0.838641 -0.198845 + 0.773474 -0.231600 + 0.700848 -0.260079 + 0.622743 -0.282891 + 0.541290 -0.298488 + 0.458710 -0.305516 + 0.377257 -0.302801 + 0.299152 -0.289617 + 0.226526 -0.266678 + 0.161359 -0.235296 + 0.105430 -0.196784 + 0.060263 -0.152440 + 0.027091 -0.103626 + 0.006819 -0.052128 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864.dat new file mode 100644 index 000000000..d9dc199df --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864.dat @@ -0,0 +1,560 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_0864 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_0864_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 72 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.2481 -2.6421 + -170.000 0.0558 0.2481 -2.6421 + -160.000 0.1117 0.2589 -2.6421 + -150.000 0.1675 0.2898 -2.6421 + -140.000 0.1378 0.3277 -2.6421 + -130.000 0.1130 0.3683 -2.6421 + -120.000 0.0870 0.4069 -2.6421 + -110.000 0.0586 0.4389 -2.6421 + -100.000 0.0288 0.4607 -2.6421 + -90.000 0.0000 0.4700 -2.6421 + -80.000 -0.0288 0.4607 -2.6421 + -70.000 -0.0586 0.4389 -2.6421 + -60.000 -0.0870 0.4069 -2.6421 + -50.000 -0.1130 0.3683 -2.6421 + -40.000 -0.1378 0.3277 -2.6421 + -30.000 -0.1675 0.2898 -2.6421 + -20.000 -0.1214 0.2697 -2.6421 + -10.000 -0.1075 0.2496 -2.8865 + -9.000 -0.1021 0.2490 -2.8453 + -8.000 -0.0966 0.2485 -2.8054 + -7.000 -0.0870 0.2483 -2.7596 + -6.000 -0.0700 0.2481 -2.7150 + -5.000 -0.0506 0.2480 -2.6796 + -4.000 -0.0304 0.2479 -2.6465 + -3.000 -0.0098 0.2477 -2.6252 + -2.000 0.0120 0.2476 -2.6251 + -1.000 0.0337 0.2476 -2.6401 + 0.000 0.0553 0.2476 -2.6560 + 1.000 0.0771 0.2476 -2.6724 + 2.000 0.0985 0.2477 -2.6888 + 3.000 0.1195 0.2477 -2.7058 + 4.000 0.1397 0.2478 -2.7228 + 5.000 0.1596 0.2479 -2.7416 + 6.000 0.1759 0.2481 -2.7611 + 7.000 0.1825 0.2484 -2.7757 + 8.000 0.1884 0.2488 -2.7984 + 9.000 0.1956 0.2493 -2.8257 + 10.000 0.2031 0.2499 -2.8541 + 11.000 0.2046 0.2510 -2.8752 + 12.000 0.2099 0.2519 -2.9201 + 13.000 0.2116 0.2533 -2.9584 + 14.000 0.2163 0.2546 -3.0058 + 15.000 0.2196 0.2562 -3.0520 + 16.000 0.2247 0.2577 -3.1066 + 17.000 0.2292 0.2593 -3.1628 + 18.000 0.2333 0.2611 -3.2222 + 19.000 0.2371 0.2630 -3.2817 + 20.000 0.2405 0.2650 -3.3431 + 21.000 0.2436 0.2671 -3.4059 + 22.000 0.2471 0.2691 -3.4734 + 23.000 0.2486 0.2714 -3.5346 + 24.000 0.2498 0.2737 -3.5950 + 25.000 0.2500 0.2762 -3.6519 + 26.000 0.2491 0.2788 -3.7006 + 28.000 0.2447 0.2841 -3.7714 + 29.000 0.2414 0.2871 -3.7894 + 30.000 0.2393 0.2898 -3.8115 + 40.000 0.1968 0.3277 -2.6421 + 50.000 0.1614 0.3683 -2.6421 + 60.000 0.1243 0.4069 -2.6421 + 70.000 0.0837 0.4389 -2.6421 + 80.000 0.0412 0.4607 -2.6421 + 90.000 0.0000 0.4700 -2.6421 + 100.000 -0.0288 0.4607 -2.6421 + 110.000 -0.0586 0.4389 -2.6421 + 120.000 -0.0870 0.4069 -2.6421 + 130.000 -0.1130 0.3683 -2.6421 + 140.000 -0.1378 0.3277 -2.6421 + 150.000 -0.1675 0.2898 -2.6421 + 160.000 -0.1117 0.2589 -2.6421 + 170.000 -0.0558 0.2481 -2.6421 + 180.000 0.0000 0.2481 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 69 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5519 -2.6421 + -170.000 0.0585 0.5519 -2.6421 + -160.000 0.1170 0.5606 -2.6421 + -150.000 0.1756 0.5916 -2.6421 + -140.000 0.1427 0.6298 -2.6421 + -130.000 0.1159 0.6706 -2.6421 + -120.000 0.0885 0.7095 -2.6421 + -110.000 0.0593 0.7419 -2.6421 + -100.000 0.0290 0.7641 -2.6421 + -90.000 0.0000 0.7738 -2.6421 + -80.000 -0.0290 0.7641 -2.6421 + -70.000 -0.0593 0.7419 -2.6421 + -60.000 -0.0885 0.7095 -2.6421 + -50.000 -0.1159 0.6706 -2.6421 + -40.000 -0.1427 0.6298 -2.6421 + -30.000 -0.1756 0.5916 -2.6421 + -20.000 -0.1283 0.5722 -2.6421 + -10.000 -0.1157 0.5529 -2.9039 + -9.000 -0.1073 0.5525 -2.8553 + -8.000 -0.1038 0.5521 -2.8178 + -7.000 -0.0886 0.5519 -2.7624 + -6.000 -0.0700 0.5518 -2.7151 + -5.000 -0.0503 0.5516 -2.6793 + -4.000 -0.0293 0.5515 -2.6454 + -3.000 -0.0081 0.5514 -2.6240 + -1.000 0.0355 0.5513 -2.6411 + 0.000 0.0575 0.5513 -2.6574 + 1.000 0.0791 0.5513 -2.6737 + 2.000 0.1010 0.5513 -2.6904 + 5.000 0.1606 0.5516 -2.7421 + 6.000 0.1748 0.5518 -2.7605 + 7.000 0.1845 0.5521 -2.7774 + 9.000 0.2027 0.5527 -2.8344 + 10.000 0.2063 0.5535 -2.8582 + 11.000 0.2125 0.5542 -2.8904 + 12.000 0.2152 0.5553 -2.9314 + 13.000 0.2203 0.5564 -2.9780 + 14.000 0.2255 0.5576 -3.0290 + 15.000 0.2316 0.5588 -3.0844 + 16.000 0.2365 0.5602 -3.1417 + 17.000 0.2415 0.5617 -3.2040 + 18.000 0.2457 0.5633 -3.2661 + 19.000 0.2499 0.5651 -3.3298 + 20.000 0.2526 0.5671 -3.3930 + 21.000 0.2554 0.5692 -3.4574 + 22.000 0.2578 0.5713 -3.5243 + 23.000 0.2586 0.5737 -3.5852 + 24.000 0.2610 0.5758 -3.6592 + 25.000 0.2612 0.5783 -3.7205 + 26.000 0.2613 0.5806 -3.7805 + 27.000 0.2606 0.5831 -3.8336 + 28.000 0.2592 0.5857 -3.8789 + 29.000 0.2538 0.5888 -3.8910 + 30.000 0.2508 0.5916 -3.9145 + 40.000 0.2038 0.6298 -2.6421 + 50.000 0.1655 0.6706 -2.6421 + 60.000 0.1265 0.7095 -2.6421 + 70.000 0.0847 0.7419 -2.6421 + 80.000 0.0414 0.7641 -2.6421 + 90.000 0.0000 0.7738 -2.6421 + 100.000 -0.0290 0.7641 -2.6421 + 110.000 -0.0593 0.7419 -2.6421 + 120.000 -0.0885 0.7095 -2.6421 + 130.000 -0.1159 0.6706 -2.6421 + 140.000 -0.1427 0.6298 -2.6421 + 150.000 -0.1756 0.5916 -2.6421 + 160.000 -0.1170 0.5606 -2.6421 + 170.000 -0.0585 0.5519 -2.6421 + 180.000 0.0000 0.5519 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 71 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5765 -2.6421 + -170.000 0.0607 0.5765 -2.6421 + -160.000 0.1214 0.5836 -2.6421 + -150.000 0.1821 0.6147 -2.6421 + -140.000 0.1467 0.6531 -2.6421 + -130.000 0.1182 0.6942 -2.6421 + -120.000 0.0898 0.7333 -2.6421 + -110.000 0.0598 0.7660 -2.6421 + -100.000 0.0291 0.7885 -2.6421 + -90.000 0.0000 0.7984 -2.6421 + -80.000 -0.0291 0.7885 -2.6421 + -70.000 -0.0598 0.7660 -2.6421 + -60.000 -0.0898 0.7333 -2.6421 + -50.000 -0.1182 0.6942 -2.6421 + -40.000 -0.1467 0.6531 -2.6421 + -30.000 -0.1821 0.6147 -2.6421 + -20.000 -0.1332 0.5960 -2.6421 + -10.000 -0.1205 0.5773 -2.9139 + -9.000 -0.1129 0.5769 -2.8656 + -8.000 -0.1057 0.5766 -2.8211 + -7.000 -0.0889 0.5765 -2.7629 + -6.000 -0.0703 0.5763 -2.7155 + -5.000 -0.0499 0.5762 -2.6789 + -4.000 -0.0288 0.5761 -2.6448 + -3.000 -0.0075 0.5760 -2.6237 + -2.000 0.0142 0.5759 -2.6263 + -1.000 0.0362 0.5758 -2.6416 + 0.000 0.0582 0.5758 -2.6578 + 1.000 0.0803 0.5758 -2.6745 + 2.000 0.1017 0.5759 -2.6909 + 4.000 0.1425 0.5760 -2.7255 + 5.000 0.1601 0.5762 -2.7421 + 6.000 0.1754 0.5765 -2.7596 + 7.000 0.1869 0.5766 -2.7798 + 8.000 0.1980 0.5768 -2.8088 + 9.000 0.2042 0.5773 -2.8362 + 10.000 0.2107 0.5779 -2.8643 + 11.000 0.2150 0.5787 -2.8954 + 12.000 0.2204 0.5796 -2.9422 + 13.000 0.2252 0.5806 -2.9891 + 14.000 0.2320 0.5817 -3.0455 + 15.000 0.2374 0.5829 -3.1009 + 16.000 0.2428 0.5842 -3.1603 + 17.000 0.2476 0.5857 -3.2240 + 19.000 0.2555 0.5891 -3.3512 + 20.000 0.2614 0.5906 -3.4288 + 21.000 0.2636 0.5927 -3.4927 + 22.000 0.2650 0.5949 -3.5583 + 23.000 0.2658 0.5973 -3.6225 + 24.000 0.2669 0.5996 -3.6923 + 25.000 0.2674 0.6019 -3.7579 + 26.000 0.2670 0.6044 -3.8169 + 27.000 0.2657 0.6069 -3.8686 + 28.000 0.2642 0.6095 -3.9160 + 29.000 0.2624 0.6121 -3.9580 + 30.000 0.2601 0.6147 -3.9922 + 40.000 0.2095 0.6531 -2.6421 + 50.000 0.1689 0.6942 -2.6421 + 60.000 0.1283 0.7333 -2.6421 + 70.000 0.0854 0.7660 -2.6421 + 80.000 0.0416 0.7885 -2.6421 + 90.000 0.0000 0.7984 -2.6421 + 100.000 -0.0291 0.7885 -2.6421 + 110.000 -0.0598 0.7660 -2.6421 + 120.000 -0.0898 0.7333 -2.6421 + 130.000 -0.1182 0.6942 -2.6421 + 140.000 -0.1467 0.6531 -2.6421 + 150.000 -0.1821 0.6147 -2.6421 + 160.000 -0.1214 0.5836 -2.6421 + 170.000 -0.0607 0.5765 -2.6421 + 180.000 0.0000 0.5765 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 62 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5765 -2.6421 + -170.000 0.0727 0.5765 -2.6421 + -160.000 0.1455 0.5865 -2.6421 + -150.000 0.1715 0.6174 -2.6421 + -140.000 0.1402 0.6554 -2.6421 + -130.000 0.1144 0.6961 -2.6421 + -120.000 0.0878 0.7348 -2.6421 + -110.000 0.0589 0.7670 -2.6421 + -100.000 0.0289 0.7890 -2.6421 + -90.000 0.0000 0.7984 -2.6421 + -80.000 -0.0289 0.7890 -2.6421 + -70.000 -0.0589 0.7670 -2.6421 + -60.000 -0.0878 0.7348 -2.6421 + -50.000 -0.1144 0.6961 -2.6421 + -40.000 -0.1402 0.6554 -2.6421 + -30.000 -0.1715 0.6174 -2.6421 + -20.000 -0.1503 0.5939 -2.6421 + -10.000 -0.1223 0.5772 -2.9191 + -9.000 -0.1172 0.5767 -2.8738 + -8.000 -0.1072 0.5765 -2.8237 + -5.000 -0.0498 0.5762 -2.6788 + -4.000 -0.0287 0.5761 -2.6447 + -3.000 -0.0071 0.5760 -2.6217 + -2.000 0.0146 0.5759 -2.6265 + -1.000 0.0366 0.5758 -2.6418 + 0.000 0.0588 0.5758 -2.6582 + 1.000 0.0806 0.5758 -2.6747 + 2.000 0.1019 0.5758 -2.6912 + 3.000 0.1229 0.5759 -2.7082 + 4.000 0.1424 0.5761 -2.7238 + 6.000 0.1765 0.5764 -2.7594 + 7.000 0.1903 0.5765 -2.7828 + 8.000 0.2007 0.5767 -2.8120 + 9.000 0.2064 0.5772 -2.8389 + 11.000 0.2172 0.5785 -2.8998 + 12.000 0.2241 0.5793 -2.9500 + 13.000 0.2312 0.5802 -3.0035 + 15.000 0.2438 0.5823 -3.1186 + 16.000 0.2491 0.5836 -3.1799 + 17.000 0.2546 0.5849 -3.2469 + 18.000 0.2582 0.5865 -3.3095 + 19.000 0.2624 0.5882 -3.3777 + 20.000 0.2652 0.5901 -3.4441 + 22.000 0.2683 0.5945 -3.5738 + 25.000 0.2704 0.6015 -3.7758 + 26.000 0.2702 0.6039 -3.8370 + 30.000 0.2450 0.6174 -3.5690 + 40.000 0.2003 0.6554 -2.6421 + 50.000 0.1634 0.6961 -2.6421 + 60.000 0.1254 0.7348 -2.6421 + 70.000 0.0842 0.7670 -2.6421 + 80.000 0.0413 0.7890 -2.6421 + 90.000 0.0000 0.7984 -2.6421 + 100.000 -0.0289 0.7890 -2.6421 + 110.000 -0.0589 0.7670 -2.6421 + 120.000 -0.0878 0.7348 -2.6421 + 130.000 -0.1144 0.6961 -2.6421 + 140.000 -0.1402 0.6554 -2.6421 + 150.000 -0.1715 0.6174 -2.6421 + 160.000 -0.1455 0.5865 -2.6421 + 170.000 -0.0727 0.5765 -2.6421 + 180.000 0.0000 0.5765 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 67 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5765 -2.6421 + -170.000 0.0628 0.5765 -2.6421 + -160.000 0.1256 0.5819 -2.6421 + -150.000 0.1883 0.6132 -2.6421 + -140.000 0.1505 0.6517 -2.6421 + -130.000 0.1204 0.6930 -2.6421 + -120.000 0.0910 0.7324 -2.6421 + -110.000 0.0603 0.7653 -2.6421 + -100.000 0.0292 0.7882 -2.6421 + -90.000 0.0000 0.7984 -2.6421 + -80.000 -0.0292 0.7882 -2.6421 + -70.000 -0.0603 0.7653 -2.6421 + -60.000 -0.0910 0.7324 -2.6421 + -50.000 -0.1204 0.6930 -2.6421 + -40.000 -0.1505 0.6517 -2.6421 + -30.000 -0.1883 0.6132 -2.6421 + -20.000 -0.1368 0.5952 -2.6421 + -10.000 -0.1219 0.5772 -2.9149 + -9.000 -0.1226 0.5766 -2.8845 + -8.000 -0.1085 0.5765 -2.8260 + -6.000 -0.0705 0.5762 -2.7163 + -4.000 -0.0286 0.5760 -2.6446 + -3.000 -0.0069 0.5760 -2.6219 + -2.000 0.0149 0.5759 -2.6267 + -1.000 0.0370 0.5758 -2.6420 + 0.000 0.0591 0.5758 -2.6583 + 1.000 0.0807 0.5758 -2.6748 + 2.000 0.1020 0.5758 -2.6915 + 4.000 0.1420 0.5761 -2.7229 + 5.000 0.1598 0.5763 -2.7401 + 6.000 0.1771 0.5764 -2.7606 + 7.000 0.1940 0.5765 -2.7861 + 8.000 0.2019 0.5767 -2.8134 + 9.000 0.2093 0.5771 -2.8426 + 10.000 0.2125 0.5778 -2.8638 + 11.000 0.2210 0.5783 -2.9073 + 12.000 0.2274 0.5791 -2.9570 + 13.000 0.2343 0.5800 -3.0108 + 14.000 0.2412 0.5809 -3.0686 + 16.000 0.2529 0.5832 -3.1916 + 17.000 0.2573 0.5847 -3.2559 + 18.000 0.2618 0.5862 -3.3219 + 19.000 0.2653 0.5879 -3.3887 + 20.000 0.2679 0.5898 -3.4550 + 21.000 0.2693 0.5919 -3.5174 + 24.000 0.2762 0.5982 -3.7432 + 25.000 0.2769 0.6005 -3.8133 + 26.000 0.2769 0.6029 -3.8791 + 27.000 0.2762 0.6053 -3.9388 + 28.000 0.2745 0.6078 -3.9894 + 29.000 0.2719 0.6105 -4.0313 + 30.000 0.2691 0.6132 -4.0663 + 40.000 0.2150 0.6517 -2.6421 + 50.000 0.1721 0.6930 -2.6421 + 60.000 0.1300 0.7324 -2.6421 + 70.000 0.0862 0.7653 -2.6421 + 80.000 0.0418 0.7882 -2.6421 + 90.000 0.0000 0.7984 -2.6421 + 100.000 -0.0292 0.7882 -2.6421 + 110.000 -0.0603 0.7653 -2.6421 + 120.000 -0.0910 0.7324 -2.6421 + 130.000 -0.1204 0.6930 -2.6421 + 140.000 -0.1505 0.6517 -2.6421 + 150.000 -0.1883 0.6132 -2.6421 + 160.000 -0.1256 0.5819 -2.6421 + 170.000 -0.0628 0.5765 -2.6421 + 180.000 0.0000 0.5765 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 68 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5765 -2.6421 + -170.000 0.0661 0.5765 -2.6421 + -160.000 0.1322 0.5825 -2.6421 + -150.000 0.1869 0.6137 -2.6421 + -140.000 0.1496 0.6521 -2.6421 + -130.000 0.1199 0.6934 -2.6421 + -120.000 0.0907 0.7327 -2.6421 + -110.000 0.0602 0.7655 -2.6421 + -100.000 0.0292 0.7883 -2.6421 + -90.000 0.0000 0.7984 -2.6421 + -80.000 -0.0292 0.7883 -2.6421 + -70.000 -0.0602 0.7655 -2.6421 + -60.000 -0.0907 0.7327 -2.6421 + -50.000 -0.1199 0.6934 -2.6421 + -40.000 -0.1496 0.6521 -2.6421 + -30.000 -0.1869 0.6137 -2.6421 + -20.000 -0.1421 0.5945 -2.6421 + -10.000 -0.1243 0.5770 -2.9202 + -7.000 -0.0904 0.5763 -2.7654 + -6.000 -0.0703 0.5762 -2.7143 + -5.000 -0.0498 0.5761 -2.6788 + -4.000 -0.0285 0.5760 -2.6445 + -3.000 -0.0068 0.5760 -2.6224 + -2.000 0.0151 0.5759 -2.6268 + -1.000 0.0372 0.5758 -2.6421 + 0.000 0.0592 0.5758 -2.6584 + 1.000 0.0809 0.5758 -2.6749 + 2.000 0.1021 0.5758 -2.6908 + 3.000 0.1228 0.5759 -2.7064 + 4.000 0.1421 0.5761 -2.7236 + 5.000 0.1599 0.5762 -2.7406 + 6.000 0.1783 0.5763 -2.7617 + 7.000 0.1954 0.5765 -2.7874 + 8.000 0.2032 0.5767 -2.8148 + 10.000 0.2152 0.5776 -2.8684 + 11.000 0.2233 0.5782 -2.9119 + 12.000 0.2310 0.5789 -2.9648 + 13.000 0.2383 0.5797 -3.0203 + 14.000 0.2437 0.5807 -3.0748 + 15.000 0.2498 0.5818 -3.1353 + 17.000 0.2600 0.5844 -3.2646 + 18.000 0.2642 0.5859 -3.3301 + 19.000 0.2676 0.5876 -3.3976 + 20.000 0.2694 0.5896 -3.4608 + 22.000 0.2766 0.5933 -3.6125 + 23.000 0.2780 0.5955 -3.6858 + 24.000 0.2791 0.5978 -3.7589 + 25.000 0.2796 0.6001 -3.8288 + 26.000 0.2794 0.6025 -3.8941 + 27.000 0.2781 0.6050 -3.9509 + 28.000 0.2762 0.6076 -4.0012 + 29.000 0.2739 0.6102 -4.0458 + 30.000 0.2671 0.6137 -3.9598 + 40.000 0.2137 0.6521 -2.6421 + 50.000 0.1713 0.6934 -2.6421 + 60.000 0.1296 0.7327 -2.6421 + 70.000 0.0860 0.7655 -2.6421 + 80.000 0.0417 0.7883 -2.6421 + 90.000 0.0000 0.7984 -2.6421 + 100.000 -0.0292 0.7883 -2.6421 + 110.000 -0.0602 0.7655 -2.6421 + 120.000 -0.0907 0.7327 -2.6421 + 130.000 -0.1199 0.6934 -2.6421 + 140.000 -0.1496 0.6521 -2.6421 + 150.000 -0.1869 0.6137 -2.6421 + 160.000 -0.1322 0.5825 -2.6421 + 170.000 -0.0661 0.5765 -2.6421 + 180.000 0.0000 0.5765 -2.6421 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.000 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 64 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.000 0.0000 0.5765 -2.6421 + -170.000 0.0637 0.5765 -2.6421 + -160.000 0.1274 0.5812 -2.6421 + -150.000 0.1912 0.6125 -2.6421 + -140.000 0.1522 0.6511 -2.6421 + -130.000 0.1215 0.6925 -2.6421 + -120.000 0.0915 0.7320 -2.6421 + -110.000 0.0606 0.7651 -2.6421 + -100.000 0.0293 0.7880 -2.6421 + -90.000 0.0000 0.7984 -2.6421 + -80.000 -0.0293 0.7880 -2.6421 + -70.000 -0.0606 0.7651 -2.6421 + -60.000 -0.0915 0.7320 -2.6421 + -50.000 -0.1215 0.6925 -2.6421 + -40.000 -0.1522 0.6511 -2.6421 + -30.000 -0.1912 0.6125 -2.6421 + -20.000 -0.1399 0.5947 -2.6421 + -10.000 -0.1267 0.5770 -2.9252 + -9.000 -0.1232 0.5766 -2.8832 + -8.000 -0.1096 0.5764 -2.8283 + -6.000 -0.0703 0.5762 -2.7138 + -5.000 -0.0497 0.5761 -2.6788 + -4.000 -0.0285 0.5760 -2.6445 + -3.000 -0.0066 0.5759 -2.6225 + -2.000 0.0153 0.5759 -2.6269 + -1.000 0.0373 0.5758 -2.6422 + 0.000 0.0594 0.5758 -2.6585 + 1.000 0.0807 0.5758 -2.6749 + 2.000 0.1021 0.5758 -2.6907 + 3.000 0.1226 0.5759 -2.7061 + 5.000 0.1605 0.5762 -2.7412 + 6.000 0.1789 0.5763 -2.7623 + 7.000 0.1967 0.5764 -2.7887 + 8.000 0.2039 0.5767 -2.8156 + 11.000 0.2254 0.5781 -2.9160 + 12.000 0.2331 0.5788 -2.9692 + 14.000 0.2475 0.5804 -3.0842 + 16.000 0.2573 0.5828 -3.2053 + 18.000 0.2662 0.5857 -3.3369 + 21.000 0.2770 0.5909 -3.5511 + 22.000 0.2788 0.5930 -3.6222 + 23.000 0.2804 0.5952 -3.6977 + 24.000 0.2812 0.5975 -3.7705 + 25.000 0.2815 0.5998 -3.8400 + 26.000 0.2807 0.6023 -3.9023 + 27.000 0.2794 0.6048 -3.9597 + 28.000 0.2777 0.6073 -4.0122 + 29.000 0.2757 0.6099 -4.0591 + 30.000 0.2731 0.6125 -4.0985 + 40.000 0.2174 0.6511 -2.6421 + 50.000 0.1735 0.6925 -2.6421 + 60.000 0.1308 0.7320 -2.6421 + 70.000 0.0865 0.7651 -2.6421 + 80.000 0.0419 0.7880 -2.6421 + 90.000 0.0000 0.7984 -2.6421 + 100.000 -0.0293 0.7880 -2.6421 + 110.000 -0.0606 0.7651 -2.6421 + 120.000 -0.0915 0.7320 -2.6421 + 130.000 -0.1215 0.6925 -2.6421 + 140.000 -0.1522 0.6511 -2.6421 + 150.000 -0.1912 0.6125 -2.6421 + 160.000 -0.1274 0.5812 -2.6421 + 170.000 -0.0637 0.5765 -2.6421 + 180.000 0.0000 0.5765 -2.6421 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864_coords.txt new file mode 100644 index 000000000..8a9382a1d --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_0864_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_0864 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.071897 + 0.027091 0.142126 + 0.060263 0.208964 + 0.105430 0.270115 + 0.161359 0.323830 + 0.226526 0.368470 + 0.299152 0.402651 + 0.377257 0.425156 + 0.458710 0.435236 + 0.541290 0.432906 + 0.622743 0.418526 + 0.700848 0.392758 + 0.773474 0.356678 + 0.838641 0.311513 + 0.894570 0.258619 + 0.939737 0.199450 + 0.972909 0.135539 + 0.993181 0.068594 + 1.000000 0.000000 + 0.993181 -0.068161 + 0.972909 -0.134227 + 0.939737 -0.197056 + 0.894570 -0.255087 + 0.838641 -0.306904 + 0.773474 -0.351143 + 0.700848 -0.386540 + 0.622743 -0.411920 + 0.541290 -0.426222 + 0.458710 -0.428757 + 0.377257 -0.419101 + 0.299152 -0.397194 + 0.226526 -0.363795 + 0.161359 -0.320050 + 0.105430 -0.267316 + 0.060263 -0.207125 + 0.027091 -0.141176 + 0.006819 -0.071509 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000.dat new file mode 100644 index 000000000..7c3e84713 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000.dat @@ -0,0 +1,108 @@ +! ------------ AirfoilInfo v1.01.x Input File ---------------------------------- +! NACA6_1000 airfoil, data based on values used for the design of the RM1 tidal current turbine. +! line +! line +! ------------------------------------------------------------------------------ +"default" InterpOrd ! Interpolation order to use for quasi-steady table lookup {1=linear; 3=cubic spline; "default"} [default=1] + 1.0 NonDimArea ! The non-dimensional area of the airfoil (area/chord^2) (set to 1.0 if unsure or unneeded) +@"NACA6_1000_coords.txt" NumCoords ! The number of coordinates in the airfoil shape file. Set to zero if coordinates not included. +"unused" BL_file ! The file name including the boundary layer characteristics of the profile. Ignored if the aeroacoustic module is not called. + 7 NumTabs ! Number of airfoil tables in this file. +! ------------------------------------------------------------------------------ +! data for table 1 +! ------------------------------------------------------------------------------ + 2.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.3 -3.0 + 0.00 0.0 0.3 -3.0 + 180.00 0.0 0.3 -3.0 +! ------------------------------------------------------------------------------ +! data for table 2 +! ------------------------------------------------------------------------------ + 4.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.67 -3.0 + 0.00 0.0 0.67 -3.0 + 180.00 0.0 0.67 -3.0 +! ------------------------------------------------------------------------------ +! data for table 3 +! ------------------------------------------------------------------------------ + 6.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.7 -3.0 + 0.00 0.0 0.7 -3.0 + 180.00 0.0 0.7 -3.0 +! ------------------------------------------------------------------------------ +! data for table 4 +! ------------------------------------------------------------------------------ + 8.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.7 -3.0 + 0.00 0.0 0.7 -3.0 + 180.00 0.0 0.7 -3.0 +! ------------------------------------------------------------------------------ +! data for table 5 +! ------------------------------------------------------------------------------ + 10.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.7 -3.0 + 0.00 0.0 0.7 -3.0 + 180.00 0.0 0.7 -3.0 +! ------------------------------------------------------------------------------ +! data for table 6 +! ------------------------------------------------------------------------------ + 12.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.7 -3.0 + 0.00 0.0 0.7 -3.0 + 180.00 0.0 0.7 -3.0 +! ------------------------------------------------------------------------------ +! data for table 7 +! ------------------------------------------------------------------------------ + 14.0 Re ! Reynolds number in millions + 0 UserProp ! User property (control) setting +False InclUAdata ! Is unsteady aerodynamics data included in this table? If TRUE, then include 30 UA coefficients below this line +!........................................ +! Table of aerodynamics coefficients + 3 NumAlf ! Number of data lines in the following table +! Alpha Cl Cd Cpmin +! (deg) (-) (-) (-) + -180.00 0.0 0.7 -3.0 + 0.00 0.0 0.7 -3.0 + 180.00 0.0 0.7 -3.0 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000_coords.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000_coords.txt new file mode 100644 index 000000000..e9876cf7d --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/Airfoils/NACA6_1000_coords.txt @@ -0,0 +1,47 @@ + 40 NumCoords ! The number of coordinates in the airfoil shape file (including an extra coordinate for airfoil reference). Set to zero if coordinates not included. +! ......... x-y coordinates are next if NumCoords > 0 ............. +! x-y coordinate of airfoil reference +! x/c y/c + 0.25 0 +! coordinates of airfoil shape +! NACA6_1000 Airfoil +! x/c y/c + 0.000000 0.000000 + 0.006819 0.082442 + 0.027091 0.162359 + 0.060263 0.237976 + 0.105430 0.307106 + 0.161359 0.367865 + 0.226526 0.418583 + 0.299152 0.457883 + 0.377257 0.484712 + 0.458710 0.498283 + 0.541290 0.498283 + 0.622743 0.484712 + 0.700848 0.457883 + 0.773474 0.418583 + 0.838641 0.367865 + 0.894570 0.307106 + 0.939737 0.237976 + 0.972909 0.162359 + 0.993181 0.082442 + 1.000000 0.000000 + 0.993181 -0.082442 + 0.972909 -0.162359 + 0.939737 -0.237976 + 0.894570 -0.307106 + 0.838641 -0.367865 + 0.773474 -0.418583 + 0.700848 -0.457883 + 0.622743 -0.484712 + 0.541290 -0.498283 + 0.458710 -0.498283 + 0.377257 -0.484712 + 0.299152 -0.457883 + 0.226526 -0.418583 + 0.161359 -0.367865 + 0.105430 -0.307106 + 0.060263 -0.237976 + 0.027091 -0.162359 + 0.006819 -0.082442 + 0.000000 0.000000 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_AeroDyn15_Blade.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_AeroDyn15_Blade.dat new file mode 100644 index 000000000..29c7548a6 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_AeroDyn15_Blade.dat @@ -0,0 +1,38 @@ +------- AERODYN v15.00.* BLADE DEFINITION INPUT FILE ------------------------------------- +Floating MHK turbine hydrodynamic blade input properties, based on the RM1 tidal current rotor +====== Blade Properties ================================================================= +32 NumBlNds - Number of blade nodes used in the analysis (-) +BlSpn BlCrvAC BlSwpAC BlCrvAng BlTwist BlChord BlAFID BlCb BlCenBn BlCenBt +(m) (m) (m) (deg) (deg) (m) (-) (-) (m) (m) +0.000 0.00 0.00 0.00 12.86 0.800 1 0.9956 0.0000 0.2000 +0.150 0.00 0.00 0.00 12.86 0.800 1 0.9956 0.0000 0.2000 +0.450 0.00 0.00 0.00 12.86 0.894 2 0.8572 0.0023 0.2202 +0.750 0.00 0.00 0.00 12.86 1.118 3 0.6118 0.0082 0.2637 +1.050 0.00 0.00 0.00 12.86 1.386 4 0.4145 0.0153 0.3027 +1.350 0.00 0.00 0.00 12.86 1.610 5 0.2873 0.0217 0.3131 +1.650 0.00 0.00 0.00 12.86 1.704 6 0.2287 0.0250 0.2973 +1.950 0.00 0.00 0.00 11.54 1.662 7 0.2099 0.0250 0.2753 +2.250 0.00 0.00 0.00 10.44 1.619 8 0.1966 0.0247 0.2565 +2.550 0.00 0.00 0.00 9.50 1.577 9 0.1870 0.0245 0.2386 +2.850 0.00 0.00 0.00 8.71 1.534 9 0.1870 0.0238 0.2321 +3.150 0.00 0.00 0.00 8.02 1.492 9 0.1870 0.0232 0.2258 +3.450 0.00 0.00 0.00 7.43 1.450 9 0.1870 0.0225 0.2194 +3.750 0.00 0.00 0.00 6.91 1.407 9 0.1870 0.0218 0.2129 +4.050 0.00 0.00 0.00 6.45 1.365 9 0.1870 0.0212 0.2066 +4.350 0.00 0.00 0.00 6.04 1.322 9 0.1870 0.0205 0.2001 +4.650 0.00 0.00 0.00 5.68 1.279 9 0.1870 0.0198 0.1935 +4.950 0.00 0.00 0.00 5.35 1.235 9 0.1870 0.0192 0.1869 +5.250 0.00 0.00 0.00 5.05 1.192 9 0.1870 0.0185 0.1804 +5.550 0.00 0.00 0.00 4.77 1.148 9 0.1870 0.0178 0.1737 +5.850 0.00 0.00 0.00 4.51 1.103 9 0.1870 0.0171 0.1669 +6.150 0.00 0.00 0.00 4.26 1.058 9 0.1870 0.0164 0.1601 +6.450 0.00 0.00 0.00 4.03 1.012 9 0.1870 0.0157 0.1531 +6.750 0.00 0.00 0.00 3.80 0.966 9 0.1870 0.0150 0.1462 +7.050 0.00 0.00 0.00 3.57 0.920 9 0.1870 0.0143 0.1392 +7.350 0.00 0.00 0.00 3.35 0.872 9 0.1870 0.0135 0.1320 +7.650 0.00 0.00 0.00 3.13 0.824 9 0.1870 0.0128 0.1247 +7.950 0.00 0.00 0.00 2.90 0.776 9 0.1870 0.0120 0.1174 +8.250 0.00 0.00 0.00 2.67 0.726 9 0.1870 0.0113 0.1099 +8.550 0.00 0.00 0.00 2.43 0.676 9 0.1870 0.0105 0.1023 +8.850 0.00 0.00 0.00 2.18 0.626 9 0.1870 0.0097 0.0947 +9.000 0.00 0.00 0.00 2.18 0.626 9 0.1870 0.0097 0.0947 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Cp_Ct_Cq.txt b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Cp_Ct_Cq.txt new file mode 100644 index 000000000..da7445bf5 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Cp_Ct_Cq.txt @@ -0,0 +1,99 @@ +# ----- Rotor performance tables for the MHK_RM1_Floating wind turbine ----- +# ------------ Written on Apr-12-23 using the ROSCO toolbox ------------ + +# Pitch angle vector, 36 entries - x axis (matrix columns) (deg) +-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0 18.0 19.0 20.0 21.0 22.0 23.0 24.0 25.0 26.0 27.0 28.0 29.0 30.0 +# TSR vector, 26 entries - y axis (matrix rows) (-) +2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 +# Wind speed vector - z axis (m/s) +2.0 + +# Power coefficient + +0.074944 0.079726 0.084378 0.088881 0.093217 0.097364 0.101301 0.105005 0.108455 0.111627 0.114499 0.117048 0.119254 0.121097 0.122559 0.123624 0.124278 0.124507 0.124300 0.123645 0.122533 0.120953 0.118899 0.116363 0.113340 0.109825 0.105817 0.101315 0.096316 0.090822 0.084833 0.078355 0.071398 0.063985 0.056154 0.047964 +0.130952 0.137109 0.142870 0.148203 0.153074 0.157453 0.161312 0.164628 0.167372 0.169517 0.171039 0.171924 0.172157 0.171726 0.170619 0.168827 0.166341 0.163154 0.159260 0.154654 0.149334 0.143297 0.136543 0.129071 0.120881 0.111975 0.102356 0.092036 0.081046 0.069441 0.057307 0.044758 0.031932 0.018970 0.006005 -0.006847 +0.195638 0.202139 0.207875 0.212814 0.216927 0.220183 0.222553 0.224026 0.224594 0.224263 0.222996 0.220777 0.217599 0.213457 0.208343 0.202253 0.195185 0.187134 0.178099 0.168078 0.157067 0.145065 0.132070 0.118086 0.103136 0.087277 0.070607 0.053271 0.035454 0.017370 -0.000779 -0.018814 -0.036579 -0.053937 -0.070772 -0.086985 +0.262033 0.267729 0.272255 0.275602 0.277770 0.278772 0.278626 0.277286 0.274722 0.270950 0.265956 0.259691 0.252150 0.243325 0.233208 0.221791 0.209065 0.195021 0.179649 0.162940 0.144884 0.125492 0.104814 0.082960 0.060099 0.036466 0.012350 -0.011952 -0.036178 -0.060102 -0.083527 -0.106282 -0.128227 -0.149248 -0.169259 -0.188203 +0.322926 0.327173 0.329927 0.331189 0.330966 0.329268 0.326118 0.321556 0.315485 0.307866 0.298685 0.287857 0.275361 0.261178 0.245286 0.227663 0.208287 0.187132 0.164179 0.139430 0.112946 0.084868 0.055418 0.024908 -0.006270 -0.037714 -0.069075 -0.100049 -0.130376 -0.159832 -0.188230 -0.215422 -0.241296 -0.265780 -0.288840 -0.310479 +0.372380 0.375319 0.376595 0.376163 0.373941 0.369893 0.364015 0.356336 0.346830 0.335323 0.321769 0.306044 0.288110 0.267928 0.245459 0.220662 0.193502 0.163952 0.132049 0.097926 0.061824 0.024095 -0.014788 -0.054287 -0.093913 -0.133250 -0.171936 -0.209660 -0.246161 -0.281224 -0.314687 -0.346436 -0.376411 -0.404602 -0.431047 -0.455838 +0.405840 0.408981 0.410112 0.409159 0.406069 0.400647 0.392786 0.382501 0.369826 0.354510 0.336432 0.315446 0.291501 0.264544 0.234524 0.201389 0.165121 0.125794 0.083613 0.038921 -0.007794 -0.055894 -0.104692 -0.153592 -0.202082 -0.249721 -0.296127 -0.340984 -0.384037 -0.425096 -0.464037 -0.500803 -0.535402 -0.567911 -0.598467 -0.627270 +0.419815 0.426133 0.429721 0.430398 0.428020 0.422533 0.413665 0.401397 0.385844 0.366737 0.343872 0.317114 0.286411 0.251710 0.212957 0.170136 0.123352 0.072872 0.019131 -0.037250 -0.095463 -0.154643 -0.214052 -0.273053 -0.331092 -0.387697 -0.442469 -0.495093 -0.545333 -0.593039 -0.638145 -0.680670 -0.720718 -0.758469 -0.794184 -0.828187 +0.416203 0.426901 0.435700 0.440694 0.441121 0.436927 0.428055 0.414373 0.396136 0.373105 0.345026 0.311802 0.273402 0.229785 0.180939 0.126984 0.068233 0.005200 -0.061381 -0.130534 -0.201181 -0.272387 -0.343354 -0.413383 -0.481873 -0.548318 -0.612312 -0.673550 -0.731832 -0.787064 -0.839260 -0.888535 -0.935107 -0.979287 -1.021467 -1.062112 +0.405435 0.418564 0.430778 0.441391 0.446693 0.445250 0.437234 0.422572 0.401692 0.374435 0.340533 0.299965 0.252737 0.198854 0.138441 0.071834 -0.000397 -0.077426 -0.158137 -0.241210 -0.325451 -0.409848 -0.493517 -0.575688 -0.655711 -0.733053 -0.807309 -0.878198 -0.945571 -1.009410 -1.069825 -1.127051 -1.181434 -1.233429 -1.283586 -1.332529 +0.390503 0.406666 0.421413 0.435076 0.446065 0.448699 0.442235 0.426873 0.403229 0.371269 0.330765 0.281798 0.224440 0.158821 0.085281 0.004419 -0.082876 -0.175390 -0.271577 -0.369910 -0.469102 -0.568030 -0.665710 -0.761300 -0.854102 -0.943564 -1.029289 -1.111036 -1.188722 -1.262423 -1.332361 -1.398905 -1.462560 -1.523949 -1.583795 -1.642861 +0.371667 0.391683 0.409587 0.425790 0.440553 0.448186 0.443843 0.427902 0.401216 0.363917 0.315877 0.257305 0.188389 0.109467 0.021145 -0.075662 -0.179678 -0.289215 -0.402371 -0.517492 -0.633155 -0.748106 -0.861259 -0.971696 -1.078673 -1.181626 -1.280179 -1.374141 -1.463512 -1.548474 -1.629388 -1.706786 -1.781347 -1.853884 -1.925308 -1.996461 +0.348976 0.373677 0.395430 0.414664 0.431984 0.444452 0.442628 0.426075 0.395932 0.352522 0.295876 0.226363 0.144361 0.050473 -0.054383 -0.168912 -0.291369 -0.419564 -0.551360 -0.684950 -0.818747 -0.951358 -1.081586 -1.208436 -1.331122 -1.449075 -1.561948 -1.669615 -1.772174 -1.869941 -1.963438 -2.053377 -2.140642 -2.226257 -2.311298 -2.396626 +0.322447 0.352671 0.378963 0.401764 0.421785 0.438064 0.438990 0.421654 0.387518 0.337104 0.270664 0.188770 0.092059 -0.018557 -0.141791 -0.275900 -0.418590 -0.567224 -0.719482 -0.873361 -1.027094 -1.179135 -1.328171 -1.473129 -1.613185 -1.747771 -1.876578 -1.999566 -2.116951 -2.229204 -2.337032 -2.441358 -2.543286 -2.644065 -2.744885 -2.846626 +0.292104 0.328674 0.360194 0.387092 0.410155 0.429464 0.433189 0.414787 0.376018 0.317600 0.240072 0.144265 0.031127 -0.098072 -0.241618 -0.397243 -0.562049 -0.733059 -0.907740 -1.083862 -1.259459 -1.432826 -1.602527 -1.767409 -1.926613 -2.079580 -2.226059 -2.366108 -2.500086 -2.628644 -2.752697 -2.873398 -2.992099 -3.110271 -3.229166 -3.349696 +0.257934 0.301688 0.339118 0.370637 0.397092 0.419035 0.425380 0.405542 0.361401 0.293886 0.203884 0.092545 -0.038828 -0.188556 -0.354435 -0.533590 -0.722527 -0.917988 -1.117178 -1.317621 -1.517135 -1.713843 -1.906181 -2.092918 -2.273163 -2.446378 -2.612385 -2.771358 -2.923825 -3.070638 -3.212946 -3.352157 -3.489890 -3.627813 -3.767210 -3.909048 +0.219932 0.271699 0.315725 0.352385 0.382573 0.407064 0.415649 0.393924 0.343586 0.265797 0.161855 0.033279 -0.118226 -0.290520 -0.480838 -0.685625 -0.900846 -1.122958 -1.348868 -1.575829 -1.801429 -2.023608 -2.240672 -2.451307 -2.654600 -2.850044 -3.037549 -3.217434 -3.390412 -3.557560 -3.720288 -3.880284 -4.039448 -4.199603 -4.362066 -4.527873 +0.178135 0.238694 0.290002 0.332317 0.366567 0.393662 0.404032 0.379897 0.322460 0.233142 0.113714 -0.033886 -0.207507 -0.404491 -0.621444 -0.854066 -1.097853 -1.348937 -1.603896 -1.859687 -2.113658 -2.363555 -2.607543 -2.844231 -3.072689 -3.292456 -3.503551 -3.706453 -3.902085 -4.091779 -4.277226 -4.460416 -4.643543 -4.828535 -5.016766 -5.209342 +0.132703 0.202667 0.261930 0.310408 0.349034 0.378811 0.390525 0.363396 0.297884 0.195706 0.059174 -0.109316 -0.307124 -0.531014 -0.776881 -1.039657 -1.314414 -1.596907 -1.883356 -2.170409 -2.455151 -2.735120 -3.008340 -3.273345 -3.529196 -3.775497 -4.012387 -4.240529 -4.461083 -4.675657 -4.886250 -5.095180 -5.304925 -5.517492 -5.734327 -5.956614 +0.084224 0.163639 0.231485 0.286625 0.329931 0.362460 0.375097 0.344332 0.269698 0.153266 -0.002060 -0.193389 -0.417541 -0.670640 -0.947787 -1.243159 -1.551402 -1.867855 -2.188355 -2.509214 -2.827235 -3.139741 -3.444611 -3.740306 -4.025892 -4.301046 -4.566051 -4.821776 -5.069639 -5.311551 -5.549845 -5.787192 -6.026332 -6.269342 -6.517755 -6.772836 +0.034952 0.121693 0.198644 0.260932 0.309208 0.344544 0.357698 0.322599 0.237730 0.105587 -0.070294 -0.286489 -0.539230 -0.823928 -1.134814 -1.465339 -1.809699 -2.162779 -2.520003 -2.877323 -3.231243 -3.578859 -3.917904 -4.246774 -4.564545 -4.870985 -5.166539 -5.452304 -5.729981 -6.001811 -6.270488 -6.539057 -6.810494 -7.086946 -7.370046 -7.661142 +-0.011115 0.077127 0.163389 0.233290 0.286813 0.324991 0.338262 0.298075 0.201799 0.052429 -0.145838 -0.389008 -0.672666 -0.991441 -1.338622 -1.706972 -2.090192 -2.482680 -2.879417 -3.275962 -3.668506 -4.053914 -4.429770 -4.794407 -5.146925 -5.487194 -5.815843 -6.134218 -6.444332 -6.748781 -7.050649 -7.353371 -7.660131 -7.973156 -8.294185 -8.624661 +-0.050911 0.031047 0.125717 0.203658 0.262692 0.303728 0.316707 0.270625 0.161721 -0.006451 -0.229008 -0.501339 -0.818328 -1.173745 -1.559880 -1.968838 -2.393776 -2.828568 -3.267710 -3.706353 -4.140357 -4.566348 -4.981760 -5.384866 -5.774799 -6.151551 -6.515951 -6.869621 -7.214911 -7.554801 -7.892791 -8.232723 -8.577956 -8.930816 -9.293154 -9.666511 +-0.083826 -0.014210 0.085661 0.171992 0.236785 0.280676 0.292937 0.240111 0.117308 -0.071300 -0.320120 -0.623879 -0.976697 -1.371406 -1.799263 -2.251722 -2.721350 -3.201448 -3.685999 -4.169721 -4.648130 -5.117604 -5.575423 -6.019808 -6.449935 -6.865932 -7.268850 -7.660612 -8.043932 -8.422205 -8.799374 -9.179693 -9.566677 -9.962766 -10.369925 -10.789805 +-0.112321 -0.056414 0.043373 0.138247 0.209032 0.255752 0.266847 0.206391 0.068370 -0.142368 -0.419492 -0.757024 -1.148255 -1.584993 -2.057449 -2.556414 -3.073813 -3.602329 -4.135402 -4.667292 -5.193160 -5.709124 -6.212312 -6.700894 -7.174100 -7.632211 -8.076523 -8.509287 -8.933609 -9.353325 -9.772854 -10.196858 -10.628998 -11.071839 -11.527466 -11.997652 +-0.138289 -0.094091 -0.000597 0.102378 0.179367 0.228869 0.238326 0.169319 0.014717 -0.219903 -0.527445 -0.901172 -1.333484 -1.815079 -2.335120 -2.883708 -3.452066 -4.032220 -4.617034 -5.200293 -5.776782 -6.342352 -6.893976 -7.429778 -7.949056 -8.452259 -8.940954 -9.417741 -9.886149 -10.350489 -10.815681 -11.286791 -11.767617 -12.260864 -12.768741 -13.293158 + + +# Thrust coefficient + +0.173936 0.174545 0.175105 0.175604 0.176027 0.176357 0.176576 0.176665 0.176604 0.176373 0.175951 0.175317 0.174451 0.173333 0.171944 0.170267 0.168285 0.165982 0.163343 0.160355 0.157004 0.153277 0.149165 0.144659 0.139751 0.134439 0.128718 0.122589 0.116054 0.109113 0.101773 0.094042 0.085936 0.077488 0.068744 0.059773 +0.244110 0.244807 0.245270 0.245468 0.245368 0.244940 0.244155 0.242988 0.241410 0.239388 0.236896 0.233914 0.230421 0.226400 0.221834 0.216710 0.211014 0.204738 0.197875 0.190421 0.182376 0.173742 0.164522 0.154724 0.144354 0.133423 0.121945 0.109944 0.097467 0.084590 0.071416 0.058076 0.044718 0.031490 0.018521 0.005920 +0.329058 0.328652 0.327699 0.326163 0.324010 0.321203 0.317708 0.313503 0.308574 0.302916 0.296490 0.289274 0.281258 0.272437 0.262806 0.252366 0.241123 0.229082 0.216256 0.202654 0.188292 0.173184 0.157347 0.140807 0.123614 0.105857 0.087669 0.069222 0.050725 0.032400 0.014449 -0.002964 -0.019698 -0.035638 -0.050689 -0.064777 +0.423724 0.420631 0.416601 0.411609 0.405640 0.398694 0.390781 0.381862 0.371916 0.360960 0.348987 0.335961 0.321889 0.306782 0.290656 0.273525 0.255410 0.236329 0.216303 0.195352 0.173503 0.150806 0.127362 0.103332 0.078936 0.054443 0.030163 0.006389 -0.016641 -0.038731 -0.059724 -0.079492 -0.097938 -0.114987 -0.130589 -0.144718 +0.522708 0.515320 0.506632 0.496639 0.485349 0.472781 0.458970 0.443976 0.427746 0.410278 0.391592 0.371635 0.350431 0.328001 0.304369 0.279560 0.253599 0.226508 0.198319 0.169094 0.138969 0.108161 0.076963 0.045733 0.014882 -0.015203 -0.044217 -0.071919 -0.098118 -0.122660 -0.145431 -0.166347 -0.185356 -0.202436 -0.217593 -0.230865 +0.620986 0.607920 0.593333 0.577234 0.559606 0.540466 0.519860 0.497874 0.474551 0.449800 0.423636 0.395993 0.366897 0.336377 0.304459 0.271170 0.236538 0.200616 0.163534 0.125537 0.086962 0.048236 0.009863 -0.027633 -0.063829 -0.098403 -0.131103 -0.161729 -0.190133 -0.216204 -0.239873 -0.261109 -0.279917 -0.296336 -0.310444 -0.322349 +0.714037 0.694660 0.673585 0.650833 0.626441 0.600346 0.572544 0.543137 0.512256 0.479758 0.445598 0.409704 0.372107 0.332841 0.291934 0.249425 0.205397 0.160063 0.113768 0.066972 0.020233 -0.025799 -0.070499 -0.113396 -0.154133 -0.192434 -0.228087 -0.260935 -0.290868 -0.317827 -0.341795 -0.362800 -0.380914 -0.396253 -0.408976 -0.419282 +0.797619 0.772416 0.745286 0.716196 0.685139 0.652162 0.617157 0.580209 0.541535 0.500976 0.458403 0.413758 0.367089 0.318440 0.267859 0.215464 0.161536 0.106521 0.050989 -0.004371 -0.058762 -0.111433 -0.161837 -0.209563 -0.254300 -0.295806 -0.333907 -0.368486 -0.399482 -0.426887 -0.450748 -0.471165 -0.488287 -0.502317 -0.513506 -0.522151 +0.870407 0.839307 0.807149 0.772800 0.735851 0.696368 0.654402 0.609945 0.563343 0.514447 0.463055 0.409157 0.352830 0.294145 0.233245 0.170467 0.106342 0.041549 -0.023101 -0.086663 -0.148218 -0.207098 -0.262819 -0.315010 -0.363387 -0.407744 -0.447945 -0.483917 -0.515653 -0.543208 -0.566698 -0.586303 -0.602262 -0.614871 -0.624481 -0.631491 +0.937801 0.898777 0.859871 0.820832 0.779117 0.733820 0.685207 0.633320 0.578660 0.521127 0.460477 0.396782 0.330166 0.260796 0.189045 0.115524 0.041008 -0.033579 -0.107154 -0.178587 -0.247028 -0.311881 -0.372693 -0.429119 -0.480906 -0.527879 -0.569945 -0.607081 -0.639338 -0.666842 -0.689787 -0.708441 -0.723135 -0.734268 -0.742302 -0.747750 +1.002272 0.954818 0.907800 0.861790 0.815766 0.765455 0.710516 0.651256 0.588370 0.521837 0.451428 0.377331 0.299768 0.219135 0.136104 0.051544 -0.033517 -0.117878 -0.200191 -0.279333 -0.354544 -0.425257 -0.491041 -0.551574 -0.606630 -0.656072 -0.699848 -0.737992 -0.770621 -0.797933 -0.820208 -0.837806 -0.851169 -0.860810 -0.867314 -0.871279 +1.064598 1.008390 0.952917 0.898894 0.846816 0.792132 0.731177 0.664544 0.593190 0.517221 0.436491 0.351342 0.262208 0.169811 0.075110 -0.020766 -0.116510 -0.210609 -0.301585 -0.388416 -0.470381 -0.546930 -0.617647 -0.682232 -0.740484 -0.792304 -0.837690 -0.876733 -0.909619 -0.936626 -0.958130 -0.974602 -0.986598 -0.994763 -0.999805 -1.002335 +1.125286 1.060007 0.995803 0.933481 0.873901 0.814638 0.747903 0.673814 0.593683 0.507777 0.416109 0.319252 0.217981 0.113352 0.006598 -0.100867 -0.207420 -0.311262 -0.410956 -0.505547 -0.594326 -0.676752 -0.752421 -0.821050 -0.882469 -0.936617 -0.983541 -1.023394 -1.056440 -1.083052 -1.103712 -1.119007 -1.129628 -1.136358 -1.139998 -1.141107 +1.184710 1.110050 1.036840 0.965984 0.898504 0.833644 0.761274 0.679565 0.590284 0.493877 0.390619 0.281434 0.167495 0.050167 -0.069022 -0.188346 -0.305833 -0.419523 -0.528075 -0.630559 -0.726266 -0.814657 -0.895336 -0.968037 -1.032619 -1.089062 -1.137468 -1.178060 -1.211191 -1.237335 -1.257092 -1.271183 -1.280440 -1.285786 -1.288054 -1.287742 +1.243183 1.158809 1.076317 0.996699 0.921110 0.849734 0.771746 0.682183 0.583321 0.475803 0.360302 0.238207 0.111071 -0.019424 -0.151434 -0.282888 -0.411466 -0.535198 -0.652809 -0.763367 -0.866154 -0.960628 -1.046399 -1.123218 -1.190975 -1.249694 -1.299539 -1.340814 -1.373965 -1.399583 -1.418398 -1.431272 -1.439187 -1.443198 -1.444104 -1.442358 +1.300910 1.206519 1.114465 1.025858 0.941977 0.863452 0.779673 0.681967 0.573037 0.453771 0.325405 0.189827 0.048963 -0.095175 -0.240399 -0.384254 -0.524144 -0.658173 -0.785084 -0.903927 -1.013972 -1.114667 -1.205628 -1.286626 -1.357580 -1.418567 -1.469819 -1.511728 -1.544850 -1.569899 -1.587742 -1.599395 -1.606003 -1.608709 -1.608248 -1.605042 +1.358030 1.253357 1.151469 1.053649 0.961301 0.875262 0.785334 0.679149 0.559618 0.427966 0.286133 0.136500 -0.018634 -0.176899 -0.335733 -0.492272 -0.643762 -0.788382 -0.924861 -1.052225 -1.169720 -1.276789 -1.373051 -1.458293 -1.532476 -1.595730 -1.648366 -1.690874 -1.723926 -1.748368 -1.765221 -1.775661 -1.780996 -1.782408 -1.780566 -1.775864 +1.414606 1.299461 1.187483 1.080229 0.979241 0.885469 0.788952 0.673911 0.543212 0.398553 0.242657 0.078384 -0.091569 -0.264451 -0.437297 -0.606831 -0.770255 -0.925787 -1.072124 -1.208256 -1.333409 -1.447015 -1.548693 -1.638256 -1.715703 -1.781230 -1.835235 -1.878313 -1.911261 -1.935071 -1.950921 -1.960159 -1.964252 -1.964367 -1.961118 -1.954875 +1.470532 1.344931 1.222636 1.105728 0.995928 0.894246 0.790705 0.666394 0.523941 0.365671 0.195109 0.015606 -0.169725 -0.357724 -0.544986 -0.727861 -0.903585 -1.070368 -1.226864 -1.372026 -1.505055 -1.625365 -1.732582 -1.826543 -1.907297 -1.975112 -2.030478 -2.074103 -2.106920 -2.130075 -2.144914 -2.152966 -2.155836 -2.154640 -2.149953 -2.142116 +1.525176 1.389818 1.257032 1.130259 1.011475 0.901713 0.790737 0.656712 0.501917 0.329434 0.143594 -0.051741 -0.253016 -0.456636 -0.658718 -0.855321 -1.043728 -1.222114 -1.389084 -1.543550 -1.684675 -1.811862 -1.924743 -2.023186 -2.107295 -2.177416 -2.234138 -2.278294 -2.310959 -2.333441 -2.347264 -2.354147 -2.355801 -2.353275 -2.347110 -2.337621 +1.576091 1.434083 1.290759 1.153917 1.025975 0.907968 0.789164 0.644957 0.477240 0.289934 0.088194 -0.123583 -0.341376 -0.561121 -0.778437 -0.989180 -1.190668 -1.381024 -1.558795 -1.722840 -1.872286 -2.006525 -2.125198 -2.228212 -2.315729 -2.388179 -2.446258 -2.490931 -2.523425 -2.545218 -2.558024 -2.563754 -2.564189 -2.560306 -2.552619 -2.541411 +1.619386 1.477421 1.323881 1.176784 1.039511 0.913087 0.786080 0.631211 0.449991 0.247242 0.028971 -0.199866 -0.434753 -0.671127 -0.904104 -1.129421 -1.344401 -1.547104 -1.736007 -1.909909 -2.067905 -2.209372 -2.333972 -2.441648 -2.532629 -2.607434 -2.666876 -2.712055 -2.744361 -2.765452 -2.777239 -2.781831 -2.781036 -2.775765 -2.766503 -2.753507 +1.652707 1.518703 1.356434 1.198932 1.052154 0.917137 0.781560 0.615550 0.420232 0.201412 -0.034029 -0.280547 -0.533104 -0.786614 -1.035692 -1.276032 -1.504928 -1.720362 -1.920730 -2.104772 -2.271546 -2.420424 -2.551086 -2.663519 -2.758022 -2.835212 -2.896022 -2.941699 -2.973802 -2.994180 -3.004949 -3.008412 -3.006369 -2.999674 -2.988782 -2.973920 +1.676548 1.555748 1.388396 1.220422 1.063964 0.920174 0.775668 0.598036 0.388014 0.152485 -0.100769 -0.365591 -0.636398 -0.907545 -1.173186 -1.429010 -1.672256 -1.900808 -2.112976 -2.307439 -2.483223 -2.639696 -2.776559 -2.893846 -2.991934 -3.071540 -3.133725 -3.179893 -3.211780 -3.231435 -3.241187 -3.243526 -3.240212 -3.232052 -3.219469 -3.202660 +1.694200 1.586793 1.419603 1.241302 1.074992 0.922243 0.768461 0.578719 0.353374 0.100493 -0.171219 -0.454970 -0.744605 -1.033891 -1.316576 -1.588357 -1.846391 -2.088450 -2.312754 -2.517922 -2.702952 -2.867206 -3.010409 -3.132651 -3.234386 -3.316440 -3.380010 -3.426664 -3.458323 -3.477245 -3.485985 -3.487195 -3.482585 -3.472915 -3.458574 -3.439733 +1.707980 1.611070 1.449551 1.261610 1.085283 0.923387 0.759987 0.557635 0.316341 0.045462 -0.245357 -0.548663 -0.857703 -1.165632 -1.465857 -1.754078 -2.027339 -2.283295 -2.520073 -2.736234 -2.930745 -3.102968 -3.252655 -3.379952 -3.485399 -3.569934 -3.634900 -3.682034 -3.713454 -3.731636 -3.739365 -3.739440 -3.733504 -3.722276 -3.706107 -3.685146 + + +# Torque coefficient + +0.037472 0.039863 0.042189 0.044441 0.046608 0.048682 0.050650 0.052503 0.054227 0.055813 0.057249 0.058524 0.059627 0.060548 0.061279 0.061812 0.062139 0.062254 0.062150 0.061823 0.061266 0.060477 0.059449 0.058181 0.056670 0.054913 0.052909 0.050657 0.048158 0.045411 0.042417 0.039178 0.035699 0.031992 0.028077 0.023982 +0.052381 0.054844 0.057148 0.059281 0.061230 0.062981 0.064525 0.065851 0.066949 0.067807 0.068416 0.068770 0.068863 0.068690 0.068248 0.067531 0.066537 0.065262 0.063704 0.061862 0.059734 0.057319 0.054617 0.051628 0.048352 0.044790 0.040942 0.036815 0.032418 0.027777 0.022923 0.017903 0.012773 0.007588 0.002402 -0.002739 +0.065213 0.067380 0.069292 0.070938 0.072309 0.073394 0.074184 0.074675 0.074865 0.074754 0.074332 0.073592 0.072533 0.071152 0.069448 0.067418 0.065062 0.062378 0.059366 0.056026 0.052356 0.048355 0.044023 0.039362 0.034379 0.029092 0.023536 0.017757 0.011818 0.005790 -0.000260 -0.006271 -0.012193 -0.017979 -0.023591 -0.028995 +0.074866 0.076494 0.077787 0.078743 0.079363 0.079649 0.079608 0.079225 0.078492 0.077414 0.075987 0.074198 0.072043 0.069521 0.066631 0.063369 0.059733 0.055720 0.051328 0.046554 0.041395 0.035855 0.029947 0.023703 0.017171 0.010419 0.003529 -0.003415 -0.010337 -0.017172 -0.023865 -0.030366 -0.036636 -0.042642 -0.048360 -0.053772 +0.080732 0.081793 0.082482 0.082797 0.082742 0.082317 0.081529 0.080389 0.078871 0.076967 0.074671 0.071964 0.068840 0.065294 0.061321 0.056916 0.052072 0.046783 0.041045 0.034858 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Controller parameter input file for the NREL-5MW wind turbine +! - File written using ROSCO version 2.8.0 controller tuning logic on 04/10/23 + +!------- DEBUG ------------------------------------------------------------ +1 ! LoggingLevel - (0: write no debug files, 1: write standard output .dbg-file, 2: LoggingLevel 1 + ROSCO LocalVars (.dbg2) 3: LoggingLevel 2 + complete avrSWAP-array (.dbg3)) +0 ! Echo - (0 - no Echo, 1 - Echo input data to .echo) + +!------- CONTROLLER FLAGS ------------------------------------------------- +1 ! F_LPFType - (1: first-order low-pass filter, 2: second-order low-pass filter), [rad/s] (currently filters generator speed and pitch control signals +0 ! F_NotchType - Notch on the measured generator speed and/or tower fore-aft motion (for floating) {0: disable, 1: generator speed, 2: tower-top fore-aft motion, 3: generator speed and tower-top fore-aft motion} +0 ! IPC_ControlMode - Turn Individual Pitch Control (IPC) for fatigue load reductions (pitch contribution) {0: off, 1: 1P reductions, 2: 1P+2P reductions} +3 ! VS_ControlMode - Generator torque control mode in above rated conditions {0: constant torque, 1: constant power, 2: TSR tracking PI control with constant torque, 3: TSR tracking PI control with constant power} +1 ! PC_ControlMode - Blade pitch control mode {0: No pitch, fix to fine pitch, 1: active PI blade pitch control} +0 ! Y_ControlMode - Yaw control mode {0: no yaw control, 1: yaw rate control, 2: yaw-by-IPC} +1 ! SS_Mode - Setpoint Smoother mode {0: no setpoint smoothing, 1: introduce setpoint smoothing} +2 ! WE_Mode - Wind speed estimator mode {0: One-second low pass filtered hub height wind speed, 1: Immersion and Invariance Estimator, 2: Extended Kalman Filter} +1 ! PS_Mode - Pitch saturation mode {0: no pitch saturation, 1: implement pitch saturation} +0 ! SD_Mode - Shutdown mode {0: no shutdown procedure, 1: pitch to max pitch at shutdown} +0 ! Fl_Mode - Floating specific feedback mode {0: no nacelle velocity feedback, 1: feed back translational velocity, 2: feed back rotational veloicty} +0 ! TD_Mode - Tower damper mode {0: no tower damper, 1: feed back translational nacelle accelleration to pitch angle} +0 ! Flp_Mode - Flap control mode {0: no flap control, 1: steady state flap angle, 2: Proportional flap control, 2: Cyclic (1P) flap control} +0 ! OL_Mode - Open loop control mode {0: no open loop control, 1: open loop control vs. time} +0 ! PA_Mode - Pitch actuator mode {0 - not used, 1 - first order filter, 2 - second order filter} +0 ! PF_Mode - Pitch fault mode {0 - not used, 1 - constant offset on one or more blades} +0 ! AWC_Mode - Active wake control {0 - not used, 1 - complex number method, 2 - Coleman transform method} +0 ! Ext_Mode - External control mode {0 - not used, 1 - call external dynamic library} +0 ! ZMQ_Mode - Fuse ZeroMQ interface {0: unused, 1: Yaw Control} +0 ! CC_Mode - Cable control mode [0- unused, 1- User defined, 2- Open loop control] +0 ! StC_Mode - Structural control mode [0- unused, 1- User defined, 2- Open loop control] + +!------- FILTERS ---------------------------------------------------------- +1.57080 ! F_LPFCornerFreq - Corner frequency (-3dB point) in the low-pass filters, [rad/s] +0.00000 ! F_LPFDamping - Damping coefficient {used only when F_FilterType = 2} [-] +0.00000 ! F_NotchCornerFreq - Natural frequency of the notch filter, [rad/s] +0.000000 0.250000 ! F_NotchBetaNumDen - Two notch damping values (numerator and denominator, resp) - determines the width and depth of the notch, [-] +0.62830 ! F_SSCornerFreq - Corner frequency (-3dB point) in the first order low pass filter for the setpoint smoother, [rad/s]. +0.20944 ! F_WECornerFreq - Corner frequency (-3dB point) in the first order low pass filter for the wind speed estimate [rad/s]. +0.17952 ! F_YawErr - Low pass filter corner frequency for yaw controller [rad/s]. +0.000000 1.000000 ! F_FlCornerFreq - Natural frequency and damping in the second order low pass filter of the tower-top fore-aft motion for floating feedback control [rad/s, -]. +0.01042 ! F_FlHighPassFreq - Natural frequency of first-order high-pass filter for nacelle fore-aft motion [rad/s]. +0.000000 1.000000 ! F_FlpCornerFreq - Corner frequency and damping in the second order low pass filter of the blade root bending moment for flap control [rad/s, -]. + +!------- BLADE PITCH CONTROL ---------------------------------------------- +30 ! PC_GS_n - Amount of gain-scheduling table entries +0.056789 0.084492 0.106018 0.124332 0.140807 0.155903 0.169931 0.183270 0.196062 0.208354 0.220050 0.231503 0.242646 0.253377 0.263967 0.274233 0.284343 0.294292 0.303997 0.313626 0.322957 0.332260 0.341319 0.350368 0.359221 0.368059 0.376700 0.385301 0.393691 0.402050 ! PC_GS_angles - Gain-schedule table: pitch angles [rad]. +-0.020655 -0.018159 -0.016134 -0.014459 -0.013049 -0.011846 -0.010809 -0.009904 -0.009108 -0.008403 -0.007774 -0.007209 -0.006698 -0.006235 -0.005813 -0.005427 -0.005072 -0.004745 -0.004442 -0.004162 -0.003901 -0.003657 -0.003430 -0.003217 -0.003017 -0.002829 -0.002652 -0.002484 -0.002326 -0.002176 ! PC_GS_KP - Gain-schedule table: pitch controller kp gains [s]. +-0.008388 -0.007514 -0.006805 -0.006218 -0.005725 -0.005304 -0.004940 -0.004624 -0.004345 -0.004098 -0.003878 -0.003680 -0.003501 -0.003339 -0.003192 -0.003056 -0.002932 -0.002817 -0.002712 -0.002613 -0.002522 -0.002437 -0.002357 -0.002283 -0.002212 -0.002147 -0.002085 -0.002026 -0.001971 -0.001918 ! PC_GS_KI - Gain-schedule table: pitch controller ki gains [-]. +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ! PC_GS_KD - Gain-schedule table: pitch controller kd gains +0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ! PC_GS_TF - Gain-schedule table: pitch controller tf gains (derivative filter) +1.570000000000 ! PC_MaxPit - Maximum physical pitch limit, [rad]. +0.000880000000 ! PC_MinPit - Minimum physical pitch limit, [rad]. +0.174500000000 ! PC_MaxRat - Maximum pitch rate (in absolute value) in pitch controller, [rad/s]. +-0.17450000000 ! PC_MinRat - Minimum pitch rate (in absolute value) in pitch controller, [rad/s]. +122.9096700000 ! PC_RefSpd - Desired (reference) HSS speed for pitch controller, [rad/s]. +0.000880000000 ! PC_FinePit - Record 5: Below-rated pitch angle set-point, [rad] +0.017450000000 ! PC_Switch - Angle above lowest minimum pitch angle for switch, [rad] + +!------- INDIVIDUAL PITCH CONTROL ----------------------------------------- +9.120000 11.400000 ! IPC_Vramp - Start and end wind speeds for cut-in ramp function. First entry: IPC inactive, second entry: IPC fully active. [m/s] +2 ! IPC_SatMode - IPC Saturation method (0 - no saturation (except by PC_MinPit), 1 - saturate by PS_BldPitchMin, 2 - saturate sotfly (full IPC cycle) by PC_MinPit, 3 - saturate softly by PS_BldPitchMin) +0.3 ! IPC_IntSat - Integrator saturation (maximum signal amplitude contribution to pitch from IPC), [rad] +0.000e+00 0.000e+00 ! IPC_KP - Proportional gain for the individual pitch controller: first parameter for 1P reductions, second for 2P reductions, [-] +0.000e+00 0.000e+00 ! IPC_KI - Integral gain for the individual pitch controller: first parameter for 1P reductions, second for 2P reductions, [-] +0.000000 0.000000 ! IPC_aziOffset - Phase offset added to the azimuth angle for the individual pitch controller, [rad]. +0.0 ! IPC_CornerFreqAct - Corner frequency of the first-order actuators model, to induce a phase lag in the IPC signal {0: Disable}, [rad/s] + +!------- VS TORQUE CONTROL ------------------------------------------------ +94.40000000000 ! VS_GenEff - Generator efficiency mechanical power -> electrical power, [should match the efficiency defined in the generator properties!], [%] +43093.51876000 ! VS_ArSatTq - Above rated generator torque PI control saturation, [Nm] +1500000.000000 ! VS_MaxRat - Maximum torque rate (in absolute value) in torque controller, [Nm/s]. +47402.87063000 ! VS_MaxTq - Maximum generator torque in Region 3 (HSS side), [Nm]. +0.000000000000 ! VS_MinTq - Minimum generator torque (HSS side), [Nm]. +35.29006000000 ! VS_MinOMSpd - Minimum generator speed [rad/s] +2.185750000000 ! VS_Rgn2K - Generator torque constant in Region 2 (HSS side), [Nm/(rad/s)^2] +5000000.000000 ! VS_RtPwr - Wind turbine rated power [W] +43093.51876000 ! VS_RtTq - Rated torque, [Nm]. +122.9096700000 ! VS_RefSpd - Rated generator speed [rad/s] +1 ! VS_n - Number of generator PI torque controller gains +-657.442080000 ! VS_KP - Proportional gain for generator PI torque controller [-]. (Only used in the transitional 2.5 region if VS_ControlMode =/ 2) +-104.507080000 ! VS_KI - Integral gain for generator PI torque controller [s]. (Only used in the transitional 2.5 region if VS_ControlMode =/ 2) +7.64 ! VS_TSRopt - Power-maximizing region 2 tip-speed-ratio [rad]. + +!------- SETPOINT SMOOTHER --------------------------------------------- +1.00000 ! SS_VSGain - Variable speed torque controller setpoint smoother gain, [-]. +0.00100 ! SS_PCGain - Collective pitch controller setpoint smoother gain, [-]. + +!------- WIND SPEED ESTIMATOR --------------------------------------------- +63.000 ! WE_BladeRadius - Blade length (distance from hub center to blade tip), [m] +1 ! WE_CP_n - Amount of parameters in the Cp array +0.0 ! WE_CP - Parameters that define the parameterized CP(lambda) function +0.0 ! WE_Gamma - Adaption gain of the wind speed estimator algorithm [m/rad] +97.0 ! WE_GearboxRatio - Gearbox ratio [>=1], [-] +43702538.05700 ! WE_Jtot - Total drivetrain inertia, including blades, hub and casted generator inertia to LSS, [kg m^2] +1.225 ! WE_RhoAir - Air density, [kg m^-3] +"MHK_RM1_Cp_Ct_Cq.txt" ! PerfFileName - File containing rotor performance tables (Cp,Ct,Cq) (absolute path or relative to this file) +36 26 ! PerfTableSize - Size of rotor performance tables, first number refers to number of blade pitch angles, second number referse to number of tip-speed ratios +60 ! WE_FOPoles_N - Number of first-order system poles used in EKF +3.0000 3.2897 3.5793 3.8690 4.1586 4.4483 4.7379 5.0276 5.3172 5.6069 5.8966 6.1862 6.4759 6.7655 7.0552 7.3448 7.6345 7.9241 8.2138 8.5034 8.7931 9.0828 9.3724 9.6621 9.9517 10.2414 10.5310 10.8207 11.1103 11.4000 11.8533 12.3067 12.7600 13.2133 13.6667 14.1200 14.5733 15.0267 15.4800 15.9333 16.3867 16.8400 17.2933 17.7467 18.2000 18.6533 19.1067 19.5600 20.0133 20.4667 20.9200 21.3733 21.8267 22.2800 22.7333 23.1867 23.6400 24.0933 24.5467 25.0000 ! WE_FOPoles_v - Wind speeds corresponding to first-order system poles [m/s] +-0.01638154 -0.01796321 -0.01954487 -0.02112654 -0.02270820 -0.02428987 -0.02587154 -0.02745320 -0.02903487 -0.03061653 -0.03219820 -0.03377987 -0.03536153 -0.03694320 -0.03852486 -0.04010653 -0.04168820 -0.04326986 -0.04485153 -0.04643319 -0.04801486 -0.04959652 -0.05117819 -0.05275986 -0.05434152 -0.05592319 -0.05758373 -0.05882656 -0.06845507 -0.05992890 0.02094683 0.01327182 0.00285485 -0.00935731 -0.02210773 -0.03573037 -0.04990222 -0.06404904 -0.07899629 -0.09463190 -0.10954192 -0.12525205 -0.14168652 -0.15843395 -0.17415061 -0.19052486 -0.20780146 -0.22581018 -0.24373777 -0.26010871 -0.27706767 -0.29551708 -0.31430599 -0.33428552 -0.35420853 -0.37183729 -0.38936451 -0.40828911 -0.42758878 -0.44818175 ! WE_FOPoles - First order system poles [1/s] + +!------- YAW CONTROL ------------------------------------------------------ +0.00000 ! Y_uSwitch - Wind speed to switch between Y_ErrThresh. If zero, only the second value of Y_ErrThresh is used [m/s] +4.000000 8.000000 ! Y_ErrThresh - Yaw error threshold/deadbands. Turbine begins to yaw when it passes this. If Y_uSwitch is zero, only the second value is used. [deg]. +0.00870 ! Y_Rate - Yaw rate [rad/s] +0.00000 ! Y_MErrSet - Integrator saturation (maximum signal amplitude contribution to pitch from yaw-by-IPC), [rad] +0.00000 ! Y_IPC_IntSat - Integrator saturation (maximum signal amplitude contribution to pitch from yaw-by-IPC), [rad] +0.00000 ! Y_IPC_KP - Yaw-by-IPC proportional controller gain Kp +0.00000 ! Y_IPC_KI - Yaw-by-IPC integral controller gain Ki + +!------- TOWER FORE-AFT DAMPING ------------------------------------------- +-1.00000 ! FA_KI - Integral gain for the fore-aft tower damper controller [rad s/m] +0.0 ! FA_HPFCornerFreq - Corner frequency (-3dB point) in the high-pass filter on the fore-aft acceleration signal [rad/s] +0.0 ! FA_IntSat - Integrator saturation (maximum signal amplitude contribution to pitch from FA damper), [rad] + +!------- MINIMUM PITCH SATURATION ------------------------------------------- +60 ! PS_BldPitchMin_N - Number of values in minimum blade pitch lookup table (should equal number of values in PS_WindSpeeds and PS_BldPitchMin) +3.0000 3.2897 3.5793 3.8690 4.1586 4.4483 4.7379 5.0276 5.3172 5.6069 5.8966 6.1862 6.4759 6.7655 7.0552 7.3448 7.6345 7.9241 8.2138 8.5034 8.7931 9.0828 9.3724 9.6621 9.9517 10.2414 10.5310 10.8207 11.1103 11.4000 11.8533 12.3067 12.7600 13.2133 13.6667 14.1200 14.5733 15.0267 15.4800 15.9333 16.3867 16.8400 17.2933 17.7467 18.2000 18.6533 19.1067 19.5600 20.0133 20.4667 20.9200 21.3733 21.8267 22.2800 22.7333 23.1867 23.6400 24.0933 24.5467 25.0000 ! PS_WindSpeeds - Wind speeds corresponding to minimum blade pitch angles [m/s] +0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.00088027 0.01116187 0.02310060 0.03164156 0.03968639 0.04741131 0.05899071 0.07019329 0.08090119 0.09141661 0.10169361 0.11174124 0.12170066 0.13137494 0.14103377 0.15048120 0.15989580 0.16913227 0.17831010 0.18739638 0.19643591 0.20537011 0.21419674 0.22293719 0.23161365 0.24025975 0.24885012 0.25735704 0.26578040 0.27407178 0.28233051 0.29047594 0.29867838 0.30674517 0.31490089 0.32284411 ! PS_BldPitchMin - Minimum blade pitch angles [rad] + +!------- SHUTDOWN ----------------------------------------------------------- +0.436300000000 ! SD_MaxPit - Maximum blade pitch angle to initiate shutdown, [rad] +0.418880000000 ! SD_CornerFreq - Cutoff Frequency for first order low-pass filter for blade pitch angle, [rad/s] + +!------- Floating ----------------------------------------------------------- +0.000000000000 ! Fl_Kp - Nacelle velocity proportional feedback gain [s] + +!------- FLAP ACTUATION ----------------------------------------------------- +0.000000000000 ! Flp_Angle - Initial or steady state flap angle [rad] +0.00000000e+00 ! Flp_Kp - Blade root bending moment proportional gain for flap control [s] +0.00000000e+00 ! Flp_Ki - Flap displacement integral gain for flap control [-] +0.174500000000 ! Flp_MaxPit - Maximum (and minimum) flap pitch angle [rad] + +!------- Open Loop Control ----------------------------------------------------- +"unused" ! OL_Filename - Input file with open loop timeseries (absolute path or relative to this file) +0 ! Ind_Breakpoint - The column in OL_Filename that contains the breakpoint (time if OL_Mode = 1) +0 ! Ind_BldPitch - The column in OL_Filename that contains the blade pitch input in rad +0 ! Ind_GenTq - The column in OL_Filename that contains the generator torque in Nm +0 ! Ind_YawRate - The column in OL_Filename that contains the nacelle yaw rate rad/s +0 ! Ind_CableControl - The column(s) in OL_Filename that contains the cable control inputs in m [Used with CC_Mode = 2, must be the same size as CC_Group_N] +0 ! Ind_StructControl - The column(s) in OL_Filename that contains the structural control inputs [Used with StC_Mode = 2, must be the same size as StC_Group_N] + +!------- Pitch Actuator Model ----------------------------------------------------- +3.140000000000 ! PA_CornerFreq - Pitch actuator bandwidth/cut-off frequency [rad/s] +0.707000000000 ! PA_Damping - Pitch actuator damping ratio [-, unused if PA_Mode = 1] + +!------- Pitch Actuator Faults ----------------------------------------------------- +0.00000000 0.00000000 0.00000000 ! PF_Offsets - Constant blade pitch offsets for blades 1-3 [rad] + +!------- Active Wake Control ----------------------------------------------------- +1 ! AWC_NumModes - Number of user-defined AWC forcing modes +1 ! AWC_n - Azimuthal mode number(s) (i.e., the number and direction of the lobes of the wake structure) +1 ! AWC_harmonic - Harmonic(s) to apply in the AWC Inverse Coleman Transformation (only used when AWC_Mode = 2) +0.0500 ! AWC_freq - Frequency(s) of forcing mode(s) [Hz] +1.0000 ! AWC_amp - Pitch amplitude(s) of individual forcing mode(s) [deg] +0.0000 ! AWC_clockangle - Initial angle(s) of forcing mode(s) [deg] + +!------- External Controller Interface ----------------------------------------------------- +"unused" ! DLL_FileName - Name/location of the dynamic library in the Bladed-DLL format +"unused" ! DLL_InFile - Name of input file sent to the DLL (-) +"DISCON" ! DLL_ProcName - Name of procedure in DLL to be called (-) + +!------- ZeroMQ Interface --------------------------------------------------------- +"tcp://localhost:5555" ! ZMQ_CommAddress - Communication address for ZMQ server, (e.g. "tcp://localhost:5555") +2 ! ZMQ_UpdatePeriod - Call ZeroMQ every [x] seconds, [s] + +!------- Cable Control --------------------------------------------------------- +1 ! CC_Group_N - Number of cable control groups + 0 ! CC_GroupIndex - First index for cable control group, should correspond to deltaL +20.000000 ! CC_ActTau - Time constant for line actuator [s] + +!------- Structural Controllers --------------------------------------------------------- +1 ! StC_Group_N - Number of cable control groups + 0 ! StC_GroupIndex - First index for structural control group, options specified in ServoDyn summary output diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Blade.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Blade.dat new file mode 100644 index 000000000..df6ab8c87 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Blade.dat @@ -0,0 +1,107 @@ +------- ELASTODYN V1.00.* INDIVIDUAL BLADE INPUT FILE -------------------------- +Floating MHK turbine structural blade input properties, based on the RM1 tidal current rotor +---------------------- BLADE PARAMETERS ---------------------------------------- + 75 NBlInpSt - Number of blade input stations (-) + 1 BldFlDmp(1) - Blade flap mode #1 structural damping in percent of critical (%) + 1 BldFlDmp(2) - Blade flap mode #2 structural damping in percent of critical (%) + 1 BldEdDmp(1) - Blade edge mode #1 structural damping in percent of critical (%) +---------------------- BLADE ADJUSTMENT FACTORS -------------------------------- + 1 FlStTunr(1) - Blade flapwise modal stiffness tuner, 1st mode (-) + 1 FlStTunr(2) - Blade flapwise modal stiffness tuner, 2nd mode (-) + 2.5 AdjBlMs - Factor to adjust blade mass density (-) + 1.0E-05 AdjFlSt - Factor to adjust blade flap stiffness (-) + 1.0E-05 AdjEdSt - Factor to adjust blade edge stiffness (-) +---------------------- DISTRIBUTED BLADE PROPERTIES ---------------------------- +BlFract PitchAxis StrcTwst BMassDen FlpStff EdgStff +(-) (-) (deg) (kg/m) (Nm^2) (Nm^2) +0.0000 0.5000 12.860 818.68 2.43E+14 2.52E+14 +0.0083 0.4953 12.860 833.60 2.47E+14 2.69E+14 +0.0167 0.4907 12.860 846.44 2.47E+14 2.84E+14 +0.0250 0.4860 12.860 858.68 2.46E+14 3.00E+14 +0.0333 0.4813 12.860 870.32 2.44E+14 3.15E+14 +0.0417 0.4701 12.860 900.64 2.50E+14 3.59E+14 +0.0500 0.4590 12.860 932.64 2.49E+14 4.03E+14 +0.0583 0.4478 12.860 681.32 1.70E+14 1.12E+14 +0.0667 0.4366 12.860 628.00 1.48E+14 1.11E+14 +0.0750 0.4233 12.860 575.28 1.30E+14 1.10E+14 +0.0833 0.4100 12.860 528.96 1.12E+14 1.07E+14 +0.0917 0.3967 12.860 458.92 8.82E+13 9.56E+13 +0.1000 0.3834 12.860 401.60 6.72E+13 8.55E+13 +0.1083 0.3722 12.860 411.16 6.45E+13 9.15E+13 +0.1167 0.3611 12.860 410.28 6.00E+13 9.53E+13 +0.1250 0.3499 12.860 415.36 5.62E+13 1.01E+14 +0.1333 0.3387 12.860 421.00 5.17E+13 1.06E+14 +0.1417 0.3340 12.860 421.28 4.94E+13 1.08E+14 +0.1500 0.3294 12.860 421.40 4.70E+13 1.10E+14 +0.1583 0.3247 12.860 424.96 4.45E+13 1.12E+14 +0.1667 0.3200 12.860 435.48 4.30E+13 1.16E+14 +0.1750 0.3200 12.530 428.40 4.09E+13 1.13E+14 +0.1833 0.3200 12.200 423.88 3.89E+13 1.10E+14 +0.1917 0.3200 11.870 435.68 3.84E+13 1.15E+14 +0.2000 0.3200 11.540 430.60 3.62E+13 1.12E+14 +0.2083 0.3200 11.265 434.52 3.57E+13 1.11E+14 +0.2167 0.3200 10.990 430.28 3.41E+13 1.09E+14 +0.2250 0.3200 10.715 426.04 3.26E+13 1.06E+14 +0.2333 0.3200 10.440 421.84 3.11E+13 1.03E+14 +0.2500 0.3200 9.970 415.00 2.91E+13 9.84E+13 +0.2667 0.3200 9.500 411.20 2.72E+13 9.54E+13 +0.2833 0.3200 9.105 395.88 2.54E+13 8.96E+13 +0.3000 0.3200 8.710 390.40 2.43E+13 8.59E+13 +0.3167 0.3200 8.365 377.84 2.26E+13 8.06E+13 +0.3333 0.3200 8.020 362.44 2.15E+13 7.34E+13 +0.3500 0.3200 7.725 347.48 2.00E+13 6.87E+13 +0.3667 0.3200 7.430 335.60 1.85E+13 6.42E+13 +0.3833 0.3200 7.170 330.64 1.77E+13 6.14E+13 +0.4000 0.3200 6.910 318.96 1.63E+13 5.72E+13 +0.4167 0.3200 6.680 302.84 1.50E+13 5.33E+13 +0.4333 0.3200 6.450 298.24 1.43E+13 5.09E+13 +0.4500 0.3200 6.245 287.12 1.32E+13 4.73E+13 +0.4667 0.3200 6.040 267.24 1.19E+13 4.12E+13 +0.4833 0.3200 5.860 256.68 1.09E+13 3.81E+13 +0.5000 0.3200 5.680 249.96 1.04E+13 3.62E+13 +0.5167 0.3200 5.515 239.68 9.42E+12 3.33E+13 +0.5333 0.3200 5.350 227.76 8.54E+12 3.06E+13 +0.5500 0.3200 5.200 215.64 7.73E+12 2.81E+13 +0.5667 0.3200 5.050 206.16 6.97E+12 2.58E+13 +0.5833 0.3200 4.910 188.80 6.17E+12 2.17E+13 +0.6000 0.3200 4.770 179.80 5.51E+12 1.97E+13 +0.6167 0.3200 4.640 176.28 5.19E+12 1.85E+13 +0.6333 0.3200 4.510 165.32 4.61E+12 1.68E+13 +0.6500 0.3200 4.385 156.80 4.07E+12 1.51E+13 +0.6667 0.3200 4.260 144.88 3.57E+12 1.36E+13 +0.6833 0.3200 4.145 136.80 3.11E+12 1.21E+13 +0.7000 0.3200 4.030 128.92 2.69E+12 1.08E+13 +0.7167 0.3200 3.915 119.36 2.31E+12 9.55E+12 +0.7333 0.3200 3.800 111.96 1.96E+12 8.42E+12 +0.7500 0.3200 3.685 103.00 1.65E+12 7.39E+12 +0.7667 0.3200 3.570 94.72 1.37E+12 6.45E+12 +0.7833 0.3200 3.460 87.96 1.12E+12 5.57E+12 +0.8000 0.3200 3.350 79.76 8.91E+11 4.77E+12 +0.8167 0.3200 3.240 73.52 6.93E+11 4.05E+12 +0.8333 0.3200 3.130 71.44 6.36E+11 3.72E+12 +0.8500 0.3200 3.015 64.00 4.76E+11 3.11E+12 +0.8667 0.3200 2.900 57.24 3.38E+11 2.56E+12 +0.8833 0.3200 2.785 50.36 2.19E+11 2.04E+12 +0.9000 0.3200 2.670 48.68 1.98E+11 1.84E+12 +0.9167 0.3200 2.550 47.00 1.78E+11 1.66E+12 +0.9333 0.3200 2.430 45.32 1.59E+11 1.49E+12 +0.9500 0.3200 2.305 46.76 1.98E+11 1.51E+12 +0.9667 0.3200 2.180 44.04 1.76E+11 1.34E+12 +0.9833 0.3200 2.060 42.28 1.56E+11 1.19E+12 +1.0000 0.3200 2.060 42.28 1.56E+11 1.19E+12 +---------------------- BLADE MODE SHAPES --------------------------------------- + 1.88070 BldFl1Sh(2) - Flap mode 1, coeff of x^2 +-1.44580 BldFl1Sh(3) - , coeff of x^3 + 0.71967 BldFl1Sh(4) - , coeff of x^4 +-0.03633 BldFl1Sh(5) - , coeff of x^5 +-0.11822 BldFl1Sh(6) - , coeff of x^6 +-5.52180 BldFl2Sh(2) - Flap mode 2, coeff of x^2 + 7.45710 BldFl2Sh(3) - , coeff of x^3 + 2.84170 BldFl2Sh(4) - , coeff of x^4 +-3.97820 BldFl2Sh(5) - , coeff of x^5 + 0.20116 BldFl2Sh(6) - , coeff of x^6 + 2.48090 BldEdgSh(2) - Edge mode 1, coeff of x^2 +-4.17160 BldEdgSh(3) - , coeff of x^3 + 5.03260 BldEdgSh(4) - , coeff of x^4 +-2.86170 BldEdgSh(5) - , coeff of x^5 + 0.51983 BldEdgSh(6) - , coeff of x^6 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Tower.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Tower.dat new file mode 100644 index 000000000..3c04a12f8 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ElastoDyn_Tower.dat @@ -0,0 +1,52 @@ +------- ELASTODYN V1.00.* TOWER INPUT FILE ------------------------------------- +Floating MHK turbine structural tower input properties, based on the RM1 tidal current rotor +---------------------- TOWER PARAMETERS ---------------------------------------- + 11 NTwInpSt - Number of input stations to specify tower geometry + 1 TwrFADmp(1) - Tower 1st fore-aft mode structural damping ratio (%) + 1 TwrFADmp(2) - Tower 2nd fore-aft mode structural damping ratio (%) + 1 TwrSSDmp(1) - Tower 1st side-to-side mode structural damping ratio (%) + 1 TwrSSDmp(2) - Tower 2nd side-to-side mode structural damping ratio (%) +---------------------- TOWER ADJUSTMUNT FACTORS -------------------------------- + 1 FAStTunr(1) - Tower fore-aft modal stiffness tuner, 1st mode (-) + 1 FAStTunr(2) - Tower fore-aft modal stiffness tuner, 2nd mode (-) + 1 SSStTunr(1) - Tower side-to-side stiffness tuner, 1st mode (-) + 1 SSStTunr(2) - Tower side-to-side stiffness tuner, 2nd mode (-) + 1 AdjTwMa - Factor to adjust tower mass density (-) + 1.0E-03 AdjFASt - Factor to adjust tower fore-aft stiffness (-) + 1.0E-03 AdjSSSt - Factor to adjust tower side-to-side stiffness (-) +---------------------- DISTRIBUTED TOWER PROPERTIES ---------------------------- +HtFract TMassDen TwFAStif TwSSStif +(-) (kg/m) (Nm^2) (Nm^2) +0.0000000E+00 5076.39 1.27E+11 1.27E+11 +1.0000000E-01 5076.39 1.27E+11 1.27E+11 +2.0000000E-01 5076.39 1.27E+11 1.27E+11 +3.0000000E-01 5076.39 1.27E+11 1.27E+11 +4.0000000E-01 5076.39 1.27E+11 1.27E+11 +5.0000000E-01 5076.39 1.27E+11 1.27E+11 +6.0000000E-01 5076.39 1.27E+11 1.27E+11 +7.0000000E-01 5076.39 1.27E+11 1.27E+11 +8.0000000E-01 5076.39 1.27E+11 1.27E+11 +9.0000000E-01 5076.39 1.27E+11 1.27E+11 +1.0000000E+00 5076.39 1.27E+11 1.27E+11 +---------------------- TOWER FORE-AFT MODE SHAPES ------------------------------ + -5.6786 TwFAM1Sh(2) - Mode 1, coefficient of x^2 term + 41.5614 TwFAM1Sh(3) - , coefficient of x^3 term + -93.3287 TwFAM1Sh(4) - , coefficient of x^4 term + 89.2568 TwFAM1Sh(5) - , coefficient of x^5 term + -30.8109 TwFAM1Sh(6) - , coefficient of x^6 term + 0.6979 TwFAM2Sh(2) - Mode 2, coefficient of x^2 term + 1.2146 TwFAM2Sh(3) - , coefficient of x^3 term + -1.6999 TwFAM2Sh(4) - , coefficient of x^4 term + 0.8473 TwFAM2Sh(5) - , coefficient of x^5 term + -0.0599 TwFAM2Sh(6) - , coefficient of x^6 term +---------------------- TOWER SIDE-TO-SIDE MODE SHAPES -------------------------- + 0.5640 TwSSM1Sh(2) - Mode 1, coefficient of x^2 term + 4.7082 TwSSM1Sh(3) - , coefficient of x^3 term + -14.9130 TwSSM1Sh(4) - , coefficient of x^4 term + 18.2340 TwSSM1Sh(5) - , coefficient of x^5 term + -7.5932 TwSSM1Sh(6) - , coefficient of x^6 term + 0.9525 TwSSM2Sh(2) - Mode 2, coefficient of x^2 term + 0.4369 TwSSM2Sh(3) - , coefficient of x^3 term + -1.1374 TwSSM2Sh(4) - , coefficient of x^4 term + 1.1954 TwSSM2Sh(5) - , coefficient of x^5 term + -0.4474 TwSSM2Sh(6) - , coefficient of x^6 term \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.1 b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.1 new file mode 100644 index 000000000..c32060dc0 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.1 @@ -0,0 +1,4220 @@ + -1.000000E+00 1 1 1.999545E+03 + -1.000000E+00 1 5 -1.038155E+04 + -1.000000E+00 2 2 2.266626E+03 + -1.000000E+00 2 4 1.450345E+04 + -1.000000E+00 3 3 2.167146E+03 + -1.000000E+00 4 2 1.451960E+04 + -1.000000E+00 4 4 2.703220E+05 + -1.000000E+00 5 1 -1.039882E+04 + -1.000000E+00 5 5 8.266187E+05 + -1.000000E+00 6 6 9.046082E+05 + 0.000000E+00 1 1 1.411834E+03 + 0.000000E+00 1 5 -9.031812E+03 + 0.000000E+00 2 2 1.657916E+03 + 0.000000E+00 2 4 1.228014E+04 + 0.000000E+00 3 3 2.166489E+03 + 0.000000E+00 4 2 1.229162E+04 + 0.000000E+00 4 4 2.611510E+05 + 0.000000E+00 5 1 -9.043577E+03 + 0.000000E+00 5 5 8.192453E+05 + 0.000000E+00 6 6 6.521804E+05 + 1.000000E-01 1 1 1.647323E+03 6.332432E-04 + 1.000000E-01 1 5 -9.712987E+03 1.055422E-06 + 1.000000E-01 2 2 1.921107E+03 -6.797895E-06 + 1.000000E-01 2 4 1.298169E+04 -4.832497E-06 + 1.000000E-01 3 3 2.120432E+03 -1.464990E-06 + 1.000000E-01 4 2 1.312899E+04 2.916794E-05 + 1.000000E-01 4 4 2.638432E+05 2.756118E-05 + 1.000000E-01 5 1 -9.566795E+03 -3.792644E-03 + 1.000000E-01 5 5 8.247277E+05 -3.473593E-07 + 1.000000E-01 6 6 8.442138E+05 7.588721E+03 + 2.000000E-01 1 1 1.489483E+03 -5.117749E-03 + 2.000000E-01 1 5 -9.217624E+03 7.927747E-03 + 2.000000E-01 2 2 1.736418E+03 8.147049E-03 + 2.000000E-01 2 4 1.245268E+04 1.932775E-02 + 2.000000E-01 3 3 2.164448E+03 9.414506E-04 + 2.000000E-01 4 2 1.251272E+04 -4.435483E-02 + 2.000000E-01 4 4 2.615695E+05 -1.109551E-01 + 2.000000E-01 5 1 -9.202860E+03 1.644901E-04 + 2.000000E-01 5 5 8.194167E+05 -1.930356E-04 + 2.000000E-01 6 6 6.884296E+05 -1.733961E-01 + 3.000000E-01 1 1 1.444033E+03 6.269322E-03 + 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-9.002525E+01 -7.234043E-10 -1.641623E-06 + 4.199993E+01 9.000000E+01 2 3.365955E+01 8.996503E+01 2.054098E-02 3.365954E+01 + 4.199993E+01 9.000000E+01 3 1.983625E+02 1.396369E-01 1.983619E+02 4.834336E-01 + 4.199993E+01 9.000000E+01 4 1.084613E+02 8.996510E+01 6.608290E-02 1.084613E+02 + 4.199993E+01 9.000000E+01 5 7.665897E-05 -8.999638E+01 4.852652E-09 -7.665897E-05 + 4.199993E+01 9.000000E+01 6 2.518950E-06 1.799999E+02 -2.518950E-06 5.402741E-12 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.fst b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.fst new file mode 100644 index 000000000..ed0fdd6dc --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.fst @@ -0,0 +1,71 @@ +------- OpenFAST EXAMPLE INPUT FILE -------------------------------------------- +Floating MHK turbine, based on the RM1 tidal current rotor +---------------------- SIMULATION CONTROL -------------------------------------- +False Echo - Echo input data to .ech (flag) +"FATAL" AbortLevel - Error level when simulation should abort (string) {"WARNING", "SEVERE", "FATAL"} + 100 TMax - Total run time (s) + 0.02 DT - Recommended module time step (s) + 2 InterpOrder - Interpolation order for input/output time history (-) {1=linear, 2=quadratic} + 5 NumCrctn - Number of correction iterations (-) {0=explicit calculation, i.e., no corrections} + 1 DT_UJac - Time between calls to get Jacobians (s) + 1E+06 UJacSclFact - Scaling factor used in Jacobians (-) +---------------------- FEATURE SWITCHES AND FLAGS ------------------------------ + 1 CompElast - Compute structural dynamics (switch) {1=ElastoDyn; 2=ElastoDyn + BeamDyn for blades} + 1 CompInflow - Compute inflow wind velocities (switch) {0=still air; 1=InflowWind; 2=external from OpenFOAM} + 2 CompAero - Compute aerodynamic loads (switch) {0=None; 1=AeroDyn v14; 2=AeroDyn v15} + 1 CompServo - Compute control and electrical-drive dynamics (switch) {0=None; 1=ServoDyn} + 1 CompHydro - Compute hydrodynamic loads (switch) {0=None; 1=HydroDyn} + 0 CompSub - Compute sub-structural dynamics (switch) {0=None; 1=SubDyn; 2=External Platform MCKF} + 3 CompMooring - Compute mooring system (switch) {0=None; 1=MAP++; 2=FEAMooring; 3=MoorDyn; 4=OrcaFlex} + 0 CompIce - Compute ice loads (switch) {0=None; 1=IceFloe; 2=IceDyn} + 2 MHK - MHK turbine type (switch) {0=Not an MHK turbine; 1=Fixed MHK turbine; 2=Floating MHK turbine} +---------------------- ENVIRONMENTAL CONDITIONS -------------------------------- + 9.80665 Gravity - Gravitational acceleration (m/s^2) + 1.225 AirDens - Air density (kg/m^3) + 1025.0 WtrDens - Water density (kg/m^3) + 1.06E-06 KinVisc - Kinematic viscosity of working fluid (m^2/s) + 1500 SpdSound - Speed of sound in working fluid (m/s) + 101325 Patm - Atmospheric pressure (Pa) [used only for an MHK turbine cavitation check] + 2500 Pvap - Vapour pressure of working fluid (Pa) [used only for an MHK turbine cavitation check] + 50 WtrDpth - Water depth (m) + 0 MSL2SWL - Offset between still-water level and mean sea level (m) [positive upward] +---------------------- INPUT FILES --------------------------------------------- +"MHK_RM1_Floating_ElastoDyn.dat" EDFile - Name of file containing ElastoDyn input parameters (quoted string) +"unused" BDBldFile(1) - Name of file containing BeamDyn input parameters for blade 1 (quoted string) +"unused" BDBldFile(2) - Name of file containing BeamDyn input parameters for blade 2 (quoted string) +"unused" BDBldFile(3) - Name of file containing BeamDyn input parameters for blade 3 (quoted string) +"MHK_RM1_Floating_InflowWind.dat" InflowFile - Name of file containing inflow wind input parameters (quoted string) +"MHK_RM1_Floating_AeroDyn15.dat" AeroFile - Name of file containing aerodynamic input parameters (quoted string) +"MHK_RM1_ServoDyn.dat" ServoFile - Name of file containing control and electrical-drive input parameters (quoted string) +"MHK_RM1_Floating_HydroDyn.dat" HydroFile - Name of file containing hydrodynamic input parameters (quoted string) +"unused" SubFile - Name of file containing sub-structural input parameters (quoted string) +"MHK_RM1_Floating_MoorDyn.dat" MooringFile - Name of file containing mooring system input parameters (quoted string) +"unused" IceFile - Name of file containing ice input parameters (quoted string) +---------------------- OUTPUT -------------------------------------------------- +True SumPrint - Print summary data to ".sum" (flag) + 5 SttsTime - Amount of time between screen status messages (s) + 99999 ChkptTime - Amount of time between creating checkpoint files for potential restart (s) + 0.02 DT_Out - Time step for tabular output (s) (or "default") + 0 TStart - Time to begin tabular output (s) + 3 OutFileFmt - Format for tabular (time-marching) output file (switch) {0: uncompressed binary [.outb], 1: text file [.out], 2: binary file [.outb], 3: both 1 and 2} +True TabDelim - Use tab delimiters in text tabular output file? (flag) {uses spaces if false} +"ES10.3E2" OutFmt - Format used for text tabular output, excluding the time channel. Resulting field should be 10 characters. (quoted string) +---------------------- LINEARIZATION ------------------------------------------- +False Linearize - Linearization analysis (flag) +False CalcSteady - Calculate a steady-state periodic operating point before linearization? [unused if Linearize=False] (flag) + 1 TrimCase - Controller parameter to be trimmed {1:yaw; 2:torque; 3:pitch} [used only if CalcSteady=True] (-) + 0.01 TrimTol - Tolerance for the rotational speed convergence [used only if CalcSteady=True] (-) + 0.01 TrimGain - Proportional gain for the rotational speed error (>0) [used only if CalcSteady=True] (rad/(rad/s) for yaw or pitch; Nm/(rad/s) for torque) + 0 Twr_Kdmp - Damping factor for the tower [used only if CalcSteady=True] (N/(m/s)) + 0 Bld_Kdmp - Damping factor for the blades [used only if CalcSteady=True] (N/(m/s)) + 0 NLinTimes - Number of times to linearize (-) [>=1] [unused if Linearize=False] + 0 LinTimes - List of times at which to linearize (s) [1 to NLinTimes] [used only when Linearize=True and CalcSteady=False] + 0 LinInputs - Inputs included in linearization (switch) {0=none; 1=standard; 2=all module inputs (debug)} [unused if Linearize=False] + 0 LinOutputs - Outputs included in linearization (switch) {0=none; 1=from OutList(s); 2=all module outputs (debug)} [unused if Linearize=False] +False LinOutJac - Include full Jacobians in linearization output (for debug) (flag) [unused if Linearize=False; used only if LinInputs=LinOutputs=2] +False LinOutMod - Write module-level linearization output files in addition to output for full system? (flag) [unused if Linearize=False] +---------------------- VISUALIZATION ------------------------------------------- + 0 WrVTK - VTK visualization data output: (switch) {0=none; 1=initialization data only; 2=animation; 3=mode shapes} + 1 VTK_type - Type of VTK visualization data: (switch) {1=surfaces; 2=basic meshes (lines/points); 3=all meshes (debug)} [unused if WrVTK=0] +False VTK_fields - Write mesh fields to VTK data files? (flag) {true/false} [unused if WrVTK=0] + 0 VTK_fps - Frame rate for VTK output (frames per second){will use closest integer multiple of DT} [used only if WrVTK=2 or WrVTK=3] diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.hst b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.hst new file mode 100644 index 000000000..7a48e35b6 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating.hst @@ -0,0 +1,36 @@ + 1 1 0.000000E+00 + 1 2 0.000000E+00 + 1 3 0.000000E+00 + 1 4 0.000000E+00 + 1 5 0.000000E+00 + 1 6 0.000000E+00 + 2 1 0.000000E+00 + 2 2 0.000000E+00 + 2 3 0.000000E+00 + 2 4 0.000000E+00 + 2 5 0.000000E+00 + 2 6 0.000000E+00 + 3 1 0.000000E+00 + 3 2 0.000000E+00 + 3 3 2.083175E+02 + 3 4 0.000000E+00 + 3 5 0.000000E+00 + 3 6 0.000000E+00 + 4 1 0.000000E+00 + 4 2 0.000000E+00 + 4 3 0.000000E+00 + 4 4 1.299587E+04 + 4 5 0.000000E+00 + 4 6 0.000000E+00 + 5 1 0.000000E+00 + 5 2 0.000000E+00 + 5 3 0.000000E+00 + 5 4 0.000000E+00 + 5 5 7.710816E+04 + 5 6 0.000000E+00 + 6 1 0.000000E+00 + 6 2 0.000000E+00 + 6 3 0.000000E+00 + 6 4 0.000000E+00 + 6 5 0.000000E+00 + 6 6 0.000000E+00 \ No newline at end of file diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_AeroDyn15.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_AeroDyn15.dat new file mode 100644 index 000000000..707ba8d92 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_AeroDyn15.dat @@ -0,0 +1,123 @@ +------- AERODYN v15 for OpenFAST INPUT FILE --------------------------------------------------------- +Floating MHK turbine hydrodynamic input properties, based on the RM1 tidal current rotor +====== General Options ============================================================================ +False Echo - Echo the input to ".AD.ech"? (flag) +"default" DTAero - Time interval for aerodynamic calculations {or "default"} (s) + 2 WakeMod - Type of wake/induction model (switch) {0=none, 1=BEMT, 2=DBEMT, 3=OLAF} [WakeMod cannot be 2 or 3 when linearizing] + 1 AFAeroMod - Type of blade airfoil aerodynamics model (switch) {1=steady model, 2=Beddoes-Leishman unsteady model} [AFAeroMod must be 1 when linearizing] + 1 TwrPotent - Type tower influence on wind based on potential flow around the tower (switch) {0=none, 1=baseline potential flow, 2=potential flow with Bak correction} + 0 TwrShadow - Calculate tower influence on wind based on downstream tower shadow (switch) {0=none, 1=Powles model, 2=Eames model} +True TwrAero - Calculate tower aerodynamic loads? (flag) +False FrozenWake - Assume frozen wake during linearization? (flag) [used only when WakeMod=1 and when linearizing] +True CavitCheck - Perform cavitation check? (flag) [AFAeroMod must be 1 when CavitCheck=true] +True Buoyancy - Include buoyancy effects? (flag) +False CompAA - Flag to compute AeroAcoustics calculation [used only when WakeMod = 1 or 2] +"unused" AA_InputFile - AeroAcoustics input file [used only when CompAA=true] +====== Environmental Conditions =================================================================== +"default" AirDens - Air density (kg/m^3) +"default" KinVisc - Kinematic viscosity of working fluid (m^2/s) +"default" SpdSound - Speed of sound in working fluid (m/s) +"default" Patm - Atmospheric pressure (Pa) [used only when CavitCheck=True] +"default" Pvap - Vapour pressure of working fluid (Pa) [used only when CavitCheck=True] +====== Blade-Element/Momentum Theory Options ====================================================== [unused when WakeMod=0 or 3] + 2 SkewMod - Type of skewed-wake correction model (switch) {1=uncoupled, 2=Pitt/Peters, 3=coupled} [unused when WakeMod=0 or 3] +"default" SkewModFactor - Constant used in Pitt/Peters skewed wake model {or "default" is 15/32*pi} (-) [used only when SkewMod=2; unused when WakeMod=0 or 3] +True TipLoss - Use the Prandtl tip-loss model? (flag) [unused when WakeMod=0 or 3] +True HubLoss - Use the Prandtl hub-loss model? (flag) [unused when WakeMod=0 or 3] +True TanInd - Include tangential induction in BEMT calculations? (flag) [unused when WakeMod=0 or 3] +True AIDrag - Include the drag term in the axial-induction calculation? (flag) [unused when WakeMod=0 or 3] +True TIDrag - Include the drag term in the tangential-induction calculation? (flag) [unused when WakeMod=0,3 or TanInd=FALSE] +"default" IndToler - Convergence tolerance for BEMT nonlinear solve residual equation {or "default"} (-) [unused when WakeMod=0 or 3] + 1000 MaxIter - Maximum number of iteration steps (-) [unused when WakeMod=0] +====== Dynamic Blade-Element/Momentum Theory Options ============================================== [used only when WakeMod=2] + 2 DBEMT_Mod - Type of dynamic BEMT (DBEMT) model {1=constant tau1, 2=time-dependent tau1} (-) [used only when WakeMod=2] + 4 tau1_const - Time constant for DBEMT (s) [used only when WakeMod=2 and DBEMT_Mod=1] +====== OLAF -- cOnvecting LAgrangian Filaments (Free Vortex Wake) Theory Options ================== [used only when WakeMod=3] +"unused" OLAFInputFileName - Input file for OLAF [used only when WakeMod=3] +====== Beddoes-Leishman Unsteady Airfoil Aerodynamics Options ===================================== [used only when AFAeroMod=2] + 3 UAMod - Unsteady Aero Model Switch (switch) {1=Baseline model (Original), 2=Gonzalez's variant (changes in Cn,Cc,Cm), 3=Minnema/Pierce variant (changes in Cc and Cm)} [used only when AFAeroMod=2] +True FLookup - Flag to indicate whether a lookup for f' will be calculated (TRUE) or whether best-fit exponential equations will be used (FALSE); if FALSE S1-S4 must be provided in airfoil input files (flag) [used only when AFAeroMod=2] +====== Airfoil Information ========================================================================= + 2 AFTabMod - Interpolation method for multiple airfoil tables {1=1D interpolation on AoA (first table only); 2=2D interpolation on AoA and Re; 3=2D interpolation on AoA and UserProp} (-) + 1 InCol_Alfa - The column in the airfoil tables that contains the angle of attack (-) + 2 InCol_Cl - The column in the airfoil tables that contains the lift coefficient (-) + 3 InCol_Cd - The column in the airfoil tables that contains the drag coefficient (-) + 0 InCol_Cm - The column in the airfoil tables that contains the pitching-moment coefficient; use zero if there is no Cm column (-) + 4 InCol_Cpmin - The column in the airfoil tables that contains the Cpmin coefficient; use zero if there is no Cpmin column (-) + 9 NumAFfiles - Number of airfoil files used (-) +"Airfoils/NACA6_1000.dat" AFNames - Airfoil file names (NumAFfiles lines) (quoted strings) +"Airfoils/NACA6_0864.dat" +"Airfoils/NACA6_0629.dat" +"Airfoils/NACA6_0444.dat" +"Airfoils/NACA6_0329.dat" +"Airfoils/NACA6_0276.dat" +"Airfoils/NACA6_0259.dat" +"Airfoils/NACA6_0247.dat" +"Airfoils/NACA6_0240.dat" +====== Rotor/Blade Properties ===================================================================== +False UseBlCm - Include aerodynamic pitching moment in calculations? (flag) +"MHK_RM1_AeroDyn15_Blade.dat" ADBlFile(1) - Name of file containing distributed aerodynamic properties for Blade #1 (-) +"MHK_RM1_AeroDyn15_Blade.dat" ADBlFile(2) - Name of file containing distributed aerodynamic properties for Blade #2 (-) [unused if NumBl < 2] +"unused" ADBlFile(3) - Name of file containing distributed aerodynamic properties for Blade #3 (-) [unused if NumBl < 3] +====== Hub Properties ============================================================================== [used only when Buoyancy=True] +7.2 VolHub - Hub volume (m^3) +0.2222 HubCenBx - Hub center of buoyancy x direction offset (m) +====== Nacelle Properties ========================================================================== [used only when Buoyancy=True] +38.6 VolNac - Nacelle volume (m^3) +0.43,0,0 NacCenB - Position of nacelle center of buoyancy from yaw bearing in nacelle coordinates (m) +====== Tail fin Aerodynamics ======================================================================== +False TFinAero - Calculate tail fin aerodynamics model (flag) +"unused" TFinFile - Input file for tail fin aerodynamics [used only when TFinAero=True] +====== Tower Influence and Aerodynamics ============================================================ [used only when TwrPotent/=0, TwrShadow/=0, TwrAero=True, or Buoyancy=True] + 4 NumTwrNds - Number of tower nodes used in the analysis (-) [used only when TwrPotent/=0, TwrShadow/=0, TwrAero=True, or Buoyancy=True] +TwrElev TwrDiam TwrCd TwrTI TwrCb !TwrTI used only with TwrShadow=2, TwrCb used only with Buoyancy=True +(m) (m) (-) (-) (-) +-9 0.3253 0.2 0.0 1.0 +-14 0.3253 0.2 0.0 1.0 +-19 0.3253 0.2 0.0 1.0 +-24 0.3253 0.2 0.0 1.0 +====== Outputs ==================================================================================== +True SumPrint - Generate a summary file listing input options and interpolated properties to ".AD.sum"? (flag) + 9 NBlOuts - Number of blade node outputs [0 - 9] (-) +1,5,9,13,17,21,25,27,30 BlOutNd - Blade nodes whose values will be output (-) + 4 NTwOuts - Number of tower node outputs [0 - 9] (-) + 1,2,3,4 TwOutNd - Tower nodes whose values will be output (-) + OutList - The next line(s) contains a list of output parameters. See OutListParameters.xlsx for a listing of available output channels. +"TwN1Fbx" - x-component of buoyant force per unit length at Tw node 1 +"TwN3Fby" - y-component of buoyant force per unit length at Tw node 3 +"TwN4Fbz" - z-component of buoyant force per unit length at Tw node 4 +"TwN1Mbx" - x-component of buoyant moment per unit length at Tw node 6 +"TwN2Mby" - y-component of buoyant moment per unit length at Tw node 5 +"TwN3Mbz" - z-component of buoyant moment per unit length at Tw node 2 +"B2N4Fbn" - Buoyant force normal to chord per unit length at blade 2 node 4 +"B1N7Fbt" - Buoyant force tangential to chord per unit length at blade 1 node 7 +"B2N8Fbs" - Buoyant spanwise force per unit length at blade 2 node 8 +"B1N2Mbn" - Buoyant moment normal to chord per unit length at blade 1 node 2 +"B2N3Mbt" - Buoyant moment tangential to chord per unit length at blade 2 node 3 +"B1N6Mbs" - Buoyant spanwise moment per unit length at blade 1 node 6 +"B1FldFz" - Total blade aerodynamic/hydrodynamic load for blade 1 (force in z-direction) +"B2FldMx" - Total blade aerodynamic/hydrodynamic load for blade 2 (moment in x-direction) +"HbFbx" - x-component of buoyant force at hub node +"HbFby" - y-component of buoyant force at hub node +"HbFbz" - z-component of buoyant force at hub node +"HbMbx" - x-component of buoyant moment at hub node +"HbMby" - y-component of buoyant moment at hub node +"HbMbz" - z-component of buoyant moment at hub node +"NcFbx" - x-component of buoyant force at nacelle node +"NcFby" - y-component of buoyant force at nacelle node +"NcFbz" - z-component of buoyant force at nacelle node +"NcMbx" - x-component of buoyant moment at nacelle node +"NcMby" - y-component of buoyant moment at nacelle node +"NcMbz" - z-component of buoyant moment at nacelle node +"RtFldFxh" - Total rotor aerodynamic/hydrodynamic and buoyant load (force in x direction) +"RtFldFyh" - Total rotor aerodynamic/hydrodynamic and buoyant load (force in y direction) +"RtFldFzg" - Total rotor aerodynamic/hydrodynamic and buoyant load (force in global z direction) +"RtFldMxh" - Total rotor aerodynamic/hydrodynamic and buoyant load (moment in x direction) +"RtFldMyg" - Total rotor aerodynamic/hydrodynamic and buoyant load (moment in global y direction) +"RtFldMzh" - Total rotor aerodynamic/hydrodynamic and buoyant load (moment in z direction) +"B1N3SigCr" - Critical cavitation number blade 1 node 3 +"B2N5SigCr" - Critical cavitation number blade 2 node 5 +"B1N2SgCav" - Cavitation number blade 1 node 2 +"B2N6SgCav" - Cavitation number blade 2 node 6 +END of input file (the word "END" must appear in the first 3 columns of this last OutList line) +----------------------------------------------------------------------------------------------------- diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_ElastoDyn.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_ElastoDyn.dat new file mode 100644 index 000000000..207ffc39e --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_ElastoDyn.dat @@ -0,0 +1,134 @@ +------- ELASTODYN for OpenFAST INPUT FILE -------------------------------------- +Floating MHK turbine structural input properties, based on the RM1 tidal current rotor +---------------------- SIMULATION CONTROL -------------------------------------- +False Echo - Echo input data to ".ech" (flag) + 3 Method - Integration method: {1: RK4, 2: AB4, or 3: ABM4} (-) +"default" DT - Integration time step (s) +---------------------- DEGREES OF FREEDOM -------------------------------------- +False FlapDOF1 - First flapwise blade mode DOF (flag) +False FlapDOF2 - Second flapwise blade mode DOF (flag) +False EdgeDOF - First edgewise blade mode DOF (flag) +False TeetDOF - Rotor-teeter DOF (flag) [unused for 3 blades] +False DrTrDOF - Drivetrain rotational-flexibility DOF (flag) +True GenDOF - Generator DOF (flag) +False YawDOF - Yaw DOF (flag) +False TwFADOF1 - First fore-aft tower bending-mode DOF (flag) +False TwFADOF2 - Second fore-aft tower bending-mode DOF (flag) +False TwSSDOF1 - First side-to-side tower bending-mode DOF (flag) +False TwSSDOF2 - Second side-to-side tower bending-mode DOF (flag) +True PtfmSgDOF - Platform horizontal surge translation DOF (flag) +True PtfmSwDOF - Platform horizontal sway translation DOF (flag) +True PtfmHvDOF - Platform vertical heave translation DOF (flag) +True PtfmRDOF - Platform roll tilt rotation DOF (flag) +True PtfmPDOF - Platform pitch tilt rotation DOF (flag) +True PtfmYDOF - Platform yaw rotation DOF (flag) +---------------------- INITIAL CONDITIONS -------------------------------------- + 0 OoPDefl - Initial out-of-plane blade-tip displacement (meters) + 0 IPDefl - Initial in-plane blade-tip deflection (meters) + 0 BlPitch(1) - Blade 1 initial pitch (degrees) + 0 BlPitch(2) - Blade 2 initial pitch (degrees) + 0 BlPitch(3) - Blade 3 initial pitch (degrees) [unused for 2 blades] + 0 TeetDefl - Initial or fixed teeter angle (degrees) [unused for 3 blades] + 0 Azimuth - Initial azimuth angle for blade 1 (degrees) + 11.50 RotSpeed - Initial or fixed rotor speed (rpm) + 0 NacYaw - Initial or fixed nacelle-yaw angle (degrees) + 0 TTDspFA - Initial fore-aft tower-top displacement (meters) + 0 TTDspSS - Initial side-to-side tower-top displacement (meters) + 20 PtfmSurge - Initial or fixed horizontal surge translational displacement of platform (meters) + 0 PtfmSway - Initial or fixed horizontal sway translational displacement of platform (meters) + 0 PtfmHeave - Initial or fixed vertical heave translational displacement of platform (meters) + 0 PtfmRoll - Initial or fixed roll tilt rotational displacement of platform (degrees) + 0 PtfmPitch - Initial or fixed pitch tilt rotational displacement of platform (degrees) + 0 PtfmYaw - Initial or fixed yaw rotational displacement of platform (degrees) +---------------------- TURBINE CONFIGURATION ----------------------------------- + 2 NumBl - Number of blades (-) + 10.0 TipRad - The distance from the rotor apex to the blade tip (meters) + 1.0 HubRad - The distance from the rotor apex to the blade root (meters) + 0.0 PreCone(1) - Blade 1 cone angle (degrees) + 0.0 PreCone(2) - Blade 2 cone angle (degrees) + 0.0 PreCone(3) - Blade 3 cone angle (degrees) [unused for 2 blades] + 0.2222 HubCM - Distance from rotor apex to hub mass [positive downwind] (meters) + 0 UndSling - Undersling length [distance from teeter pin to the rotor apex] (meters) [unused for 3 blades] + 0 Delta3 - Delta-3 angle for teetering rotors (degrees) [unused for 3 blades] + 0 AzimB1Up - Azimuth value to use for I/O when blade 1 points up (degrees) + -4.91 OverHang - Distance from yaw axis to rotor apex [3 blades] or teeter pin [2 blades] (meters) + 0 ShftGagL - Distance from rotor apex [3 blades] or teeter pin [2 blades] to shaft strain gages [positive for upwind rotors] (meters) + 0 ShftTilt - Rotor shaft tilt angle (degrees) + 0.43 NacCMxn - Downwind distance from the tower-top to the nacelle CM (meters) + 0 NacCMyn - Lateral distance from the tower-top to the nacelle CM (meters) + -1.2 NacCMzn - Vertical distance from the tower-top to the nacelle CM (meters) + 0 NcIMUxn - Downwind distance from the tower-top to the nacelle IMU (meters) + 0 NcIMUyn - Lateral distance from the tower-top to the nacelle IMU (meters) + -1.2 NcIMUzn - Vertical distance from the tower-top to the nacelle IMU (meters) + -1.2 Twr2Shft - Vertical distance from the tower-top to the rotor shaft (meters) + -24.0 TowerHt - Height of tower relative to ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] (meters) + -9.0 TowerBsHt - Height of tower base relative to ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] (meters) + 0 PtfmCMxt - Downwind distance from the ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] to the platform CM (meters) + 0 PtfmCMyt - Lateral distance from the ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] to the platform CM (meters) + -6.09 PtfmCMzt - Vertical distance from the ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] to the platform CM (meters) + 0 PtfmRefzt - Vertical distance from the ground level [onshore], MSL [offshore wind or floating MHK], or seabed [fixed MHK] to the platform reference point (meters) +---------------------- MASS AND INERTIA ---------------------------------------- + 0 TipMass(1) - Tip-brake mass, blade 1 (kg) + 0 TipMass(2) - Tip-brake mass, blade 2 (kg) + 0 TipMass(3) - Tip-brake mass, blade 3 (kg) [unused for 2 blades] + 140 HubMass - Hub mass (kg) + 79.6 HubIner - Hub inertia about rotor axis [3 blades] or teeter axis [2 blades] (kg m^2) + 139.50 GenIner - Generator inertia about HSS (kg m^2) + 40100 NacMass - Nacelle mass (kg) + 244643 NacYIner - Nacelle inertia about yaw axis (kg m^2) + 0 YawBrMass - Yaw bearing mass (kg) + 2525214 PtfmMass - Platform mass (kg) + 195242474 PtfmRIner - Platform inertia for roll tilt rotation about the platform CM (kg m^2) + 919435755 PtfmPIner - Platform inertia for pitch tilt rotation about the platform CM (kg m^2) + 1053535885 PtfmYIner - Platform inertia for yaw rotation about the platform CM (kg m^2) +---------------------- BLADE --------------------------------------------------- + 8 BldNodes - Number of blade nodes (per blade) used for analysis (-) +"MHK_RM1_ElastoDyn_Blade.dat" BldFile(1) - Name of file containing properties for blade 1 (quoted string) +"MHK_RM1_ElastoDyn_Blade.dat" BldFile(2) - Name of file containing properties for blade 2 (quoted string) +"unused" BldFile(3) - Name of file containing properties for blade 3 (quoted string) [unused for 2 blades] +---------------------- ROTOR-TEETER -------------------------------------------- + 0 TeetMod - Rotor-teeter spring/damper model {0: none, 1: standard, 2: user-defined from routine UserTeet} (switch) [unused for 3 blades] + 0 TeetDmpP - Rotor-teeter damper position (degrees) [used only for 2 blades and when TeetMod=1] + 0 TeetDmp - Rotor-teeter damping constant (N-m/(rad/s)) [used only for 2 blades and when TeetMod=1] + 0 TeetCDmp - Rotor-teeter rate-independent Coulomb-damping moment (N-m) [used only for 2 blades and when TeetMod=1] + 0 TeetSStP - Rotor-teeter soft-stop position (degrees) [used only for 2 blades and when TeetMod=1] + 0 TeetHStP - Rotor-teeter hard-stop position (degrees) [used only for 2 blades and when TeetMod=1] + 0 TeetSSSp - Rotor-teeter soft-stop linear-spring constant (N-m/rad) [used only for 2 blades and when TeetMod=1] + 0 TeetHSSp - Rotor-teeter hard-stop linear-spring constant (N-m/rad) [used only for 2 blades and when TeetMod=1] +---------------------- DRIVETRAIN ---------------------------------------------- + 92 GBoxEff - Gearbox efficiency (%) + 53 GBRatio - Gearbox ratio (-) + 600000 DTTorSpr - Drivetrain torsional spring (N-m/rad) + 100000 DTTorDmp - Drivetrain torsional damper (N-m/(rad/s)) +---------------------- FURLING ------------------------------------------------- +False Furling - Read in additional model properties for furling turbine (flag) [must currently be FALSE) +"unused" FurlFile - Name of file containing furling properties (quoted string) [unused when Furling=False] +---------------------- TOWER --------------------------------------------------- + 2 TwrNodes - Number of tower nodes used for analysis (-) +"MHK_RM1_ElastoDyn_Tower.dat" TwrFile - Name of file containing tower properties (quoted string) +---------------------- OUTPUT -------------------------------------------------- +True SumPrint - Print summary data to ".sum" (flag) + 1 OutFile - Switch to determine where output will be placed: {1: in module output file only; 2: in glue code output file only; 3: both} (currently unused) +True TabDelim - Use tab delimiters in text tabular output file? (flag) (currently unused) +"ES10.3E2" OutFmt - Format used for text tabular output (except time). Resulting field should be 10 characters. (quoted string) (currently unused) + 0 TStart - Time to begin tabular output (s) (currently unused) + 1 DecFact - Decimation factor for tabular output {1: output every time step} (-) (currently unused) + 0 NTwGages - Number of tower nodes that have strain gages for output [0 to 9] (-) + 0 TwrGagNd - List of tower nodes that have strain gages [1 to TwrNodes] (-) [unused if NTwGages=0] + 0 NBlGages - Number of blade nodes that have strain gages for output [0 to 9] (-) + 0 BldGagNd - List of blade nodes that have strain gages [1 to BldNodes] (-) [unused if NBlGages=0] + OutList - The next line(s) contains a list of output parameters. See OutListParameters.xlsx for a listing of available output channels, (-) +"PtfmSurge" +"PtfmSway" +"PtfmHeave" +"PtfmRoll" +"PtfmPitch" +"PtfmYaw" +"TwrTpTDxi" +"TwrTpTDyi" +"TwrTpTDzi" +"OoPDefl1" +"RotSpeed" +"GenSpeed" +END of input file (the word "END" must appear in the first 3 columns of this last OutList line) +-------------------------------------------------------------------------------- diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_HydroDyn.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_HydroDyn.dat new file mode 100644 index 000000000..0f893afb7 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_HydroDyn.dat @@ -0,0 +1,207 @@ +------- HydroDyn Input File ---------------------------------------------------- +Floating MHK turbine hydrodynamic support structure input properties, based on the RM1 tidal current rotor with a quad-style floating platform +False Echo - Echo the input file data (flag) +---------------------- ENVIRONMENTAL CONDITIONS -------------------------------- +"DEFAULT" WtrDens - Water density (kg/m^3) +"DEFAULT" WtrDpth - Water depth (meters) +"DEFAULT" MSL2SWL - Offset between still-water level and mean sea level (meters) [positive upward; unused when WaveMod = 6; must be zero if PotMod=1 or 2] +---------------------- WAVES --------------------------------------------------- + 1 WaveMod - Incident wave kinematics model {0: none=still water, 1: regular (periodic), 1P#: regular with user-specified phase, 2: JONSWAP/Pierson-Moskowitz spectrum (irregular), 3: White noise spectrum (irregular), 4: user-defined spectrum from routine UserWaveSpctrm (irregular), 5: Externally generated wave-elevation time series, 6: Externally generated full wave-kinematics time series [option 6 is invalid for PotMod/=0]} (switch) + 0 WaveStMod - Model for stretching incident wave kinematics to instantaneous free surface {0: none=no stretching, 1: vertical stretching, 2: extrapolation stretching, 3: Wheeler stretching} (switch) [unused when WaveMod=0 or when PotMod/=0] + 3600 WaveTMax - Analysis time for incident wave calculations (sec) [unused when WaveMod=0; determines WaveDOmega=2Pi/WaveTMax in the IFFT] + 0.1 WaveDT - Time step for incident wave calculations (sec) [unused when WaveMod=0; 0.1<=WaveDT<=1.0 recommended; determines WaveOmegaMax=Pi/WaveDT in the IFFT] + 1.0 WaveHs - Significant wave height of incident waves (meters) [used only when WaveMod=1, 2, or 3] + 10 WaveTp - Peak-spectral period of incident waves (sec) [used only when WaveMod=1 or 2] +"DEFAULT" WavePkShp - Peak-shape parameter of incident wave spectrum (-) or DEFAULT (string) [used only when WaveMod=2; use 1.0 for Pierson-Moskowitz] + 0.314159 WvLowCOff - Low cut-off frequency or lower frequency limit of the wave spectrum beyond which the wave spectrum is zeroed (rad/s) [unused when WaveMod=0, 1, or 6] + 1.570796 WvHiCOff - High cut-off frequency or upper frequency limit of the wave spectrum beyond which the wave spectrum is zeroed (rad/s) [unused when WaveMod=0, 1, or 6] + 0 WaveDir - Incident wave propagation heading direction (degrees) [unused when WaveMod=0 or 6] + 0 WaveDirMod - Directional spreading function {0: none, 1: COS2S} (-) [only used when WaveMod=2,3, or 4] + 1 WaveDirSpread - Wave direction spreading coefficient ( > 0 ) (-) [only used when WaveMod=2,3, or 4 and WaveDirMod=1] + 1 WaveNDir - Number of wave directions (-) [only used when WaveMod=2,3, or 4 and WaveDirMod=1; odd number only] + 0 WaveDirRange - Range of wave directions (full range: WaveDir +/- 1/2*WaveDirRange) (degrees) [only used when WaveMod=2,3,or 4 and WaveDirMod=1] + 123456789 WaveSeed(1) - First random seed of incident waves [-2147483648 to 2147483647] (-) [unused when WaveMod=0, 5, or 6] + RANLUX WaveSeed(2) - Second random seed of incident waves [-2147483648 to 2147483647] for intrinsic pRNG, or an alternative pRNG: "RanLux" (-) [unused when WaveMod=0, 5, or 6] +FALSE WaveNDAmp - Flag for normally distributed amplitudes (flag) [only used when WaveMod=2, 3, or 4] +"" WvKinFile - Root name of externally generated wave data file(s) (quoted string) [used only when WaveMod=5 or 6] + 1 NWaveElev - Number of points where the incident wave elevations can be computed (-) [maximum of 9 output locations] + 0 WaveElevxi - List of xi-coordinates for points where the incident wave elevations can be output (meters) [NWaveElev points, separated by commas or white space; usused if NWaveElev = 0] + 0 WaveElevyi - List of yi-coordinates for points where the incident wave elevations can be output (meters) [NWaveElev points, separated by commas or white space; usused if NWaveElev = 0] +---------------------- 2ND-ORDER WAVES ----------------------------------------- [unused with WaveMod=0 or 6] +FALSE WvDiffQTF - Full difference-frequency 2nd-order wave kinematics (flag) +FALSE WvSumQTF - Full summation-frequency 2nd-order wave kinematics (flag) + 0 WvLowCOffD - Low frequency cutoff used in the difference-frequencies (rad/s) [Only used with a difference-frequency method] + 1.256637 WvHiCOffD - High frequency cutoff used in the difference-frequencies (rad/s) [Only used with a difference-frequency method] + 0.618319 WvLowCOffS - Low frequency cutoff used in the summation-frequencies (rad/s) [Only used with a summation-frequency method] + 3.141593 WvHiCOffS - High frequency cutoff used in the summation-frequencies (rad/s) [Only used with a summation-frequency method] +---------------------- CURRENT ------------------------------------------------- [unused with WaveMod=6] + 1 CurrMod - Current profile model {0: none=no current, 1: standard, 2: user-defined from routine UserCurrent} (switch) + 0 CurrSSV0 - Sub-surface current velocity at still water level (m/s) [used only when CurrMod=1] + 0 CurrSSDir - Sub-surface current heading direction (degrees) or DEFAULT (string) [used only when CurrMod=1] + 12.2 CurrNSRef - Near-surface current reference depth (meters) [used only when CurrMod=1] + 0 CurrNSV0 - Near-surface current velocity at still water level (m/s) [used only when CurrMod=1] + 0 CurrNSDir - Near-surface current heading direction (degrees) [used only when CurrMod=1] + 1.9 CurrDIV - Depth-independent current velocity (m/s) [used only when CurrMod=1] + 0 CurrDIDir - Depth-independent current heading direction (degrees) [used only when CurrMod=1] +---------------------- FLOATING PLATFORM --------------------------------------- [unused with WaveMod=6] + 1 PotMod - Potential-flow model {0: none=no potential flow, 1: frequency-to-time-domain transforms based on WAMIT output, 2: fluid-impulse theory (FIT)} (switch) + 1 ExctnMod - Wave-excitation model {0: no wave-excitation calculation, 1: DFT, 2: state-space} (switch) [only used when PotMod=1; STATE-SPACE REQUIRES *.ssexctn INPUT FILE] + 1 RdtnMod - Radiation memory-effect model {0: no memory-effect calculation, 1: convolution, 2: state-space} (switch) [only used when PotMod=1; STATE-SPACE REQUIRES *.ss INPUT FILE] + 0 RdtnTMax - Analysis time for wave radiation kernel calculations (sec) [only used when PotMod=1 and RdtnMod=1; determines RdtnDOmega=Pi/RdtnTMax in the cosine transform; MAKE SURE THIS IS LONG ENOUGH FOR THE RADIATION IMPULSE RESPONSE FUNCTIONS TO DECAY TO NEAR-ZERO FOR THE GIVEN PLATFORM!] + "DEFAULT" RdtnDT - Time step for wave radiation kernel calculations (sec) [only used when PotMod=1 and ExctnMod>1 or RdtnMod>0; DT<=RdtnDT<=0.1 recommended; determines RdtnOmegaMax=Pi/RdtnDT in the cosine transform] + 1 NBody - Number of WAMIT bodies to be used (-) [>=1; only used when PotMod=1. If NBodyMod=1, the WAMIT data contains a vector of size 6*NBody x 1 and matrices of size 6*NBody x 6*NBody; if NBodyMod>1, there are NBody sets of WAMIT data each with a vector of size 6 x 1 and matrices of size 6 x 6] + 2 NBodyMod - Body coupling model {1: include coupling terms between each body and NBody in HydroDyn equals NBODY in WAMIT, 2: neglect coupling terms between each body and NBODY=1 with XBODY=0 in WAMIT, 3: Neglect coupling terms between each body and NBODY=1 with XBODY=/0 in WAMIT} (switch) [only used when PotMod=1] +"MHK_RM1_Floating" PotFile - Root name of potential-flow model data; WAMIT output files containing the linear, nondimensionalized, hydrostatic restoring matrix (.hst), frequency-dependent hydrodynamic added mass matrix and damping matrix (.1), and frequency- and direction-dependent wave excitation force vector per unit wave amplitude (.3) (quoted string) [1 to NBody if NBodyMod>1] [only used when PotMod=1 and ExctnMod>0 or RdtnMod>0] [MAKE SURE THE FREQUENCIES INHERENT IN THESE WAMIT FILES SPAN THE PHYSICALLY-SIGNIFICANT RANGE OF FREQUENCIES FOR THE GIVEN PLATFORM; THEY MUST CONTAIN THE ZERO- AND INFINITE-FREQUENCY LIMITS!] + 1 WAMITULEN - Characteristic body length scale used to redimensionalize WAMIT output (meters) [1 to NBody if NBodyMod>1] [only used when PotMod=1 and ExctnMod=1 or RdtnMod=1] + 0 PtfmRefxt - The xt offset of the body reference point(s) from (0,0,0) (meters) [1 to NBody] [only used when PotMod=1] + 0 PtfmRefyt - The yt offset of the body reference point(s) from (0,0,0) (meters) [1 to NBody] [only used when PotMod=1] + 0 PtfmRefzt - The zt offset of the body reference point(s) from (0,0,0) (meters) [1 to NBody] [only used when PotMod=1. If NBodyMod=2,PtfmRefzt=0.0] + 0 PtfmRefztRot - The rotation about zt of the body reference frame(s) from xt/yt (degrees) [1 to NBody] [only used when PotMod=1] + 2671.85 PtfmVol0 - **Note - 2672.6 from WAMIT with -0.748 correction for missing tower base hydrostatic pressure ** Displaced volume of water when the body is in its undisplaced position (m^3) [1 to NBody] [only used when PotMod=1; USE THE SAME VALUE COMPUTED BY WAMIT AS OUTPUT IN THE .OUT FILE!] + 0 PtfmCOBxt - The xt offset of the center of buoyancy (COB) from (0,0) (meters) [1 to NBody] [only used when PotMod=1] + 0 PtfmCOByt - The yt offset of the center of buoyancy (COB) from (0,0) (meters) [1 to NBody] [only used when PotMod=1] +---------------------- 2ND-ORDER FLOATING PLATFORM FORCES ---------------------- [unused with WaveMod=0 or 6, or PotMod=0 or 2] + 0 MnDrift - Mean-drift 2nd-order forces computed {0: None; [7, 8, 9, 10, 11, or 12]: WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero. If NBody>1, MnDrift /=8] + 0 NewmanApp - Mean- and slow-drift 2nd-order forces computed with Newman's approximation {0: None; [7, 8, 9, 10, 11, or 12]: WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero. If NBody>1, NewmanApp/=8. Used only when WaveDirMod=0] + 0 DiffQTF - Full difference-frequency 2nd-order forces computed with full QTF {0: None; [10, 11, or 12]: WAMIT file to use} [Only one of MnDrift, NewmanApp, or DiffQTF can be non-zero] + 0 SumQTF - Full summation -frequency 2nd-order forces computed with full QTF {0: None; [10, 11, or 12]: WAMIT file to use} +---------------------- PLATFORM ADDITIONAL STIFFNESS AND DAMPING -------------- [unused with PotMod=0 or 2] + 0 AddF0 - Additional preload (N, N-m) [If NBodyMod=1, one size 6*NBody x 1 vector; if NBodyMod>1, NBody size 6 x 1 vectors] + 0 + 0 + 0 + 0 + 0 + 0 0 0 0 0 0 AddCLin - Additional linear stiffness (N/m, N/rad, N-m/m, N-m/rad) [If NBodyMod=1, one size 6*NBody x 6*NBody matrix; if NBodyMod>1, NBody size 6 x 6 matrices] + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 AddBLin - Additional linear damping(N/(m/s), N/(rad/s), N-m/(m/s), N-m/(rad/s)) [If NBodyMod=1, one size 6*NBody x 6*NBody matrix; if NBodyMod>1, NBody size 6 x 6 matrices] + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 AddBQuad - Additional quadratic drag(N/(m/s)^2, N/(rad/s)^2, N-m(m/s)^2, N-m/(rad/s)^2) [If NBodyMod=1, one size 6*NBody x 6*NBody matrix; if NBodyMod>1, NBody size 6 x 6 matrices] + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 + 0 0 0 0 0 0 +---------------------- AXIAL COEFFICIENTS -------------------------------------- + 2 NAxCoef - Number of axial coefficients (-) +AxCoefID AxCd AxCa AxCp +(-) (-) (-) (-) +1 0.00 0.00 0.00 ! Columns / Braces (no exposed member ends) +2 1.00 1.00 1.00 ! Heave Plates +---------------------- MEMBER JOINTS ------------------------------------------- + 33 NJoints - Number of joints (-) [must be exactly 0 or at least 2] +JointID Jointxi Jointyi Jointzi JointAxID JointOvrlp [JointOvrlp= 0: do nothing at joint, 1: eliminate overlaps by calculating super member] +(-) (m) (m) (m) (-) (switch) +0 28.0 0 -10 1 0 ! Downstream Column +1 28.0 0 6 1 0 +2 -28.0 0 -10 1 0 ! Upstream Column +3 -28.0 0 6 1 0 +4 0 -12.0 -10 1 0 ! Starboard Column +5 0 -12.0 6 1 0 +6 0 12.0 -10 1 0 ! Port Column +7 0 12.0 6 1 0 +8 3.67658 10.4243 4.5 1 0 ! Upper Braces +9 24.3234 1.57568 4.5 1 0 +10 3.67658 -10.4243 4.5 1 0 +11 24.3234 -1.57568 4.5 1 0 +12 -3.67658 10.4243 4.5 1 0 +13 -24.3234 1.57568 4.5 1 0 +14 -3.67658 -10.4243 4.5 1 0 +15 -24.3234 -1.57568 4.5 1 0 +16 3.67658 10.4243 -8.5 1 0 ! Lower Braces +17 24.3234 1.57568 -8.5 1 0 +18 3.67658 -10.4243 -8.5 1 0 +19 24.3234 -1.57568 -8.5 1 0 +20 -3.67658 10.4243 -8.5 1 0 +21 -24.3234 1.57568 -8.5 1 0 +22 -3.67658 -10.4243 -8.5 1 0 +23 -24.3234 -1.57568 -8.5 1 0 +24 0 -8 4.5 1 0 ! Tower Braces +25 0 8 4.5 1 0 +26 0 -8 -8.5 1 0 +27 0 8 -8.5 1 0 +28 0 0 -8.5 1 0 +29 28.0 0 -10.5 2 0 ! Heave Plates +30 -28.0 0 -10.5 2 0 +31 0 -12.0 -10.5 2 0 +32 0 12.0 -10.5 2 0 +---------------------- MEMBER CROSS-SECTION PROPERTIES ------------------------- + 4 NPropSets - Number of member property sets (-) +PropSetID PropD PropThck +(-) (m) (m) +0 8.0 0.02 ! Columns +1 2.0 0.02 ! Braces +2 2.0 0.081 ! Flooded Braces (not flooded in hydrodyn) +3 12.0 0.3925 ! Flooded Heave Plates (not flooded in hydrodyn) +---------------------- SIMPLE HYDRODYNAMIC COEFFICIENTS (model 1) -------------- +SimplCd SimplCdMG SimplCa SimplCaMG SimplCp SimplCpMG SimplAxCd SimplAxCdMG SimplAxCa SimplAxCaMG SimplAxCp SimplAxCpMG +(-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) +1.2 0.00 1.00 0.00 1.00 1.00 0.00 0.00 0.00 0.00 1.00 1.00 +---------------------- DEPTH-BASED HYDRODYNAMIC COEFFICIENTS (model 2) --------- + 0 NCoefDpth - Number of depth-dependent coefficients (-) +Dpth DpthCd DpthCdMG DpthCa DpthCaMG DpthCp DpthCpMG DpthAxCd DpthAxCdMG DpthAxCa DpthAxCaMG DpthAxCp DpthAxCpMG +(m) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) +---------------------- MEMBER-BASED HYDRODYNAMIC COEFFICIENTS (model 3) -------- + 0 NCoefMembers - Number of member-based coefficients (-) +MemberID MemberCd1 MemberCd2 MemberCdMG1 MemberCdMG2 MemberCa1 MemberCa2 MemberCaMG1 MemberCaMG2 MemberCp1 MemberCp2 MemberCpMG1 MemberCpMG2 MemberAxCd1 MemberAxCd2 MemberAxCdMG1 MemberAxCdMG2 MemberAxCa1 MemberAxCa2 MemberAxCaMG1 MemberAxCaMG2 MemberAxCp1 MemberAxCp2 MemberAxCpMG1 MemberAxCpMG2 +(-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) (-) +-------------------- MEMBERS --------------------------------------------------- + 20 NMembers - Number of members (-) +MemberID MJointID1 MJointID2 MPropSetID1 MPropSetID2 MDivSize MCoefMod PropPot [MCoefMod=1: use simple coeff table, 2: use depth-based coeff table, 3: use member-based coeff table] [ PropPot/=0 if member is modeled with potential-flow theory] +(-) (-) (-) (-) (-) (m) (switch) (flag) +0 0 1 0 0 0.1 1 TRUE ! Columns +1 2 3 0 0 0.1 1 TRUE +2 4 5 0 0 0.1 1 TRUE +3 6 7 0 0 0.1 1 TRUE +4 8 9 1 1 0.1 1 TRUE ! Upper Braces +5 10 11 1 1 0.1 1 TRUE +6 12 13 1 1 0.1 1 TRUE +7 14 15 1 1 0.1 1 TRUE +8 16 17 2 2 0.1 1 TRUE ! Lower Braces +9 18 19 2 2 0.1 1 TRUE +10 20 21 2 2 0.1 1 TRUE +11 22 23 2 2 0.1 1 TRUE +12 24 25 1 1 0.1 1 TRUE ! Tower Braces +13 26 27 1 1 0.1 1 TRUE +14 24 28 1 1 0.1 1 TRUE +15 25 28 1 1 0.1 1 TRUE +16 0 29 3 3 0.1 1 TRUE ! Heave Plates +17 2 30 3 3 0.1 1 TRUE +18 4 31 3 3 0.1 1 TRUE +19 6 32 3 3 0.1 1 TRUE +---------------------- FILLED MEMBERS ------------------------------------------ + 0 NFillGroups - Number of filled member groups (-) [If FillDens = DEFAULT, then FillDens = WtrDens; FillFSLoc is related to MSL2SWL] +FillNumM FillMList FillFSLoc FillDens +(-) (-) (m) (kg/m^3) +---------------------- MARINE GROWTH ------------------------------------------- + 0 NMGDepths - Number of -growth depths specified (-) +MGDpth MGThck MGDens +(m) (m) (kg/m^3) +---------------------- MEMBER OUTPUT LIST -------------------------------------- + 0 NMOutputs - Number of member outputs (-) [must be < 10] +MemberID NOutLoc NodeLocs [NOutLoc < 10; node locations are normalized distance from the start of the member, and must be >=0 and <= 1] [unused if NMOutputs=0] +(-) (-) (-) +---------------------- JOINT OUTPUT LIST --------------------------------------- + 0 NJOutputs - Number of joint outputs [Must be < 10] + 0 JOutLst - List of JointIDs which are to be output (-)[unused if NJOutputs=0] +---------------------- OUTPUT -------------------------------------------------- +True HDSum - Output a summary file [flag] +False OutAll - Output all user-specified member and joint loads (only at each member end, not interior locations) [flag] + 2 OutSwtch - Output requested channels to: [1=Hydrodyn.out, 2=GlueCode.out, 3=both files] +"E15.7e2" OutFmt - Output format for numerical results (quoted string) [not checked for validity!] +"A11" OutSFmt - Output format for header strings (quoted string) [not checked for validity!] +---------------------- OUTPUT CHANNELS ----------------------------------------- +"Wave1Elev" - Wave elevation at the platform reference point (0, 0) +"HydroFxi" - Buoyancy force [N] in the X direction. +"HydroFyi" - Buoyancy force [N] in the Y direction. +"HydroFzi" - Buoyancy force [N] in the vertical direction (Z). +END of output channels and end of file. (the word "END" must appear in the first 3 columns of this line) + diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_InflowWind.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_InflowWind.dat new file mode 100644 index 000000000..73c0b2f76 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_InflowWind.dat @@ -0,0 +1,69 @@ +------- InflowWind INPUT FILE --------------------------------------------------------------------------------- +Steady 1.9 m/s inflow for floating MHK turbine, based on the RM1 tidal current rotor +--------------------------------------------------------------------------------------------------------------- +False Echo - Echo input data to .ech (flag) + 1 WindType - Switch for wind file type (1=steady; 2=uniform; 3=binary TurbSim FF; 4=binary Bladed-style FF; 5=HAWC format; 6=User defined; 7=native Bladed FF) + 0 PropagationDir - Direction of wind propagation (meteorological rotation from aligned with X (positive rotates towards -Y) -- degrees) (not used for native Bladed format WindType=7) + 0 VFlowAng - Upflow angle (degrees) (not used for native Bladed format WindType=7) +False VelInterpCubic - Use cubic interpolation for velocity in time (false=linear, true=cubic) [Used with WindType=2,3,4,5,7] + 1 NWindVel - Number of points to output the wind velocity (0 to 9) + 0 WindVxiList - List of coordinates in the inertial X direction (m) + 0 WindVyiList - List of coordinates in the inertial Y direction (m) + 12.2 WindVziList - List of coordinates in the inertial Z direction (m) +================== Parameters for Steady Wind Conditions [used only for WindType = 1] ======================= + 1.9 HWindSpeed - Horizontal wind speed (m/s) + 12.2 RefHt - Reference height for horizontal wind speed (m) + 0 PLExp - Power law exponent (-) +================== Parameters for Uniform wind file [used only for WindType = 2] ============================ +"unused" Filename_Uni - Filename of time series data for uniform wind field. (-) + 30 RefHt_Uni - Reference height for horizontal wind speed (m) + 125.88 RefLength - Reference length for linear horizontal and vertical sheer (-) +================== Parameters for Binary TurbSim Full-Field files [used only for WindType = 3] ============== +"unused" FileName_BTS - Name of the Full field wind file to use (.bts) +================== Parameters for Binary Bladed-style Full-Field files [used only for WindType = 4 or WindType = 7] ======= +"unused" FileNameRoot - WindType=4: Rootname of the full-field wind file to use (.wnd, .sum); WindType=7: name of the intermediate file with wind scaling values +False TowerFile - Have tower file (.twr) (flag) ignored when WindType = 7 +================== Parameters for HAWC-format binary files [Only used with WindType = 5] ==================== +"unused" FileName_u - name of the file containing the u-component fluctuating wind (.bin) +"unused" FileName_v - name of the file containing the v-component fluctuating wind (.bin) +"unused" FileName_w - name of the file containing the w-component fluctuating wind (.bin) + 64 nx - number of grids in the x direction (in the 3 files above) (-) + 32 ny - number of grids in the y direction (in the 3 files above) (-) + 32 nz - number of grids in the z direction (in the 3 files above) (-) + 16 dx - distance (in meters) between points in the x direction (m) + 3 dy - distance (in meters) between points in the y direction (m) + 3 dz - distance (in meters) between points in the z direction (m) + 90 RefHt_Hawc - reference height; the height (in meters) of the vertical center of the grid (m) + ------------- Scaling parameters for turbulence --------------------------------------------------------- + 1 ScaleMethod - Turbulence scaling method [0 = none, 1 = direct scaling, 2 = calculate scaling factor based on a desired standard deviation] + 1 SFx - Turbulence scaling factor for the x direction (-) [ScaleMethod=1] + 1 SFy - Turbulence scaling factor for the y direction (-) [ScaleMethod=1] + 1 SFz - Turbulence scaling factor for the z direction (-) [ScaleMethod=1] + 1 SigmaFx - Turbulence standard deviation to calculate scaling from in x direction (m/s) [ScaleMethod=2] + 1 SigmaFy - Turbulence standard deviation to calculate scaling from in y direction (m/s) [ScaleMethod=2] + 1 SigmaFz - Turbulence standard deviation to calculate scaling from in z direction (m/s) [ScaleMethod=2] + ------------- Mean wind profile parameters (added to HAWC-format files) --------------------------------- + 5 URef - Mean u-component wind speed at the reference height (m/s) + 2 WindProfile - Wind profile type (0=constant;1=logarithmic,2=power law) + 0 PLExp_Hawc - Power law exponent (-) (used for PL wind profile type only) + 0.03 Z0 - Surface roughness length (m) (used for LG wind profile type only) + 0 XOffset - Initial offset in +x direction (shift of wind box) +================== LIDAR Parameters =========================================================================== +0 SensorType - Switch for lidar configuration (0 = None, 1 = Single Point Beam(s), 2 = Continuous, 3 = Pulsed) +0 NumPulseGate - Number of lidar measurement gates (used when SensorType = 3) +30 PulseSpacing - Distance between range gates (m) (used when SensorType = 3) +0 NumBeam - Number of lidar measurement beams (0-5)(used when SensorType = 1) +-200 FocalDistanceX - Focal distance co-ordinates of the lidar beam in the x direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) +0 FocalDistanceY - Focal distance co-ordinates of the lidar beam in the y direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) +0 FocalDistanceZ - Focal distance co-ordinates of the lidar beam in the z direction (relative to hub height) (only first coordinate used for SensorType 2 and 3) (m) +0.0 0.0 0.0 RotorApexOffsetPos - Offset of the lidar from hub height (m) +17 URefLid - Reference average wind speed for the lidar[m/s] +0.25 MeasurementInterval - Time between each measurement [s] +False LidRadialVel - TRUE => return radial component, FALSE => return 'x' direction estimate +1 ConsiderHubMotion - Flag whether to consider the hub motion's impact on Lidar measurements +================== OUTPUT ===================================================================================== +True SumPrint - Print summary data to .IfW.sum (flag) + OutList - The next line(s) contains a list of output parameters. See OutListParameters.xlsx for a listing of available output channels, (-) +Wind1VelX +END of input file (the word "END" must appear in the first 3 columns of this last OutList line) +--------------------------------------------------------------------------------------------------------------- diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_MoorDyn.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_MoorDyn.dat new file mode 100644 index 000000000..89e416dc7 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_Floating_MoorDyn.dat @@ -0,0 +1,58 @@ +--------------------- MoorDyn Input File ------------------------------------ +Floating MHK turbine mooring input properties, based on the RM1 tidal current rotor with a quad-style floating platform +FALSE Echo - echo the input file data (flag) +----------------------- LINE TYPES Chain studless 0.018m -------------------- +Name Diam MassDen EA BA/-zeta EI Cd Ca CdAx CaAx +(-) (m) (kg/m) (N) (N-s/-) (-) (-) (-) (-) (-) +main 0.324 644.8 85.4e8 -0.8 0.8 2.4 1.0 1.15 0.5 +---------------------- POINTS ----------------------------------------------- +Node Type X Y Z M V CdA CA +(-) (-) (m) (m) (m) (kg) (m^3) (m^2) (-) +1 Fixed -152.0 -50.0 -50.0 0 0 0 0 +2 Fixed -152.0 0.0 -50.0 0 0 0 0 +3 Fixed -152.0 50.0 -50.0 0 0 0 0 +4 Fixed 152.0 -50.0 -50.0 0 0 0 0 +5 Fixed 152.0 0.0 -50.0 0 0 0 0 +6 Fixed 152.0 50.0 -50.0 0 0 0 0 +7 Vessel -34.0 0.0 -10.0 0 0 0 0 +8 Vessel -34.0 0.0 -10.0 0 0 0 0 +9 Vessel -34.0 0.0 -10.0 0 0 0 0 +10 Vessel 34.0 0.0 -10.0 0 0 0 0 +11 Vessel 34.0 0.0 -10.0 0 0 0 0 +12 Vessel 34.0 0.0 -10.0 0 0 0 0 +---------------------- LINES ------------------------------------------------ +Line LineType AttachA AttachB UnstrLen NumSegs Outputs +(-) (-) (-) (-) (m) (-) (-) +1 main 1 7 160.0 30 - +2 main 2 8 152.0 30 - +3 main 3 9 160.0 30 - +4 main 4 10 160.0 30 - +5 main 5 11 152.0 30 - +6 main 6 12 160.0 30 - +---------------------- SOLVER OPTIONS --------------------------------------- +0.5e-4 dtM - time step to use in mooring integration (s) +3.0e6 kbot - bottom stiffness (Pa/m) +3.0e5 cbot - bottom damping (Pa-s/m) +1.0 dtIC - time interval for analyzing convergence during IC gen (s) +10.0 TmaxIC - max time for ic gen (s) +4.0 CdScaleIC - factor by which to scale drag coefficients during dynamic relaxation (-) +0.1 threshIC - threshold for IC convergence (-) +------------------------ OUTPUTS -------------------------------------------- +FairTen1 FairTen2 FairTen3 FairTen4 FairTen5 FairTen6 +AnchTen1 AnchTen2 AnchTen3 AnchTen4 AnchTen5 AnchTen6 +Con1fX Con1fY Con1fZ +Con2fX Con2fY Con2fZ +Con3fX Con3fY Con3fZ +Con4fX Con4fY Con4fZ +Con5fX Con5fY Con5fZ +Con6fX Con6fY Con6fZ +Con7fX Con7fY Con7fZ Con7pX Con7pY Con7pZ +Con8fX Con8fY Con8fZ Con8pX Con8pY Con8pZ +Con9fX Con9fY Con9fZ Con9pX Con9pY Con9pZ +Con10fX Con10fY Con10fZ Con10pX Con10pY Con10pZ +Con11fX Con11fY Con11fZ Con11pX Con11pY Con11pZ +Con12fX Con12fY Con12fZ Con12pX Con12pY Con12pZ +L1N1pZ L1N2pZ L1N3pZ L1N4pZ L1N5pZ L1N6pZ L1N7pZ L1N8pZ L1N9pZ L1N10pZ L1N11pZ L1N12pZ L1N13pZ L1N14pZ L1N15pZ L1N16pZ L1N17pZ L1N18pZ L1N19pZ L1N20pZ L1N21pZ L1N22pZ L1N23pZ L1N24pZ L1N25pZ L1N26pZ L1N27pZ L1N28pZ L1N29pZ L1N30pZ +L2N1pZ L2N2pZ L2N3pZ L2N4pZ L2N5pZ L2N6pZ L2N7pZ L2N8pZ L2N9pZ L2N10pZ L2N11pZ L2N12pZ L2N13pZ L2N14pZ L2N15pZ L2N16pZ L2N17pZ L2N18pZ L2N19pZ L2N20pZ L2N21pZ L2N22pZ L2N23pZ L2N24pZ L2N25pZ L2N26pZ L2N27pZ L2N28pZ L2N29pZ L2N30pZ +END +------------------------- need this line ------------------------------------ diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ServoDyn.dat b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ServoDyn.dat new file mode 100644 index 000000000..1e9b8ff82 --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/MHK_RM1_ServoDyn.dat @@ -0,0 +1,111 @@ +------- SERVODYN v1.05.* INPUT FILE -------------------------------------------- +NREL 5.0 MW Baseline Wind Turbine for Use in Offshore Analysis. Properties from Dutch Offshore Wind Energy Converter (DOWEC) 6MW Pre-Design (10046_009.pdf) and REpower 5M 5MW (5m_uk.pdf) +---------------------- SIMULATION CONTROL -------------------------------------- +False Echo - Echo input data to .ech (flag) +"default" DT - Communication interval for controllers (s) (or "default") +---------------------- PITCH CONTROL ------------------------------------------- + 5 PCMode - Pitch control mode {0: none, 3: user-defined from routine PitchCntrl, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) + 0 TPCOn - Time to enable active pitch control (s) [unused when PCMode=0] + 9999.9 TPitManS(1) - Time to start override pitch maneuver for blade 1 and end standard pitch control (s) + 9999.9 TPitManS(2) - Time to start override pitch maneuver for blade 2 and end standard pitch control (s) + 9999.9 TPitManS(3) - Time to start override pitch maneuver for blade 3 and end standard pitch control (s) [unused for 2 blades] + 2 PitManRat(1) - Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 1 (deg/s) + 2 PitManRat(2) - Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 2 (deg/s) + 2 PitManRat(3) - Pitch rate at which override pitch maneuver heads toward final pitch angle for blade 3 (deg/s) [unused for 2 blades] + 0 BlPitchF(1) - Blade 1 final pitch for pitch maneuvers (degrees) + 0 BlPitchF(2) - Blade 2 final pitch for pitch maneuvers (degrees) + 0 BlPitchF(3) - Blade 3 final pitch for pitch maneuvers (degrees) [unused for 2 blades] +---------------------- GENERATOR AND TORQUE CONTROL ---------------------------- + 5 VSContrl - Variable-speed control mode {0: none, 1: simple VS, 3: user-defined from routine UserVSCont, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) + 2 GenModel - Generator model {1: simple, 2: Thevenin, 3: user-defined from routine UserGen} (switch) [used only when VSContrl=0] + 94.4 GenEff - Generator efficiency [ignored by the Thevenin and user-defined generator models] (%) +True GenTiStr - Method to start the generator {T: timed using TimGenOn, F: generator speed using SpdGenOn} (flag) +True GenTiStp - Method to stop the generator {T: timed using TimGenOf, F: when generator power = 0} (flag) + 9999.9 SpdGenOn - Generator speed to turn on the generator for a startup (HSS speed) (rpm) [used only when GenTiStr=False] + 0 TimGenOn - Time to turn on the generator for a startup (s) [used only when GenTiStr=True] + 9999.9 TimGenOf - Time to turn off the generator (s) [used only when GenTiStp=True] +---------------------- SIMPLE VARIABLE-SPEED TORQUE CONTROL -------------------- + 9999.9 VS_RtGnSp - Rated generator speed for simple variable-speed generator control (HSS side) (rpm) [used only when VSContrl=1] + 9999.9 VS_RtTq - Rated generator torque/constant generator torque in Region 3 for simple variable-speed generator control (HSS side) (N-m) [used only when VSContrl=1] + 9999.9 VS_Rgn2K - Generator torque constant in Region 2 for simple variable-speed generator control (HSS side) (N-m/rpm^2) [used only when VSContrl=1] + 9999.9 VS_SlPc - Rated generator slip percentage in Region 2 1/2 for simple variable-speed generator control (%) [used only when VSContrl=1] +---------------------- SIMPLE INDUCTION GENERATOR ------------------------------ + 9999.9 SIG_SlPc - Rated generator slip percentage (%) [used only when VSContrl=0 and GenModel=1] + 9999.9 SIG_SySp - Synchronous (zero-torque) generator speed (rpm) [used only when VSContrl=0 and GenModel=1] + 9999.9 SIG_RtTq - Rated torque (N-m) [used only when VSContrl=0 and GenModel=1] + 9999.9 SIG_PORt - Pull-out ratio (Tpullout/Trated) (-) [used only when VSContrl=0 and GenModel=1] +---------------------- THEVENIN-EQUIVALENT INDUCTION GENERATOR ----------------- + 9999.9 TEC_Freq - Line frequency [50 or 60] (Hz) [used only when VSContrl=0 and GenModel=2] + 9998 TEC_NPol - Number of poles [even integer > 0] (-) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_SRes - Stator resistance (ohms) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_RRes - Rotor resistance (ohms) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_VLL - Line-to-line RMS voltage (volts) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_SLR - Stator leakage reactance (ohms) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_RLR - Rotor leakage reactance (ohms) [used only when VSContrl=0 and GenModel=2] + 9999.9 TEC_MR - Magnetizing reactance (ohms) [used only when VSContrl=0 and GenModel=2] +---------------------- HIGH-SPEED SHAFT BRAKE ---------------------------------- + 0 HSSBrMode - HSS brake model {0: none, 1: simple, 3: user-defined from routine UserHSSBr, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) + 9999.9 THSSBrDp - Time to initiate deployment of the HSS brake (s) + 0.6 HSSBrDT - Time for HSS-brake to reach full deployment once initiated (sec) [used only when HSSBrMode=1] + 28116.2 HSSBrTqF - Fully deployed HSS-brake torque (N-m) +---------------------- NACELLE-YAW CONTROL ------------------------------------- + 0 YCMode - Yaw control mode {0: none, 3: user-defined from routine UserYawCont, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) + 9999.9 TYCOn - Time to enable active yaw control (s) [unused when YCMode=0] + 0 YawNeut - Neutral yaw position--yaw spring force is zero at this yaw (degrees) +9.02832E+09 YawSpr - Nacelle-yaw spring constant (N-m/rad) + 1.916E+07 YawDamp - Nacelle-yaw damping constant (N-m/(rad/s)) + 9999.9 TYawManS - Time to start override yaw maneuver and end standard yaw control (s) + 2 YawManRat - Yaw maneuver rate (in absolute value) (deg/s) + 0 NacYawF - Final yaw angle for override yaw maneuvers (degrees) +---------------------- AERODYNAMIC FLOW CONTROL -------------------------------- + 0 AfCmode - Airfoil control mode {0: none, 1: cosine wave cycle, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) + 0 AfC_Mean - Mean level for cosine cycling or steady value (-) [used only with AfCmode==1] + 0 AfC_Amp - Amplitude for for cosine cycling of flap signal (-) [used only with AfCmode==1] + 0 AfC_Phase - Phase relative to the blade azimuth (0 is vertical) for for cosine cycling of flap signal (deg) [used only with AfCmode==1] +---------------------- STRUCTURAL CONTROL -------------------------------------- +0 NumBStC - Number of blade structural controllers (integer) +"unused" BStCfiles - Name of the files for blade structural controllers (quoted strings) [unused when NumBStC==0] +0 NumNStC - Number of nacelle structural controllers (integer) +"unused" NStCfiles - Name of the files for nacelle structural controllers (quoted strings) [unused when NumNStC==0] +0 NumTStC - Number of tower structural controllers (integer) +"unused" TStCfiles - Name of the files for tower structural controllers (quoted strings) [unused when NumTStC==0] +0 NumSStC - Number of substructure structural controllers (integer) +"unused" SStCfiles - Name of the files for substructure structural controllers (quoted strings) [unused when NumSStC==0] +---------------------- CABLE CONTROL ------------------------------------------- + 0 CCmode - Cable control mode {0: none, 4: user-defined from Simulink/Labview, 5: user-defined from Bladed-style DLL} (switch) +---------------------- BLADED INTERFACE ---------------------------------------- [used only with Bladed Interface] +"../../ROSCO/install/lib/libdiscon.so" DLL_FileName - Name/location of the dynamic library {.dll [Windows] or .so [Linux]} in the Bladed-DLL format (-) [used only with Bladed Interface] +"MHK_RM1_DISCON.IN" DLL_InFile - Name of input file sent to the DLL (-) [used only with Bladed Interface] +"DISCON" DLL_ProcName - Name of procedure in DLL to be called (-) [case sensitive; used only with DLL Interface] +"default" DLL_DT - Communication interval for dynamic library (s) (or "default") [used only with Bladed Interface] +false DLL_Ramp - Whether a linear ramp should be used between DLL_DT time steps [introduces time shift when true] (flag) [used only with Bladed Interface] + 9999.9 BPCutoff - Cutoff frequency for low-pass filter on blade pitch from DLL (Hz) [used only with Bladed Interface] + 0 NacYaw_North - Reference yaw angle of the nacelle when the upwind end points due North (deg) [used only with Bladed Interface] + 1 Ptch_Cntrl - Record 28: Use individual pitch control {0: collective pitch; 1: individual pitch control} (switch) [used only with Bladed Interface] + 0 Ptch_SetPnt - Record 5: Below-rated pitch angle set-point (deg) [used only with Bladed Interface] + 0 Ptch_Min - Record 6: Minimum pitch angle (deg) [used only with Bladed Interface] + 0 Ptch_Max - Record 7: Maximum pitch angle (deg) [used only with Bladed Interface] + 0 PtchRate_Min - Record 8: Minimum pitch rate (most negative value allowed) (deg/s) [used only with Bladed Interface] + 0 PtchRate_Max - Record 9: Maximum pitch rate (deg/s) [used only with Bladed Interface] + 0 Gain_OM - Record 16: Optimal mode gain (Nm/(rad/s)^2) [used only with Bladed Interface] + 0 GenSpd_MinOM - Record 17: Minimum generator speed (rpm) [used only with Bladed Interface] + 0 GenSpd_MaxOM - Record 18: Optimal mode maximum speed (rpm) [used only with Bladed Interface] + 0 GenSpd_Dem - Record 19: Demanded generator speed above rated (rpm) [used only with Bladed Interface] + 0 GenTrq_Dem - Record 22: Demanded generator torque above rated (Nm) [used only with Bladed Interface] + 0 GenPwr_Dem - Record 13: Demanded power (W) [used only with Bladed Interface] +---------------------- BLADED INTERFACE TORQUE-SPEED LOOK-UP TABLE ------------- + 0 DLL_NumTrq - Record 26: No. of points in torque-speed look-up table {0 = none and use the optimal mode parameters; nonzero = ignore the optimal mode PARAMETERs by setting Record 16 to 0.0} (-) [used only with Bladed Interface] + GenSpd_TLU GenTrq_TLU + (rpm) (Nm) +---------------------- OUTPUT -------------------------------------------------- +True SumPrint - Print summary data to .sum (flag) (currently unused) + 1 OutFile - Switch to determine where output will be placed: {1: in module output file only; 2: in glue code output file only; 3: both} (currently unused) +True TabDelim - Use tab delimiters in text tabular output file? (flag) (currently unused) +"ES10.3E2" OutFmt - Format used for text tabular output (except time). Resulting field should be 10 characters. (quoted string) (currently unused) + 0 TStart - Time to begin tabular output (s) (currently unused) + OutList - The next line(s) contains a list of output parameters. See OutListParameters.xlsx for a listing of available output channels, (-) +"GenPwr" - Electrical generator power and torque +"GenTq" - Electrical generator power and torque +"GenSpeed" +END of input file (the word "END" must appear in the first 3 columns of this last OutList line) +--------------------------------------------------------------------------------------- diff --git a/examples/01_aeroelasticse/OpenFAST_models/RM1/RM1_MHK.yaml b/examples/01_aeroelasticse/OpenFAST_models/RM1/RM1_MHK.yaml new file mode 100644 index 000000000..9a0022cdb --- /dev/null +++ b/examples/01_aeroelasticse/OpenFAST_models/RM1/RM1_MHK.yaml @@ -0,0 +1,69 @@ +--- # ---------------------NREL Generic controller tuning input file ------------------- + # Written for use with ROSCO_Toolbox tuning procedures + # Turbine: NREL 5MW Reference Wind Turbine +# ------------------------------ OpenFAST PATH DEFINITIONS ------------------------------ +path_params: + FAST_InputFile: 'MHK_RM1_Floating.fst' # Name of *.fst file + FAST_directory: '.' # Main OpenFAST model directory, where the *.fst lives + # Optional + rotor_performance_filename: 'MHK_RM1_Cp_Ct_Cq.txt' # Filename for rotor performance text file (if it has been generated by ccblade already) + +# -------------------------------- TURBINE PARAMETERS ----------------------------------- +turbine_params: + rotor_inertia: 92169 # Rotor inertia [kg m^2], {Available in Elastodyn .sum file} + rated_rotor_speed: 1.204 # 11.5 rpm # Rated rotor speed [rad/s] + v_min: 0.5 # Cut-in wind speed [m/s] + v_rated: 1.9 # Rated wind speed [m/s] + v_max: 3.0 # Cut-out wind speed [m/s], -- Does not need to be exact (JUST ASSUME FOR NOW) + max_pitch_rate: 0.1745 # Maximum blade pitch rate [rad/s] + max_torque_rate: 1500000. # Maximum torque rate [Nm/s], {~1/4 VS_RtTq/s} + rated_power: 500000 # Rated Power [W] + bld_edgewise_freq: 60.2831853 # Blade edgewise first natural frequency [rad/s] + bld_flapwise_freq: 0.0 # Blade flapwise first natural frequency [rad/s] + reynolds_ref: 8e6 + # Optional + # TSR_operational: # None # Desired below-rated operational tip speed ratio (Cp-maximizing TSR is used if not defined) +#------------------------------- CONTROLLER PARAMETERS ---------------------------------- +controller_params: + # Controller flags + LoggingLevel: 0 # {0: write no debug files, 1: write standard output .dbg-file, 2: write standard output .dbg-file and complete avrSWAP-array .dbg2-file + F_LPFType: 1 # {1: first-order low-pass filter, 2: second-order low-pass filter}, [rad/s] (currently filters generator speed and pitch control signals) + F_NotchType: 1 # Notch filter on generator speed and/or tower fore-aft motion (for floating) {0: disable, 1: generator speed, 2: tower-top fore-aft motion, 3: generator speed and tower-top fore-aft motion} + IPC_ControlMode: 0 # Turn Individual Pitch Control (IPC) for fatigue load reductions (pitch contribution) {0: off, 1: 1P reductions, 2: 1P+2P reductions} + VS_ControlMode: 3 # Generator torque control mode in above rated conditions {0: constant torque, 1: constant power, 2: TSR tracking PI control} + VS_ConstPower: 0 # Generator torque control mode in above rated conditions {0: constant torque, 1: constant power, 2: TSR tracking PI control} + PC_ControlMode: 1 # Blade pitch control mode {0: No pitch, fix to fine pitch, 1: active PI blade pitch control} + Y_ControlMode: 0 # Yaw control mode {0: no yaw control, 1: yaw rate control, 2: yaw-by-IPC} + SS_Mode: 1 # Setpoint Smoother mode {0: no setpoint smoothing, 1: introduce setpoint smoothing} + WE_Mode: 0 # Wind speed estimator mode {0: One-second low pass filtered hub height wind speed, 1: Immersion and Invariance Estimator (Ortega et al.)} + PS_Mode: 0 # Pitch saturation mode {0: no pitch saturation, 1: peak shaving, 2: Cp-maximizing pitch saturation, 3: peak shaving and Cp-maximizing pitch saturation} + SD_Mode: 0 # Shutdown mode {0: no shutdown procedure, 1: pitch to max pitch at shutdown} + Fl_Mode: 2 # Floating specific feedback mode {0: no nacelle velocity feedback, 1: nacelle velocity feedback} + Flp_Mode: 0 # Flap control mode {0: no flap control, 1: steady state flap angle, 2: Proportional flap control} + # Controller parameters + U_pc: [2.1] + interp_type: sigma + zeta_pc: [0.7] # Pitch controller desired damping ratio [-] + omega_pc: [0.9] # Pitch controller desired natural frequency [rad/s] + zeta_vs: 0.7 # Torque controller desired damping ratio [-] + omega_vs: 0.7 # Torque controller desired natural frequency [rad/s] + twr_freq: 3.3404 # Tower natural frequency [rad/s] # 0.4499 (old value) 3.3404(new value) + # twr_freq: 0.061009 # 2P + ptfm_freq: 0.6613 # Platform natural frequency [rad/s] (OC4Hywind Parameters, here) 0.2325 (old value) 0.6613879263 (new value) + # Optional + ps_percent: 0.80 # Percent peak shaving [%, <= 1 ], {default = 80%} + sd_maxpit: 0.4363 # Maximum blade pitch angle to initiate shutdown [rad], {default = bld pitch at v_max} + Kp_float: -0.3897 + max_torque_factor: 1.5 + DISCON: + F_NumNotchFilts: 1 + F_NotchFreqs: [2.4200] # 2P + F_NotchBetaNum: [0.0] + F_NotchBetaDen: [0.25] + F_GenSpdNotch_N: 1 + F_GenSpdNotch_Ind: [1] + F_TwrTopNotch_N: 1 + F_TwrTopNotch_Ind: [1] + F_NotchCornerFreq_GS: [2.42] + F_FlHighPassFreq: .015 + F_LPFCornerFreq: 15.0 diff --git a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml index bfcf06d5a..5ad2c7b3d 100644 --- a/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml +++ b/examples/06_IEA-15-240-RWT/IEA-15-240-RWT_VolturnUS-S.yaml @@ -9,6 +9,7 @@ assembly: rotor_diameter: 241.94 rated_power: 15.e+6 lifetime: 25.0 + marine_hydro: False components: blade: outer_shape_bem: diff --git a/examples/13_DTQP/gen_oloc.py b/examples/13_DTQP/gen_oloc.py index 61e344f11..12735747d 100644 --- a/examples/13_DTQP/gen_oloc.py +++ b/examples/13_DTQP/gen_oloc.py @@ -11,6 +11,8 @@ from pCrunch import LoadsAnalysis, PowerProduction, FatigueParams import pandas as pd +import matplotlib.pyplot as plt + import numpy as np @@ -24,7 +26,7 @@ fname_modeling_options = mydir + os.sep + "modeling_options.yaml" modeling_options = sch.load_modeling_yaml(fname_modeling_options) - fname_wt_input = mydir + os.sep + "IEA-15-floating.yaml" + fname_wt_input = os.path.join(this_dir,"..","06_IEA-15-240-RWT", "IEA-15-240-RWT_VolturnUS-S.yaml") wt_init = sch.load_geometry_yaml(fname_wt_input) fname_analysis_options = mydir + os.sep + "analysis_options.yaml" @@ -73,6 +75,7 @@ WindFile_name = [''] * dlc_generator.n_cases level2_disturbance = [] + fig,ax = plt.subplots(1) for i_case in range(dlc_generator.n_cases): dlc_generator.cases[i_case].AnalysisTime = dlc_generator.cases[i_case].analysis_time + dlc_generator.cases[i_case].transient_time @@ -95,6 +98,13 @@ tt = ts_file['t'] level2_disturbance.append({'Time':tt, 'Wind': u_h}) + ax.plot(tt,u_h) + ax.set_xlim([tt[0],tt[-1]]) + ax.set_xlabel('Time [s]') + ax.set_ylabel('Wind Speed [m/s]') + + plt.show() + # Linear Model diff --git a/examples/17_MHK/RM1.yaml b/examples/17_MHK/RM1.yaml index bb9d9ffae..9a429aae3 100644 --- a/examples/17_MHK/RM1.yaml +++ b/examples/17_MHK/RM1.yaml @@ -1054,7 +1054,7 @@ materials: - {name: adhesive, description: Sample adhesive, source: https://www.nrel.gov/docs/fy19osti/73585.pdf, orth: 0, rho: 1100, E: 4560000.0, nu: 0.49, alpha: 0.0, Xt: 690000.0, Xc: 400000.0, S: 310000.0, G: 1520000.0, unit_cost: 9.0} control: supervisory: {Vin: 0.5, Vout: 4.0, maxTS: 60} - pitch: {PC_zeta: !!null '', PC_omega: !!null '', ps_percent: !!null '', max_pitch: !!null '', max_pitch_rate: 0.1745, min_pitch: 0.00088} + pitch: {PC_zeta: !!null '', PC_omega: !!null '', ps_percent: 1.0, max_pitch: !!null '', max_pitch_rate: 0.1745, min_pitch: 0.00088} torque: {control_type: !!null '', tsr: 7.64, VS_zeta: !!null '', VS_omega: !!null '', max_torque_rate: 1500000.0, VS_minspd: 0.0, VS_maxspd: 1.26711} setpoint_smooth: {ss_vsgain: !!null '', ss_pcgain: !!null ''} shutdown: {limit_type: !!null '', limit_value: !!null ''} diff --git a/examples/17_MHK/driver_RM1_WISDEM_loads.py b/examples/17_MHK/driver_RM1_WISDEM_loads.py index b21f49c02..0ad28f444 100644 --- a/examples/17_MHK/driver_RM1_WISDEM_loads.py +++ b/examples/17_MHK/driver_RM1_WISDEM_loads.py @@ -1,6 +1,6 @@ from weis.glue_code.runWEIS import run_weis -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI import os, time, sys ''' diff --git a/examples/17_MHK/modeling_options_MHK_WISDEM.yaml b/examples/17_MHK/modeling_options_MHK_WISDEM.yaml index 8f510cafa..bd3460d18 100644 --- a/examples/17_MHK/modeling_options_MHK_WISDEM.yaml +++ b/examples/17_MHK/modeling_options_MHK_WISDEM.yaml @@ -8,12 +8,14 @@ General: use_exe: True # FAST_exe: /home/dzalkind/.conda-envs/rosco-env/bin/openfast # FAST_exe: /Users/dzalkind/opt/anaconda3/envs/rosco-env/bin/openfast - FAST_exe: /Users/dzalkind/opt/anaconda3/envs/weis-env4/bin/openfast + #FAST_exe: /Users/dzalkind/opt/anaconda3/envs/weis-env4/bin/openfast WISDEM: RotorSE: - flag: True + flag: False + n_pitch_perf_surfaces: 5 + n_tsr_perf_surfaces: 5 spar_cap_ss: Spar_Cap_SS spar_cap_ps: Spar_Cap_PS te_ss: TE_reinforcement_SS @@ -21,15 +23,15 @@ WISDEM: frame3dd: flag: False TowerSE: - flag: True + flag: False frame3dd: flag: False DriveSE: - flag: True + flag: False frame3dd: flag: False FloatingSE: - flag: True + flag: False rank_and_file: True symmetric_moorings: False frame3dd: @@ -38,12 +40,15 @@ WISDEM: # flag: True OWENS: - flags: True + flags: False path_to_owens_dir: path Level3: # Options for WEIS fidelity level 3 = nonlinear time domain flag: True + from_openfast: True + openfast_file: MHK_RM1_Floating.fst + openfast_dir: ../01_aeroelasticse/OpenFAST_models/RM1 simulation: DT: 0.01 CompElast: 1 @@ -144,6 +149,7 @@ Level3: # Options for WEIS fidelity level 3 = nonlinear time domain ROSCO: flag: True tuning_yaml: RM1_MHK.rosco.yaml + PRC_Mode: 0 # zeta_pc: 1.23 # omega_pc: 0.198 # U_pc: [12] @@ -189,10 +195,10 @@ DLC_driver: probability: [0.2,0.2,0.3,0.15,0.15] DLCs: - DLC: "1.1" # Eagle, full - # n_seeds: 1 + n_seeds: 2 transient_time: 60. - # current_speed: [1.5] - analysis_time: 300. + current_speed: [2.5] + analysis_time: 100. ws_bin_size: 0.25 turbsim_inputs: PLExp: 0 diff --git a/examples/19_DFSM/DFSM.ipynb b/examples/19_DFSM/DFSM.ipynb new file mode 100644 index 000000000..1f3a0f42c --- /dev/null +++ b/examples/19_DFSM/DFSM.ipynb @@ -0,0 +1,134 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "d5f9f236", + "metadata": {}, + "source": [ + "This notebook builds on `Preprocessing.ipynb' and uses the data to construct surrogate models that can be used for controller optimization studies.\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "4fe70129", + "metadata": {}, + "source": [ + "# Surrogate Model for Controller Gains Optimization\n", + "\n", + "Models that balance accuracy against computational costs are advantageous when designing floating offshore wind turbines (FOWT) using OpenFAST as the high-fidelity model through optimization studies, as several hundred predictive function evaluations might be necessary to identify the optimal solution. \n", + "\n", + "OpenFAST models the dynamic response of FOWT systems as a system of differential-algebraic equations (DAE).\n", + "Whenever OpenFAST is evaluated for a load case, this DAE system is solved to obtain the dynamic response of the FOWT. OpenFAST also uses different modules like AeroDyn, Hydrodyn etc. to capture the appropriate physics.\n", + "Therefore evaluating OpenFAST can be computationally expensive, making it unsuitable for optimization based studies where several hundred model evaluations might be required.\n", + "\n", + "## Surrogate Model Requirements\n", + "\n", + "Consider the following figure illustrating the steps involved in a controller optimization study:\n", + "\n", + "\n", + "\n", + "The goal is to develop a surrogate model to replace the aero-hydro-servo-elastic model that can predict the timeseries of outputs given the inputs and controls. Once constructed, this model can then be coupled with the controller as shown in the figure:\n", + "\n", + "\n", + "\n", + "\n", + "There are different approaches that can be used to construct such a surrogate model such as classic system identification (sys-id) methods and long-short-term memory (LSTM) networks.\n", + "These methods can be classified as 'black-box' approaches to map the inputs to the outputs. They have been widely utilized for various complex engineering systems.\n", + "\n", + "However, in this example we use the derivative function surrogate modeling (DFSM) approach to construct the surrogate model.\n", + "\n", + "## Derivative Function Surrogate Model (DFSM)\n", + "\n", + "DFSM are surrogate models of the dynamic function or derivative function, that describes how the state derivatives evolve for the given system.\n", + "A key assumption that is made when using DFSM approach is that the system dynamics can be described by an ordinary differential equation (ODE) of the following form:\n", + " \\begin{align}\n", + " \\dot{\\xi} &= f(\\xi,u)\\\\\n", + " y &= g(\\xi,u)\n", + " \\end{align}\n", + "Where $\\xi$ denotes the states, $u$ denotes the controls, $y$ denotes the outputs, and $f$, $g$ correspond to the state derivative and output functions respectively. \n", + "\n", + "DFSM approximates $f$ and $g$ as:\n", + " \\begin{align}\n", + " \\dot{\\xi} &\\approx f_{\\text{DFSM}}(\\xi,u)\\\\\n", + " y &\\approx g_{\\text{DFSM}}(\\xi,u) \\\\\n", + " \\end{align}\n", + "\n", + "Unlike the aforementioned black-box approaches, the DFSM approach allows the user to prescribe a relation between the quantities approximated. In this study, we assume that the underlying ODE can be approximated as an linear-parameter varying (LPV) system, where the key parameter is the wind speed ($w$):\n", + " \\begin{align}\n", + " f_{\\text{DFSM}}(\\xi,u) = A(w)\\xi + B(w)u\\\\\n", + " g_{\\text{DFSM}}(\\xi,u) = C(w)\\xi + D(w)u \\\\\n", + " \\end{align}\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "a8e6b6c8", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "id": "84e0bffc", + "metadata": {}, + "source": [ + "## Model Construction\n", + "\n", + "### Inputs\n", + "The inputs when constructing the DFSM are turbulent OpenFAST simulations for different wind speeds between $w \\in [3,25]$. More details regarding these inputs, and how they are preprocessed can be found in `Preprocessing.ipynb'.\n", + "\n", + "Once the inputs are available and preprocessed, then the LPV model is constructed as follows:\n", + "1. Individual state-space matrices $\\Sigma = [A,B,C,D]$ are obtained for different wind speeds values for $w \\in [3,25]$\n", + "2. The system matrices $\\Sigma$ are then linearly interpolated over $w$ to obtain a continuous model. For more details regarding LPV modeling, please refer to [https://doi.org/10.1115/1.4063969](https://doi.org/10.1115/1.4063969) \n", + "\n", + "### Obtaining linear models\n", + "We solve an optimization problem to obtain the system matrices $\\Sigma$. The $A$ and $B$ matrices are evaluated as part of one problem, and the $C$ and $D$ matrices are evaluate separately, as they predict different quantities, and have different requirements.\n", + "\n", + "\n", + "The problem solved to identify the A and B matrices can be formulated as:\n", + "\\begin{align}\n", + " \\min_{\\theta} \\frac{Tr(E'*E)}{N} \\\\\n", + " \\text{s.t} \\quad max(real(eig(A(\\theta))) < 0\\\\\n", + " \\text{where:} \\quad E = \\dot{\\xi}_{\\text{act}} - \\dot{\\xi}_{\\text{DFSM}}\\\\\n", + " \\dot{\\xi}_{\\text{DFSM}} = A(\\theta)\\xi + B(\\theta)u \\\\\n", + " Tr~ \\text{is the trace operation}\n", + "\\end{align}\n", + "The constraint on the eigen value is required to ensure the the simulations and the state-space system remain stable.\n", + "We use a hybrid optimization strategy to solve this problem. A genetic algorithm is used to get a good estimate of $\\theta$, then a gradient-based optimizer using the interior-point algorithm is used to solve the problem.\n", + "\n", + "Then for the next wind speed $w_2$, the $A$ and $B$ matrices evaluated for the previous wind speed $w_1$ are used as the starting point in the gradient-based optimizer." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "254e1213", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/19_DFSM/Preprocessing.ipynb b/examples/19_DFSM/Preprocessing.ipynb new file mode 100644 index 000000000..08a3c3fa8 --- /dev/null +++ b/examples/19_DFSM/Preprocessing.ipynb @@ -0,0 +1,453 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a531fe41-a1ac-48fd-ae17-a2e9570a3d8b", + "metadata": {}, + "source": [ + "This notebook summarizes the pre-processing done to the data when constructing the derivative function surrogate models (DFSM).\n", + "These preprocessing steps are what happen under the hood in the class `SimulationDetails', which can be found in the DFSM package.\n", + "The three broad steps involved in constructing the DFSM are as follows:\n", + "1. Run OpenFAST simulations\n", + "2. Preprocess simulation results\n", + "3. Construct DFSM\n", + "\n", + "This notebook will go over step 2, preprocessing." + ] + }, + { + "cell_type": "markdown", + "id": "f073009e-324f-4151-982c-b598ed44ee11", + "metadata": {}, + "source": [ + "There are broadly five steps to postprocessing:\n", + "1. Load OpenFAST simulations from the out/pkg file to obtain the time-series signals of key quantities.\n", + "2. Scale quantities.\n", + "3. Filter certain signals.\n", + "4. Approximate first and second-time derivatives for states.\n", + "5. Store key indices and processed signals.\n", + "\n", + "We will use the simulation results in `example.outb' to illustrate the different steps in the process.\n", + "This file contains the simulation results of a load case in the rated region from DLC 1.6. \n", + "\n", + "The process aims to construct a state-space model with the following quantities.\n", + "\n", + "The generator, tower fore-aft, and platform pitch degrees of freedom are enabled, so these will be the states. In addition to these, the first-time derivatives of these quantities are also included as part of the states, similar to the linearized OpenFAST models.\n", + "\n", + "X = [x,dx]\n", + "\n", + "dX = [dx,d2x]\n", + "\n", + "The system's inputs are the rotor's average wind speed, generator torque, blade pitch, and wave elevation.\n", + "\n", + "The system's outputs are the tower base-fore aft force and moment, nacelle translational and rotational acceleration, and generator power.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "8e74cc76-4a79-4e63-bd89-9821d3749548", + "metadata": {}, + "outputs": [], + "source": [ + "#---------------------------------------------------------------------------------------------------------------\n", + "# Step 1: Load OpenFAST simulations from the out/pkg file to obtain the time-series signals of key quantities. \n", + "#---------------------------------------------------------------------------------------------------------------\n", + "\n", + "# Import necessary libraries\n", + "import matplotlib.pyplot as plt \n", + "import numpy as np\n", + "import os\n", + "import pickle\n", + "from scipy.interpolate import CubicSpline\n", + "\n", + "# path to this directory\n", + "this_dir = os.getcwd()\n", + "\n", + "# list out the states, controls and outputs\n", + "reqd_states = ['PtfmPitch','TTDspFA','GenSpeed']\n", + "reqd_controls = ['RtVAvgxh','GenTq','BldPitch1','Wave1Elev']\n", + "reqd_outputs = ['TwrBsFxt','TwrBsMyt','YawBrTAxp','NcIMURAys','GenPwr']\n", + "\n", + "# outb file\n", + "outb_file = this_dir + os.sep + 'example.outb'\n", + "\n", + "# import load_FAST_out from pCrunch to load the outb file\n", + "# pcrunch is available at https://github.com/NREL/pCrunch\n", + "from pCrunch.io import load_FAST_out\n", + "\n", + "# load\n", + "FAST_out = load_FAST_out(outb_file)[0]\n", + "\n", + "# load time\n", + "time = FAST_out['Time']\n", + "\n", + "ns = len(reqd_states)\n", + "nc = len(reqd_controls)\n", + "ny = len(reqd_outputs)\n", + "nt = len(time)\n", + "\n", + "# initialize storage arrays\n", + "states = np.zeros((nt,ns))\n", + "controls = np.zeros((nt,nc))\n", + "outputs = np.zeros((nt,ny))\n", + "\n", + "# Loop through and extract states, controls, and outputs\n", + "for ix,state_name in enumerate(reqd_states):\n", + " states[:,ix] = FAST_out[state_name]\n", + "\n", + "\n", + "for iu,control_name in enumerate(reqd_controls):\n", + " controls[:,iu] = FAST_out[control_name]\n", + "\n", + "\n", + "for iy,output_name in enumerate(reqd_outputs):\n", + " outputs[:,iy] = FAST_out[output_name]\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "efc9f89b-bad4-4e33-8d03-8c647db0d991", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Time')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plot states, controls and outputs\n", + "fig,ax = plt.subplots(ns,1)\n", + "\n", + "for ix in range(ns):\n", + " ax[ix].plot(time,states[:,ix])\n", + " ax[ix].set_title(reqd_states[ix])\n", + " \n", + " if not(ix == ns-1):\n", + " ax[ix].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "fig.subplots_adjust(hspace = 0.35)\n", + "ax[-1].set_xlabel('Time')\n", + "\n", + "# Controls\n", + "fig,ax = plt.subplots(nc,1)\n", + "\n", + "for ix in range(nc):\n", + " ax[ix].plot(time,controls[:,ix])\n", + " ax[ix].set_title(reqd_controls[ix])\n", + " \n", + " if not(ix == nc-1):\n", + " ax[ix].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "fig.subplots_adjust(hspace = 0.35)\n", + "ax[-1].set_xlabel('Time')\n", + "\n", + "# Outputs\n", + "fig,ax = plt.subplots(ny,1)\n", + "\n", + "for ix in range(ny):\n", + " ax[ix].plot(time,outputs[:,ix])\n", + " ax[ix].set_title(reqd_outputs[ix])\n", + " \n", + " if not(ix == ny-1):\n", + " ax[ix].tick_params(\n", + " axis='x', # changes apply to the x-axis\n", + " which='both', # both major and minor ticks are affected\n", + " bottom=False, # ticks along the bottom edge are off\n", + " top=False, # ticks along the top edge are off\n", + " labelbottom=False) # labels along the bottom edge are off\n", + "\n", + "fig.subplots_adjust(hspace = 0.4)\n", + "ax[-1].set_xlabel('Time')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "dea21935", + "metadata": {}, + "outputs": [], + "source": [ + "#---------------------------------------------------------------------------------------------------------------\n", + "# Step 2: Scale quantities.\n", + "#---------------------------------------------------------------------------------------------------------------\n", + "\n", + "# Some quantities in states/controls/outputs can be of a different order of magnitude than others.\n", + "# These quantities can be scaled to be of the same order.\n", + "# For this example, no quantities will be scaled. Therefore the scalers will be all ones\n", + "\n", + "scaler_states = np.ones((ns,))\n", + "scaler_controls = np.ones((nc,))\n", + "scaler_outputs = np.ones((ny,))\n", + "\n", + "states = states/scaler_states\n", + "controls = controls/scaler_controls\n", + "outputs = outputs/scaler_outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "db5c672c", + "metadata": {}, + "outputs": [], + "source": [ + "#---------------------------------------------------------------------------------------------------------------\n", + "# Step 3: Filter quantities.\n", + "#---------------------------------------------------------------------------------------------------------------\n", + "# Some quantitites can be noisy, so these quantities can be filtered before constructing the model\n", + "# The states are all filtered slightly using 'filtfilt', with a tf of 0.1.\n", + "# This is done to help with the derivative calculation\n", + "\n", + "from scipy.signal import filtfilt\n", + "\n", + "def filter_signal(t_f,time,signal):\n", + "\n", + " '''\n", + " Function to filter the given signal\n", + " '''\n", + "\n", + " dt = time[1]-time[0]\n", + " nb = int(np.floor(t_f/dt))\n", + " b = np.ones((nb,))/nb;a = 1\n", + " signal = filtfilt(b,a,signal,axis = 0)\n", + "\n", + " return signal\n", + "\n", + "t_f = 0.1\n", + "\n", + "# store a copy of unfiltered states\n", + "states_unfiltered = states.copy()\n", + "\n", + "# loop through and filter\n", + "for ix in range(ns):\n", + " \n", + " states[:,ix] = filter_signal(t_f,time,states[:,ix])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6ef4b457", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#---------------------------------------------------------------------------------------------------------------\n", + "# Step 4: Approximate first and second-time derivatives for states.\n", + "#---------------------------------------------------------------------------------------------------------------\n", + "\n", + "# The state derivatives are calculated by constructing polynomial approximation of these states on the time esh, and evaluating \n", + "# the state derivatives of these polynomial approximations\n", + "\n", + "# construct polynomial approximation\n", + "states_pp = CubicSpline(time,states)\n", + "dx_pp = states_pp.derivative\n", + "\n", + "# evaluate first time derivative\n", + "dx_pp1 = dx_pp(nu = 1)\n", + "\n", + "# evaluate second time derivative\n", + "dx_pp2 = dx_pp(nu = 2)\n", + "\n", + "# evaluate state derivatives\n", + "state_derivatives = dx_pp1(time)\n", + "\n", + "# evaluate the second time derivatives\n", + "state_derivatives2 = dx_pp2(time)\n", + "\n", + "# calculate the state derivatives for the unfiltered states\n", + "\n", + "# construct polynomial approximation\n", + "states_pp_uf = CubicSpline(time,states_unfiltered)\n", + "dx_pp_uf = states_pp_uf.derivative\n", + "\n", + "# evaluate first time derivative\n", + "dx_pp1_uf = dx_pp_uf(nu = 1)\n", + "\n", + "# evaluate second time derivative\n", + "dx_pp2_uf = dx_pp_uf(nu = 2)\n", + "\n", + "# evaluate state derivatives\n", + "state_derivatives_uf = dx_pp1_uf(time)\n", + "\n", + "# evaluate the second time derivatives\n", + "state_derivatives2_uf = dx_pp2_uf(time)\n", + "\n", + "# plot\n", + "\n", + "fig,ax = plt.subplots(1)\n", + "\n", + "ax.plot(time,states_unfiltered[:,0],label = 'unfiltered')\n", + "ax.plot(time,states[:,0],label = 'filtered')\n", + "ax.set_xlabel('Time [s]')\n", + "ax.set_title('PtfmPitch [deg]')\n", + "ax.legend(ncol = 2)\n", + "\n", + "fig,ax = plt.subplots(1)\n", + "\n", + "ax.plot(time,state_derivatives_uf[:,0],label = 'unfiltered')\n", + "ax.plot(time,state_derivatives[:,0],label = 'filtered')\n", + "ax.set_xlabel('Time [s]')\n", + "ax.set_title('dPtfmPitch')\n", + "ax.legend(ncol = 2)\n", + "\n", + "fig,ax = plt.subplots(1)\n", + "\n", + "ax.plot(time,state_derivatives2_uf[:,0],label = 'unfiltered')\n", + "ax.plot(time,state_derivatives2[:,0],label = 'filtered')\n", + "ax.set_xlabel('Time [s]')\n", + "ax.set_title('ddPtfmPitch')\n", + "ax.legend(ncol = 2)\n", + "\n", + "# From the plots, its clear that filtering the states help when calculating the state derivatives" + ] + }, + { + "cell_type": "markdown", + "id": "ce09c3ef-fb30-4f1c-9729-794d12089f16", + "metadata": {}, + "source": [ + "In the final step, the indices for some key quantities are stored\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d9599d60-d490-48bb-a7e4-3be4decd5625", + "metadata": {}, + "outputs": [], + "source": [ + "blade_pitch_ind = reqd_controls.index('BldPitch1')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20358a9e-1181-4851-bcaf-50d51525277d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d3895870-ba9f-4953-8a84-32e5901f8572", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/19_DFSM/README.md b/examples/19_DFSM/README.md new file mode 100644 index 000000000..9c6aebc57 --- /dev/null +++ b/examples/19_DFSM/README.md @@ -0,0 +1,26 @@ +## Overview +This example introduces the basic workings of the derivative function surrogate modeling (DFSM) approach, and demonstrates a usecase for closed-loop simulations. + +Checkout `weis_driver_dfsm_mhk.py` on how to use the DFSM with ROSCO for closed-loop simulation. + +The DFSM is available in the `dfsm_mhk.pkl` file. This model has been specifically built for simulating load cases from DLC 1.1. + +## Model Description +The DFSM approximates the system response as a linear parameter varying (LPV) state-space model with the following structure: + +dx/dt = A(w)x + B(w)u + +y = C(w)x + D(w)u + +The states (x) considered in the model are platform pitch (PtfmPitch), platform heave (PtfmHeave), generator speed (GenSpeed), and their first-time derivatives. + +The controls/inputs (u) are rotor average wind speed (w) (RtVAvgxh), generator torque (GenTq), blade pitch (BldPitch1), and wave elevation (Wave1Elev). + +The outputs (y) are tower-base fore-aft shear force (TwrBsFxt), side-to-side moment (TwrBsMyt), tower top translational and rotational accelerations (YawBrTAxp, NcIMURAys), generator power (GenPwr), and Fluid Cp and Ct. + +For more details on preprocessing and model construction please refer to the `Preprocessing.ipynb` and `DFSM.ipynb` notebooks. + + + + + diff --git a/examples/19_DFSM/RM1.yaml b/examples/19_DFSM/RM1.yaml new file mode 100644 index 000000000..9a429aae3 --- /dev/null +++ b/examples/19_DFSM/RM1.yaml @@ -0,0 +1,1063 @@ +name: MHK RM1 +assembly: {turbine_class: I, turbulence_class: B, drivetrain: geared, rotor_orientation: upwind, number_of_blades: 2, hub_height: -25.2, rotor_diameter: 20.0, rated_power: 500000, lifetime: 25.0, marine_hydro: true} +components: + blade: + outer_shape_bem: + airfoil_position: + grid: &id001 [0.0, 0.016666666666666666, 0.05, 0.08333333333333333, 0.11666666666666667, 0.15000000000000002, 0.18333333333333332, 0.21666666666666667, 0.25, 0.2833333333333333, 0.31666666666666665, 0.35, 0.38333333333333336, 0.4166666666666667, 0.44999999999999996, 0.4833333333333333, 0.5166666666666667, 0.55, 0.5833333333333334, 0.6166666666666667, 0.6499999999999999, 0.6833333333333333, 0.7166666666666667, 0.75, 0.7833333333333333, 0.8166666666666667, 0.8500000000000001, 0.8833333333333333, 0.9166666666666666, 0.9500000000000001, 0.9833333333333333, 1.0] 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flange_t2shell_t: 6.0, flange_OD2hub_D: 0.6, flange_ID2OD: 0.8, hub_blade_spacing_margin: 1.2, hub_stress_concentration: 3.0, n_front_brackets: 5, n_rear_brackets: 5, clearance_hub_spinner: 0.5, spin_hole_incr: 1.2, pitch_system_scaling_factor: 0.75, spinner_gust_ws: 70.0, hub_material: cast_iron, spinner_material: glass_uni} + nacelle: + drivetrain: + uptilt: -0.0 + distance_tt_hub: 1.2 + distance_hub_mb: 2.0 + distance_mb_mb: 1.0 + overhang: 4.91 + generator_length: 2.0 + generator_radius_user: 0.25 + generator_mass_user: 8928.0 + generator_rpm_efficiency_user: + grid: [0.0,1.0] + values: [1.0,1.0] + gear_ratio: 53.0 + gearbox_length_user: 0.0 + gearbox_radius_user: 0.0 + gearbox_mass_user: 0.0 + gearbox_efficiency: 0.92 + damping_ratio: 0.01 + lss_diameter: [0.577, 0.577] + lss_wall_thickness: [0.288, 0.288] + lss_material: steel + hss_length: 1.5 + hss_diameter: [0.288, 0.288] + hss_wall_thickness: [0.144, 0.144] + hss_material: steel + nose_diameter: [0.3, 0.3] + nose_wall_thickness: [0.1, 0.1] + bedplate_wall_thickness: + grid: [0.0, 1.0] + values: [0.05, 0.05] + bedplate_flange_width: 1.0 + bedplate_flange_thickness: 0.05 + bedplate_web_thickness: 0.05 + bedplate_material: steel + mb1Type: CARB + mb2Type: SRB + uptower: true + gear_configuration: eep + planet_numbers: [3, 3, 0] + brake_mass_user: 0.0 + hvac_mass_coefficient: 0.025 + converter_mass_user: 0.0 + transformer_mass_user: 0.0 + # generator: {generator_type: DFIG, rho_Fe: 7700.0, rho_Fes: 7850.0, rho_Copper: 8900.0, rho_PM: 7450.0, B_r: 1.2, P_Fe0e: 1.0, P_Fe0h: 4.0, S_N: -0.002, alpha_p: 1.0995574287564276, b_r_tau_r: 0.45, b_ro: 0.004, b_s_tau_s: 0.45, b_so: 0.004, freq: 60, h_i: 0.001, h_sy0: 0.0, h_w: 0.005, k_fes: 0.9, k_s: 0.2, m: 3, mu_0: 1.2566370614359173e-06, mu_r: 1.06, p: 3.0, phi: 1.5707963267948966, ratio_mw2pp: 0.7, resist_Cu: 2.52e-08, y_tau_pr: 0.8333333, rated_rpm: 1200.0, cofi: 0.9, y_tau_p: 0.8, sigma: 21500.0, rad_ag: 0.61, len_s: 0.49, h_s: 0.08, I_0: 40.0, B_symax: 1.3, S_Nmax: -0.2, h_0: 0.01, k_fillr: 0.55, k_fills: 0.65, q1: 5, q2: 4, C_Cu: 4.786, C_Fe: 0.556, C_Fes: 0.50139, C_PM: 50.0, mass_coefficient: 0.0, q3: 4, d_r: 0.0, h_m: 0.0, n_r: 0.0, t_wr: 0.0, n_s: 0.0, b_st: 0.0, d_s: 0.0, t_ws: 0.0} + tower: + outer_shape_bem: + reference_axis: + x: + grid: [0.375, 0.5833333333333334, 0.7916666666666666, 1.0] + values: [0, 0, 0, 0] + y: + grid: [0.375, 0.5833333333333334, 0.7916666666666666, 1.0] + values: [0, 0, 0, 0] + z: + grid: [0.375, 0.5833333333333334, 0.7916666666666666, 1.0] + values: [-9.0, -14.0, -19.0, -24.0] + outer_diameter: + grid: [0.375, 0.5833333333333334, 0.7916666666666666, 1.0] + values: [0.3253, 0.3253, 0.3253, 0.3253] + drag_coefficient: + grid: [0.375, 0.5833333333333334, 0.7916666666666666, 1.0] + values: [0.2, 0.2, 0.2, 0.2] + internal_structure_2d_fem: + outfitting_factor: 1.07 + reference_axis: + x: + grid: [0.0, 1.0] + values: [0.0, 0.0] + y: + grid: [0.0, 1.0] + values: [0.0, 0.0] + z: + grid: [0.0, 7.728811463372998e-05, 0.10047454902385111, 0.10055183713848485, 0.20094909804770222, 0.20102638616233595, 0.30142364707155334, 0.30150093518618704, 0.40189819609540445, 0.40197548421003826, 0.5023727451192556, 0.5024500332338894, 0.6028472941431067, 0.6029245822577405, 0.7033218431669578, 0.7033991312815916, 0.8037963921908089, 0.8038736803054427, 0.90427094121466, 0.9043482293292937, 1.0] + values: [15, 15.01, 28, 28.01, 41, 41.01, 54, 54.01, 67, 67.01, 80, 80.01, 93, 93.01, 106, 106.01, 119, 119.01, 132, 132.01, 144.386] + layers: + - name: tower_wall + material: steel + thickness: + grid: [0.0, 0.10047454902385111, 0.20094909804770222, 0.30142364707155334, 0.40189819609540445, 0.5023727451192556, 0.6028472941431067, 0.7033218431669578, 0.8037963921908089, 0.90427094121466, 1.0] + values: [0.09, 0.0870568244887821, 0.06969791129654272, 0.06659467020278927, 0.048969783194233195, 0.04562355995431616, 0.028128174258004363, 0.025616868141627953, 0.009881156462835017, 0.009941082963793929, 0.005931142626464429] + floating_platform: + joints: + - name: 'downstream_upper_col' + location: [28.0, 0.0, 6.0] + cylindrical: True + axial_coeffs: &ax_coeffs_col + Cd: 0.0 + Ca: 0.0 + Cp: 0.0 + - name: 'downstream_lower_col' + location: [28.0, 0.0, -10.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'upstream_upper_col' + location: [28.0, 3.141592653589793, 6.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'upstream_lower_col' + location: [28.0, 3.141592653589793, -10.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'starboard_upper_col' + location: [12, -1.5707963267948966, 6.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'starboard_lower_col' + location: [12, -1.5707963267948966, -10.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'port_upper_col' + location: [12, 1.5707963267948966, 6.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: 'port_lower_col' + location: [12, 1.5707963267948966, -10.0] + cylindrical: True + axial_coeffs: *ax_coeffs_col + - name: upstream_heave_plate + location: [0, 0, -0.5] + relative: upstream_lower_col + axial_coeffs: &ax_coeffs_hp + Cd: 1.0 + Ca: 1.0 + Cp: 1.0 + - name: starboard_heave_plate + location: [0, 0, -0.5] + relative: starboard_lower_col + axial_coeffs: *ax_coeffs_hp + - name: downstream_heave_plate + location: [0, 0, -0.5] + relative: downstream_lower_col + axial_coeffs: *ax_coeffs_hp + - name: port_heave_plate + location: [0, 0, -0.5] + relative: port_lower_col + axial_coeffs: *ax_coeffs_hp + - name: 'anchor_1' + location: [-124.0, -50.0, -50.0] + cylindrical: false + relative: upstream_lower_col + relative_dims: [True, True, False] # z not relative + - name: 'anchor_2' + location: [-124.0, 50.0, -50.0] + cylindrical: false + relative: upstream_lower_col + relative_dims: [True, True, False] # z not relative + - name: 'anchor_3' + location: [-124.0, 0.0, -50.0] + cylindrical: false + relative: upstream_lower_col + relative_dims: [True, True, False] # z not relative + - name: 'anchor_4' + location: [124.0, -50.0, -50.0] + cylindrical: false + relative: downstream_lower_col + relative_dims: [True, True, False] # z not relative + - name: 'anchor_5' + location: [124.0, 50.0, -50.0] + cylindrical: false + relative: downstream_lower_col + relative_dims: [True, True, False] # z not relative + - name: 'anchor_6' + location: [124.0, 0.0, -50.0] + cylindrical: false + relative: downstream_lower_col + relative_dims: [True, True, False] # z not relative + + members: + - name: 'downstream_col' + joint1: 'downstream_lower_col' + joint2: 'downstream_upper_col' + outer_shape: &col_out + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [8.0, 8.0] + internal_structure: &col_int + layers: + - name: col_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + ballasts: + # - variable_flag: False + # material: slurry + # volume: 40 + # grid: [0.0, 0.05] + - variable_flag: True + grid: [0.0, 0.5] + bulkhead: + material: 'steel' + thickness: + grid: [0.0, 0.5, 1.0] + values: [0.02, 0.02, 0.02] + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + axial_joints: + - {name: 'downstream_lower_pon', grid: 0.0625} + - {name: 'downstream_upper_pon', grid: 0.9375} + - name: 'upstream_col' + joint1: 'upstream_lower_col' + joint2: 'upstream_upper_col' + outer_shape: *col_out + internal_structure: *col_int + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + axial_joints: + - {name: 'upstream_lower_pon', grid: 0.0625} + - {name: 'upstream_upper_pon', grid: 0.9375} + - name: 'starboard_col' + joint1: 'starboard_lower_col' + joint2: 'starboard_upper_col' + outer_shape: *col_out + internal_structure: *col_int + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + axial_joints: + - {name: 'starboard_lower_pon', grid: 0.0625} + - {name: 'starboard_upper_pon', grid: 0.9375} + - name: 'port_col' + joint1: 'port_lower_col' + joint2: 'port_upper_col' + outer_shape: *col_out + internal_structure: *col_int + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + axial_joints: + - {name: 'port_lower_pon', grid: 0.0625} + - {name: 'port_upper_pon', grid: 0.9375} + # - name: 'downstream_heave_plate' + # joint1: 'downstream_heave_plate' + # joint2: 'downstream_lower_col' + # no_intersect: True + # outer_shape: &heave_plate_out + # shape: circular + # outer_diameter: + # grid: [0.0, 1.0] + # values: [12.0, 12.0] + # internal_structure: &heave_plate_int + # layers: + # - name: col_twall + # material: steel + # thickness: + # grid: [0.0, 1.0] + # values: [0.4, 0.4] + # Ca: 1.0 + # Cd: 1.2 + # Cp: 1.0 + # - name: 'upstream_heave_plate' + # joint1: 'upstream_heave_plate' + # joint2: 'upstream_lower_col' + # no_intersect: True + # outer_shape: *heave_plate_out + # internal_structure: *heave_plate_int + # Ca: 1.0 + # Cd: 1.2 + # Cp: 1.0 + # - name: 'port_heave_plate' + # joint1: 'port_heave_plate' + # joint2: 'port_lower_col' + # no_intersect: True + # outer_shape: *heave_plate_out + # internal_structure: *heave_plate_int + # Ca: 1.0 + # Cd: 1.2 + # Cp: 1.0 + # - name: 'starboard_heave_plate' + # joint1: 'starboard_heave_plate' + # joint2: 'starboard_lower_col' + # no_intersect: True + # outer_shape: *heave_plate_out + # internal_structure: *heave_plate_int + # Ca: 1.0 + # Cd: 1.2 + # Cp: 1.0 + - name: 'upper_pontoon_DP' + joint1: 'downstream_upper_pon' + joint2: 'port_upper_pon' + outer_shape: &pon_out + shape: circular + outer_diameter: + grid: [0.0, 1.0] + values: [2.0, 2.0] + internal_structure: &pon_int_upper + layers: + - name: pon_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + # ballasts: + # - variable_flag: False + # material: slurry + # volume: 519 + # grid: [0.0, 0.05] + # - variable_flag: True + # grid: [0.0, 1.0] + # bulkhead: + # material: '' + # thickness: + # grid: [] + # values: [] + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'upper_pontoon_DS' + joint1: 'downstream_upper_pon' + joint2: 'starboard_upper_pon' + outer_shape: *pon_out + internal_structure: *pon_int_upper + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'upper_pontoon_UP' + joint1: 'upstream_upper_pon' + joint2: 'port_upper_pon' + outer_shape: *pon_out + internal_structure: *pon_int_upper + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'upper_pontoon_US' + joint1: 'upstream_upper_pon' + joint2: 'starboard_upper_pon' + outer_shape: *pon_out + internal_structure: *pon_int_upper + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'upper_pontoon_PS' + joint1: 'port_upper_pon' + joint2: 'starboard_upper_pon' + outer_shape: *pon_out + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + internal_structure: *pon_int_upper + - name: 'lower_pontoon_DP' + joint1: 'downstream_lower_pon' + joint2: 'port_lower_pon' + outer_shape: *pon_out + internal_structure: &pon_int_lower + layers: + - name: pon_twall + material: steel + thickness: + grid: [0.0, 1.0] + values: [0.02, 0.02] + ballasts: + - variable_flag: True + grid: [0.0, 1.0] + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'lower_pontoon_DS' + joint1: 'downstream_lower_pon' + joint2: 'starboard_lower_pon' + outer_shape: *pon_out + internal_structure: *pon_int_lower + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'lower_pontoon_UP' + joint1: 'upstream_lower_pon' + joint2: 'port_lower_pon' + outer_shape: *pon_out + internal_structure: *pon_int_lower + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'lower_pontoon_US' + joint1: 'upstream_lower_pon' + joint2: 'starboard_lower_pon' + outer_shape: *pon_out + internal_structure: *pon_int_lower + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + # axial_joints: + # - {name: '', grid: 0} + - name: 'lower_pontoon_PS' + joint1: 'port_lower_pon' + joint2: 'starboard_lower_pon' + outer_shape: *pon_out + internal_structure: *pon_int_upper # no ballast because it fills from one end to other + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + axial_joints: + - {name: 'lower_brace', grid: 0.5} + - name: 'tower_brace_port' + joint1: 'lower_brace' + joint2: 'port_upper_pon' + outer_shape: *pon_out + internal_structure: *pon_int_lower + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + - name: 'tower_brace_starboard' + joint1: 'lower_brace' + joint2: 'starboard_upper_pon' + outer_shape: *pon_out + internal_structure: *pon_int_lower + Ca: [1.0,1.0] + Cd: [1.2,1.2] + Cp: 1.0 + + transition_piece_mass: 0 + transition_piece_cost: 0 + mooring: + nodes: + - {name: 'line1_anchor', node_type: fixed, joint: 'anchor_1', anchor_type: drag_embedment} + - {name: 'line2_anchor', node_type: fixed, joint: 'anchor_2', anchor_type: drag_embedment} + - {name: 'line3_anchor', node_type: fixed, joint: 'anchor_3', anchor_type: drag_embedment} + - {name: 'line4_anchor', node_type: fixed, joint: 'anchor_4', anchor_type: drag_embedment} + - {name: 'line5_anchor', node_type: fixed, joint: 'anchor_5', anchor_type: drag_embedment} + - {name: 'line6_anchor', node_type: fixed, joint: 'anchor_6', anchor_type: drag_embedment} + - {name: 'line1_fairlead', node_type: vessel, joint: 'upstream_lower_col', fairlead_type: rigid} + - {name: 'line2_fairlead', node_type: vessel, joint: 'upstream_lower_col', fairlead_type: rigid} + - {name: 'line3_fairlead', node_type: vessel, joint: 'upstream_lower_col', fairlead_type: rigid} + - {name: 'line4_fairlead', node_type: vessel, joint: 'downstream_lower_col', fairlead_type: rigid} + - {name: 'line5_fairlead', node_type: vessel, joint: 'downstream_lower_col', fairlead_type: rigid} + - {name: 'line6_fairlead', node_type: vessel, joint: 'downstream_lower_col', fairlead_type: rigid} + lines: + - {name: 'line1', node1: 'line1_anchor', node2: 'line1_fairlead', line_type: 'main', unstretched_length: 160} + - {name: 'line2', node1: 'line2_anchor', node2: 'line2_fairlead', line_type: 'main', unstretched_length: 160} + - {name: 'line3', node1: 'line3_anchor', node2: 'line3_fairlead', line_type: 'main', unstretched_length: 160} + - {name: 'line4', node1: 'line4_anchor', node2: 'line4_fairlead', line_type: 'main', unstretched_length: 160} + - {name: 'line5', node1: 'line5_anchor', node2: 'line5_fairlead', line_type: 'main', unstretched_length: 152} + - {name: 'line6', node1: 'line6_anchor', node2: 'line6_fairlead', line_type: 'main', unstretched_length: 152} + line_types: + - name: 'main' + diameter: 0.324 + type: custom + mass_density: 644.8 + stiffness: 85.4E+8 + damping: 0 + transverse_added_mass: 1.0 + tangential_added_mass: 0.5 + transverse_drag: 2.4 + tangential_drag: 1.15 + breaking_load: 1.0E+10 + cost: 100 + anchor_types: + - {name: drag_embedment, type: drag_embedment} +airfoils: + - name: NACA6_1000 + coordinates: + x: [1.0, 0.993181, 0.972909, 0.939737, 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http://www.matweb.com/search/DataSheet.aspx?MatGUID=38108bfd64c44b4c9c6a02af78d5b6c6, orth: 0, rho: 7850, alpha: 0.0, E: 205000000000.0, nu: 0.3, G: 80000000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 814000000.0, Xc: 814000000.0, S: 0, Xy: 485000000.0, m: 3, unit_cost: 0.9} + - {name: cast_iron, description: Cast iron for hub and nacelle components, source: TODO, orth: 0, rho: 7200, alpha: 0.0, E: 118000000000.0, nu: 0.3, G: 47600000000.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 310000000.0, Xc: 310000000.0, S: 0, Xy: 265000000.0, m: 3, unit_cost: 0.5} + - name: glass_uni + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1 + rho: 1940.0 + E: [44600000000.0, 17000000000.0, 16700000000.0] + G: [3270000000.0, 3480000000.0, 3500000000.0] + nu: [0.262, 0.35, 0.264] + Xt: [609200000.0, 38100000.0, 15290000.0] + Xc: [474710000.0, 112640000.0, 113220000.0] + S: [18910000.0, 17240000.0, 13160000.0] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.005 + unit_cost: 1.87 + waste: 0.05 + fiber_density: 2535.5 + area_density_dry: 7.227162215457267 + component_id: 5 + - name: CarbonUD + E: [114500000000.0, 8390000000.0, 8390000000.0] + G: [5990000000.0, 5990000000.0, 5990000000.0] + rho: 1220.0 + orth: 1 + nu: [0.27, 0.27, 0.27] + Xt: [1546000000.0, 0.0, 0.0] + Xc: [1047000000.0, 0.0, 0.0] + S: [0.0, 0.0, 0.0] + m: 16.1 + GIc: 0.0 + GIIc: 0.0 + alp0: 0.0 + fvf: 0.1076923076923077 + fwf: 0.15889029003783103 + ply_t: 0.005158730158730159 + unit_cost: 30.0 + waste: 0.05 + fiber_density: 1800.0 + area_density_dry: 1.0 + component_id: 4 + - name: glass_biax + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1 + rho: 1940.0 + E: [11100000000.0, 11100000000.0, 16700000000.0] + G: [13530000000.0, 3490000000.0, 3490000000.0] + nu: [0.5, 0.0, 0.066] + Xt: [42900000.0, 42600000.0, 15300000.0] + Xc: [70700000.0, 70700000.0, 113200000.0] + S: [103400000.0, 17200000.0, 13200000.0] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + waste: 0.15 + unit_cost: 3.0 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 3 + roll_mass: 181.4368 + - name: glass_triax + description: Vectorply E-LT-5500, Epikote MGS RIMR 135/Epicure MGS RIMH 1366 epoxy + source: MSU composites database 3D property tests, Engineering Mechanics of Composite Materials, Daniel, I & Ishai, O., 1994, pg. 34 + orth: 1.0 + rho: 1940.0 + E: [28700000000.0, 16600000000.0, 16700000000.0] + G: [8400000000.0, 3490000000.0, 3490000000.0] + nu: [0.5, 0.0, 0.17] + Xt: [396000000.0, 76400000.0, 15300000.0] + Xc: [448900000.0, 174700000.0, 113200000.0] + S: [103400000.0, 17200000.0, 13200000.0] + m: 10 + GIc: 303 + GIIc: 3446 + alp0: 53 + fvf: 0.57 + fwf: 0.7450682696347697 + ply_t: 0.001 + unit_cost: 2.86 + waste: 0.15 + fiber_density: 2535.5 + area_density_dry: 1.4454324430914534 + component_id: 2 + roll_mass: 181.4368 + - {name: medium_density_foam, description: "Airex C70.130 PVC Foam, source 'https://www.3accorematerials.com/uploads/documents/TDS-AIREX-C70-E_1106.pdf'", orth: 0.0, rho: 130.0, E: 129200000.0, G: 48946969.696969695, nu: 0.32, Xt: 2083000.0, Xc: 1563000.0, S: 1250000.0, GIc: 303, GIIc: 3446, alp0: 53, component_id: 1, waste: 0.2, unit_cost: 13} + - {name: resin, description: epoxy, E: 1000000.0, nu: 0.3, G: 312500.0, GIc: 0, GIIc: 0, alp0: 0, Xt: 0, Xc: 0, S: 0, rho: 1150.0, alpha: 0.0, orth: 0, unit_cost: 3.63} + - {name: slurry, description: fixed/permanent ballast, E: 1.0, nu: 0.3, G: 1.0, Xt: 0, Xc: 0, S: 0, rho: 5000.0, alpha: 0.0, unit_cost: 1.0, orth: 0.0} + - {name: adhesive, description: Sample adhesive, source: https://www.nrel.gov/docs/fy19osti/73585.pdf, orth: 0, rho: 1100, E: 4560000.0, nu: 0.49, alpha: 0.0, Xt: 690000.0, Xc: 400000.0, S: 310000.0, G: 1520000.0, unit_cost: 9.0} +control: + supervisory: {Vin: 0.5, Vout: 4.0, maxTS: 60} + pitch: {PC_zeta: !!null '', PC_omega: !!null '', ps_percent: 1.0, max_pitch: !!null '', max_pitch_rate: 0.1745, min_pitch: 0.00088} + torque: {control_type: !!null '', tsr: 7.64, VS_zeta: !!null '', VS_omega: !!null '', max_torque_rate: 1500000.0, VS_minspd: 0.0, VS_maxspd: 1.26711} + setpoint_smooth: {ss_vsgain: !!null '', ss_pcgain: !!null ''} + shutdown: {limit_type: !!null '', limit_value: !!null ''} +environment: {air_density: 1.225, air_dyn_viscosity: 1.81e-05, speed_sound: 1500.0, shear_exp: 0.0, gravity: 9.80665, weib_shape_parameter: 2.0, water_density: 1025.0, water_dyn_viscosity: 0.0013351, soil_shear_modulus: 140000000.0, soil_poisson: 0.4, water_depth: 50.0, air_pressure: 101325.0, air_vapor_pressure: 2500.0, significant_wave_height: 0.5, significant_wave_period: 2.0} +bos: {plant_turbine_spacing: 7, plant_row_spacing: 7, commissioning_pct: 0.01, decommissioning_pct: 0.15, distance_to_substation: 1.0, distance_to_interconnection: 8.5, interconnect_voltage: 130.0, distance_to_site: 115.0, distance_to_landfall: 50.0, port_cost_per_month: 2000000.0, site_auction_price: 100000000.0, site_assessment_plan_cost: 1000000.0, site_assessment_cost: 25000000.0, construction_operations_plan_cost: 2500000.0, boem_review_cost: 0.0, design_install_plan_cost: 2500000.0} +costs: {wake_loss_factor: 0.15, fixed_charge_rate: 0.056, bos_per_kW: 4053.0, opex_per_kW: 137.0, turbine_number: 40.0, labor_rate: 58.8, painting_rate: 30.0, blade_mass_cost_coeff: 14.6, hub_mass_cost_coeff: 3.9, pitch_system_mass_cost_coeff: 22.1, spinner_mass_cost_coeff: 11.1, lss_mass_cost_coeff: 11.9, bearing_mass_cost_coeff: 4.5, gearbox_mass_cost_coeff: 12.9, hss_mass_cost_coeff: 6.8, generator_mass_cost_coeff: 12.4, bedplate_mass_cost_coeff: 2.9, yaw_mass_cost_coeff: 8.3, converter_mass_cost_coeff: 18.8, transformer_mass_cost_coeff: 18.8, hvac_mass_cost_coeff: 124.0, cover_mass_cost_coeff: 5.7, elec_connec_machine_rating_cost_coeff: 41.85, platforms_mass_cost_coeff: 17.1, tower_mass_cost_coeff: 2.9, controls_machine_rating_cost_coeff: 21.15, crane_cost: 12000.0, electricity_price: 0.04, reserve_margin_price: 120.0, capacity_credit: 0.0, benchmark_price: 0.071} diff --git a/examples/19_DFSM/analysis_options_dfsm_mhk.yaml b/examples/19_DFSM/analysis_options_dfsm_mhk.yaml new file mode 100644 index 000000000..b9dff5c25 --- /dev/null +++ b/examples/19_DFSM/analysis_options_dfsm_mhk.yaml @@ -0,0 +1,46 @@ +general: + folder_output: outputs/RM1_test + fname_output: iea15mw + +design_variables: + control: + servo: + pitch_control: + omega: + flag: True + min: 0.1 + max: 1.5 + zeta: + flag: True + min: 0.1 + max: 3.0 + +merit_figure: DEL_TwrBsMyt # Merit figure of the optimization problem. The options are 'AEP' - 'LCOE' - 'Cp' - 'blade_mass' - 'blade_tip_deflection' + +constraints: + control: + rotor_overspeed: + flag: False + min: 0.0 + max: 0.2 + +driver: + optimization: + flag: False + tol: 1.e-2 # Optimality tolerance + max_major_iter: 2 # Maximum number of major design iterations (SNOPT) + max_minor_iter: 100 # Maximum number of minor design iterations (SNOPT) + max_iter: 2 # Maximum number of iterations (SLSQP) + solver: LN_COBYLA # Optimization solver. Other options are 'SLSQP' - 'CONMIN' + step_size: 1.e-3 # Step size for finite differencing + form: forward # Finite differencing mode, either forward or central + design_of_experiments: + flag: True # Flag to enable design of experiments + run_parallel: False # Flag to run using parallel processing + generator: FullFact # Type of input generator. (Uniform) + num_samples: 1 # number of samples for (Uniform only) + + +recorder: + flag: True # Flag to activate OpenMDAO recorder + file_name: log_opt.sql # Name of OpenMDAO recorder diff --git a/examples/19_DFSM/construct_LPV.m b/examples/19_DFSM/construct_LPV.m new file mode 100644 index 000000000..b247beaba --- /dev/null +++ b/examples/19_DFSM/construct_LPV.m @@ -0,0 +1,136 @@ +function [A,B,C,D,x,x_CD] = construct_LPV(x0,x0_CD,n_var,ns,nc,ny,inputs,state_dx,outputs,gaopt,fminconopt) + + rng(34535) + + % set options + options_ga = optimoptions('ga','UseParallel',true,'Display','iter','MaxGenerations',n_var*2); + + m = mean(inputs,1); + m(2) = m(2)/1e4; + disp(m) + + % solve for C and D matrices + CD = linsolve(inputs,outputs); + CD = CD'; + + C = CD(:,nc+1:end); + D = CD(:,1:nc); + + x0_CD = CD(:); + + % set fmincon options + options_fmincon = optimoptions('fmincon','Display','iter','Algorithm','sqp','MaxIterations',500,'MaxFunctionEvaluations',20000,'UseParallel',true,"EnableFeasibilityMode",true,'StepTolerance',1e-14,'ConstraintTolerance',1e-3); + options_fmincon = optimoptions('fmincon','Display','iter','Algorithm','interior-point','MaxIterations',500,'MaxFunctionEvaluations',20000,'UseParallel',true,"EnableFeasibilityMode",true,'StepTolerance',1e-8,'ConstraintTolerance',1e-4); + n_CD = length(x0_CD); + + if isempty(x0) + + % if x0 is empty, then it implies that this is the first call, and + % we should use ga to find a good starting point + + x = ga(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'deriv'),n_var,[],[],[],[],[],[],@(x)constraints(x,ns,nc),[],options_ga) ; + + [x,F] = fmincon(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'deriv'),x,[],[],[],[],[],[],@(x)constraints(x,ns,nc),options_fmincon); + + %disp('Estimating C and D matrices') + % x_CD = ga(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'output'),n_CD,[],[],[],[],[],[],[],[],options_ga); + % + % x_CD = fmincon(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'output'),x_CD,[],[],[],[],[],[],[],options_fmincon); + + else + + % else, use fmincon to find the model parameters + [x,F] = fmincon(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'deriv'),x0,[],[],[],[],[],[],@(x)constraints(x,ns,nc),options_fmincon); + %disp('Estimating C and D matrices') + %x_CD = ga(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'output'),n_CD,[],[],[],[],[],[],[],[],options_ga); + + %x_CD = fmincon(@(x) objective(x,ns,nc,ny,inputs,state_dx,outputs,'output'),x0_CD,[],[],[],[],[],[],[],options_fmincon); + end + + + % get linear model + [A,B] = LTI_function(x,ns,nc); + x_CD = []; + + % estimate CD + + + + %[C,D] = output_function(x_CD,ns,nc,ny); + + +end + + +function V = objective(x,ns,nc,ny,inputs,dx_act,outputs,func) + + if strcmpi(func,'deriv') + + % get linear model + [A,B] = LTI_function(x,ns,nc); + + % evaluate state derivatives + dx_predicted = inputs*[B,A]'; + + elseif strcmpi(func,'output') + + [C,D] = output_function(x,ns,nc,ny); + + dx_predicted = inputs*[D,C]'; + + dx_act = outputs; + end + + % number of data samples + N = length(inputs); + + % calculate error + error = dx_act - dx_predicted; + + % calculate loss + V = 1/N*(trace(error'*error)); + +end + +function [C,D] = output_function(x,ns,nc,ny) + +CD = reshape(x,[ny,ns+nc]); + +C = CD(:,nc+1:end); +D = CD(:,1:nc); + + +end + + + +function [A,B] = LTI_function(x,ns,nc) + + + % reshape + x = reshape(x,[ns/2,ns+nc]); + + % extract right elements + B_par = x(:,1:nc); + A_par = x(:,nc+1:nc+ns); + + A = [zeros(ns/2),eye(ns/2);A_par]; + B = [zeros(ns/2,nc);B_par]; + +end + +function [c,ceq] = constraints(x,ns,nc) + + % get the linear model + [A,~] = LTI_function(x,ns,nc); + + % evaluate the eigen values + eigA = eig(A); + + % real values + c = max(real(eigA))+0.0; + + % no equality constraints + ceq = []; + +end diff --git a/examples/19_DFSM/construct_LPV_matlab.py b/examples/19_DFSM/construct_LPV_matlab.py new file mode 100644 index 000000000..c8d911cc8 --- /dev/null +++ b/examples/19_DFSM/construct_LPV_matlab.py @@ -0,0 +1,447 @@ +import matplotlib.pyplot as plt +import numpy as np +import os +import pickle + +from scipy.interpolate import CubicSpline,interp1d + + +import time as timer + +# DFSM modules +from weis.dfsm.simulation_details import SimulationDetails +from weis.dfsm.dfsm_utilities import valid_extension +from weis.dfsm.test_dfsm import test_dfsm +from weis.dfsm.construct_dfsm import DFSM +from weis.dfsm.evaluate_dfsm import evaluate_dfsm +from weis.dfsm.dfsm_sample_data import sample_data +from weis.dfsm.wrapper_LTI import wrapper_LTI + + +from mat4py import loadmat +from numpy.linalg import lstsq + +from weis.dfsm.dfsm_rosco_simulation import run_sim_ROSCO +from sklearn.preprocessing import StandardScaler, MinMaxScaler + +import argparse + + +import matlab.engine + +def ModelData(): + + model_data = {} + + model_data['reqd_states'] = ['PtfmPitch','PtfmHeave','GenSpeed'];ns = len(model_data['reqd_states']) + model_data['reqd_controls'] = ['RtVAvgxh','GenTq','BldPitch1','Wave1Elev'];nc = len(model_data['reqd_controls']) + model_data['reqd_outputs'] = ['TwrBsFxt','TwrBsMyt','YawBrTAxp','NcIMURAys','GenPwr','RtFldCp','RtFldCt'] + + model_data['datapath'] = '/home/athulsun/DFSM/data' + os.sep + 'MHK_1p1_full' + + scale_args = {'state_scaling_factor': np.array([1,1,1]), + 'control_scaling_factor': np.array([1,1,1,1]), + 'output_scaling_factor': np.array([1,1,1,1,1,1,1]) + } + + model_data['scale_args'] = scale_args + + model_data['W'] = np.array([0.5,0.75,1.0,1.25,1.5,1.75,2.0,2.25,2.5,2.75,3.0]) + + n_var = ns*(nc+2*ns) + + model_data['n_var'] = n_var + model_data['lb'] = [-np.inf]*n_var + model_data['ub'] = [np.inf]*n_var + + + model_data['gaopt'] = {'UseParallel':1,'Display':'iter','MaxGenerations':n_var*2} + model_data['fminconopt'] = {'Display':'iter','Algorithm':'interior-point','MaxIterations':500,'MaxFunctionEvaluations':20000, + 'UseParallel':1,"EnableFeasibilityMode":1,'StepTolerance':1e-8,'ConstraintTolerance':1e-4} + + model_data['nseeds'] = 7 + + model_data['test_inds'] = np.array([56,66,76]) + + model_data['w_start'] = 2.0 + + return model_data + + +if __name__ == '__main__': + + # path to this directory + this_dir = os.path.dirname(os.path.abspath(__file__)) + + # datapath + region = 'LPV' + + model_data = ModelData() + datapath = model_data['datapath'] #this_dir + os.sep + 'outputs' + os.sep + 'FOWT_1p6' #+ os.sep + 'openfast_runs/rank_0' + + # get the path to all .outb files in the directory + outfiles = [os.path.join(datapath,f) for f in os.listdir(datapath) if valid_extension(f)] + outfiles = sorted(outfiles) + + # required states + reqd_states = model_data['reqd_states'] + reqd_controls = model_data['reqd_controls'] + reqd_outputs = model_data['reqd_outputs'] + + ns = len(reqd_states) + nc = len(reqd_controls) + ny = len(reqd_outputs) + + + # scaling parameters + scale_args = model_data['scale_args'] + + # filter parameters + filter_args = {'state_filter_flag': [True]*ns, + 'state_filter_type': [['filtfilt']]*ns, + 'state_filter_tf': [[0.1]]*ns, + 'control_filter_flag': [False]*nc, + 'control_filter_tf': [0]*nc, + 'output_filter_flag': [] + } + + # name of mat file that has the linearized models + mat_file_name = None #this_dir + os.sep + model_data['mat_file_name'] + + # instantiate class + sim_detail = SimulationDetails(outfiles, reqd_states,reqd_controls,reqd_outputs,scale_args,filter_args,tmin=00 + ,add_dx2 = True,linear_model_file = mat_file_name,region = region) + + # load and process data + sim_detail.load_openfast_sim() + + + # extract data + FAST_sim = sim_detail.FAST_sim + + + nseeds = model_data['nseeds'] + W = model_data['W'] + n_var = model_data['n_var'] + + nW = len(W) + + + FAST_sim_array = np.array(FAST_sim) + FAST_sim = np.reshape(FAST_sim_array,[nseeds,nW],order = 'F') + + + train_inds = np.arange(0,5) + + + n_samples = 1 + + w_start = model_data['w_start'] + + ind_start = np.where(W == w_start)[0][0] + ind_forward_pass = np.where(W >= w_start)[0] + ind_backward_pass = np.flip(np.where(W < w_start))[0] + + A_array = np.zeros((nW,ns*2,ns*2)) + B_array = np.zeros((nW,2*ns,nc)) + C_array = np.zeros((nW,ny,2*ns)) + D_array = np.zeros((nW,ny,nc)) + + X_array = np.zeros((nW,n_var)) + + model_construct_time = np.zeros((nW,)) + + test_inds = model_data['test_inds'] + + # Start the MATLAB Engine + eng = matlab.engine.start_matlab() + + X_matlab = np.array((nW),dtype = object) + X_cd_array = np.zeros((nW),dtype = object) + + LTI = wrapper_LTI(nstates = int(2*ns),ncontrols = int(nc)) + + print('started matlab engine') + + # perform forward pass + for ind in range(len(ind_forward_pass)): + + iw = ind_forward_pass[ind] + + _,_,_,inputs,state_dx,outputs = sample_data(FAST_sim[train_inds,iw],'KM',n_samples = 1) + + # calculate the C and D matrices + CD = lstsq(inputs,outputs,rcond = None) + CD = CD[0] + + CD = CD.T + + C_ = CD[:,nc:] + D_ = CD[:,:nc] + + + inputs_ = matlab.double(inputs) + state_dx_ = matlab.double(state_dx) + outputs_ = matlab.double(outputs) + + n_var = matlab.double(n_var) + ns2 = matlab.double(2*ns) + nc_ = matlab.double(nc) + ny_ = matlab.double(ny) + + + if W[iw] == w_start: + + x0 = matlab.double([]) + + t1 = timer.time() + A,B,C,D,x,x_cd = eng.construct_LPV(x0,x0,n_var,ns2,nc_,ny_,inputs_,state_dx_,outputs_,model_data['gaopt'],model_data['fminconopt'],nargout = 6) + t2 = timer.time() + + else: + + x0 = matlab.double(X_array[iw-1]) + x0_cd = matlab.double(X_cd_array[iw-1]) + + t1 = timer.time() + A,B,C,D,x,x_cd = eng.construct_LPV(x0,x0_cd,n_var,ns2,nc_,ny_,inputs_,state_dx_,outputs_,model_data['gaopt'],model_data['fminconopt'],nargout = 6) + t2 = timer.time() + + + # stop timer + model_construct_time[iw] = t2-t1 + + # store soultion + X_array[iw,:] = np.squeeze(np.array(x)) + X_cd_array[iw] = x_cd + + # convert the A,B,C,D matrices to np arrays + A = np.array(A);B = np.array(B) + C = np.array(C); D = np.array(D) + + + C[3,:] = A[3,:];#C[2,:] = A[4,:] + D[3,:] = B[3,:];#D[2,:] = B[4,:] + + # store linear models + A_array[iw,:,:] = A + B_array[iw,:,:] = B + C_array[iw,:,:] = C + D_array[iw,:,:] = D + + # perform backward pass + for ind in range(len(ind_backward_pass)): + + iw = ind_backward_pass[ind] + + _,_,_,inputs,state_dx,outputs = sample_data(FAST_sim[train_inds,iw],'KM',n_samples = 1) + + inputs_ = matlab.double(inputs) + state_dx_ = matlab.double(state_dx) + outputs_ = matlab.double(outputs) + + + # calculate the C and D matrices + CD = lstsq(inputs,outputs,rcond = None) + CD = CD[0] + + CD = CD.T + + C_ = CD[:,nc:] + D_ = CD[:,:nc] + + C_array[iw,:,:] = C + D_array[iw,:,:] = D + + x0 = matlab.double(X_array[iw+1]) + x0_cd = X_cd_array[iw+1] + + + t1 = timer.time() + A,B,C,D,x,x_cd = eng.construct_LPV(x0,x0_cd,n_var,ns2,nc_,ny_,inputs_,state_dx_,outputs_,model_data['gaopt'],model_data['fminconopt'],nargout = 6) + t2 = timer.time() + + model_construct_time[iw] = t2-t1 + + # store soultion + X_array[iw,:] = np.squeeze(np.array(x)) + X_cd_array[iw] = x_cd + + A = np.array(A);B = np.array(B) + C = np.array(C); D = np.array(D); + + C[3,:] = A[3,:];#C[2,:] = A[4,:] + D[3,:] = B[3,:];#D[2,:] = B[4,:] + + # store linear models + A_array[iw,:,:] = A + B_array[iw,:,:] = B + C_array[iw,:,:] = C + D_array[iw,:,:] = D + + print('shutting matlab down') + eng.quit() + + A0 = np.squeeze(A_array[0,:,:]);A1 = np.squeeze(A_array[-1,:,:]) + B0 = np.squeeze(B_array[0,:,:]);B1 = np.squeeze(B_array[-1,:,:]) + C0 = np.squeeze(C_array[0,:,:]);C1 = np.squeeze(C_array[-1,:,:]) + D0 = np.squeeze(D_array[0,:,:]);D1 = np.squeeze(D_array[-1,:,:]) + + interp_type = 'nearest' + + # A_fun = interp1d(W,A_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (A0,A1)) + # B_fun = interp1d(W,B_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (B0,B1)) + # C_fun = interp1d(W,C_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (C0,C1)) + # D_fun = interp1d(W,D_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (D0,D1)) + + # construct surrogate model + dfsm_python = DFSM(sim_detail,n_samples = n_samples,L_type = 'LPV',N_type = None, train_split = 0.5) + + dfsm_python.A_array = A_array + dfsm_python.B_array = B_array + dfsm_python.C_array = C_array + dfsm_python.D_array = D_array + dfsm_python.W = W + + dfsm_python.setup_LPV(interp_type) + + dfsm_python.AB = [] + dfsm_python.CD = [] + + dfsm_python.error_ind_deriv = [] + dfsm_python.error_ind_outputs = [] + + dfsm_python.dx_error = [] + dfsm_python.test_data = [] + dfsm_python.train_data =[] + + dfsm_python.nonlin_deriv = [] + dfsm_python.nonlin_outputs = [] + dfsm_python.nonlin_output = False + + + dfsm_python.scaler_dx = None + dfsm_python.scaler_outputs = None + dfsm_python.nonlin_deriv = np.array([None]) + dfsm_python.nonlin_outputs = np.array([None]) + + + + # load results from matlab + save_flag = True;save_dict_flag = True;plot_flag = False + plot_path = 'LPV_results_comp' + + + # test dfsm + # flags related to testing + simulation_flag = True + outputs_flag = (len(reqd_outputs) > 0) + plot_flag = True + #dfsm,U_list,X_list_matlab,dx_list,Y_list_matlab = test_dfsm(dfsm_matlab,FAST_sim_array,test_inds,simulation_flag,plot_flag) + dfsm,U_list,X_list_python,dx_list,Y_list_python = test_dfsm(dfsm_python,FAST_sim_array,test_inds,simulation_flag,plot_flag) + dfsm.train_data = [];dfsm.test_data = [] + print(dfsm.simulation_time) + + # plot properties + markersize = 10 + linewidth = 1.5 + fontsize_legend = 16 + fontsize_axlabel = 18 + fontsize_tick = 12 + + + for idx,ind in enumerate(test_inds): + + #time = results_mat['time'] + time = X_list_python[idx]['time'] + + U = U_list[idx]['OpenFAST'] + + #x_matlab = X_list_matlab[idx]['DFSM'] + x_python = X_list_python[idx]['DFSM'] + x_OF = X_list_python[idx]['OpenFAST'] + + #y_matlab = Y_list_matlab[idx]['DFSM'] + y_python = Y_list_python[idx]['DFSM'] + y_OF = Y_list_python[idx]['OpenFAST'] + + + ns = len(reqd_states) + ny = len(reqd_outputs) + current_speed = [] + + if plot_flag: + + current_speed.append(np.round(np.mean(U[:,0]),2)) + + fig,ax = plt.subplots(1) + ax.plot(time,U[:,0]) + ax.tick_params(labelsize=fontsize_tick) + ax.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + ax.set_title('RtVAvgxh',fontsize = fontsize_axlabel) + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ 'RtVAvgxh_' +str(idx)+ '.pdf') + plt.close(fig) + + + + for i in range(ns): + + fig,ax1 = plt.subplots(1) + + ax1.plot(time,x_OF[:,i],label = 'OpenFAST') + ax1.plot(time,x_python[:,i],label = 'python',alpha = 0.8) + + ax1.tick_params(labelsize=fontsize_tick) + ax1.legend(ncol = 3,fontsize = fontsize_legend) + ax1.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + ax1.set_title(reqd_states[i],fontsize = fontsize_axlabel) + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ reqd_states[i] +'_' +str(idx)+ '_comp.pdf') + plt.close(fig) + + for i in range(ny): + + fig,ax1 = plt.subplots(1) + + ax1.plot(time,y_OF[:,i],label = 'OpenFAST') + ax1.plot(time,y_python[:,i],label = 'python',alpha = 0.8) + + ax1.tick_params(labelsize=fontsize_tick) + ax1.legend(ncol = 3,fontsize = fontsize_legend) + ax1.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + ax1.set_title(reqd_outputs[i],fontsize = fontsize_axlabel) + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ reqd_outputs[i] +'_' +str(idx)+ '_comp.pdf') + plt.close(fig) + + + + + + plt.show() + + dfsm_python_pkl = 'dfsm_mhk.pkl' + + with open(dfsm_python_pkl,'wb') as handle: + pickle.dump(dfsm_python,handle) + + results_dict = {'U_list':U_list,'X_list':X_list_python,'dx_list':dx_list,'Y_list':Y_list_python,'DFSM':dfsm,'current_speed':current_speed} + + + + + + \ No newline at end of file diff --git a/examples/19_DFSM/dfsm_mhk.pkl b/examples/19_DFSM/dfsm_mhk.pkl new file mode 100644 index 000000000..0d6551597 Binary files /dev/null and b/examples/19_DFSM/dfsm_mhk.pkl differ diff --git a/examples/19_DFSM/modeling_options_dfsm_mhk.yaml b/examples/19_DFSM/modeling_options_dfsm_mhk.yaml new file mode 100644 index 000000000..b8197da05 --- /dev/null +++ b/examples/19_DFSM/modeling_options_dfsm_mhk.yaml @@ -0,0 +1,181 @@ +General: + verbosity: False # When set to True, the code prints to screen many infos + openfast_configuration: + OF_run_fst: RM1_test + # OF_run_dir: outputs/05_low_shear_hi_TI + save_timeseries: True + save_iterations: True + use_exe: True + allow_fails: True + fail_value: 9999 + + +WISDEM: + RotorSE: + flag: False + n_pitch_perf_surfaces: 5 + n_tsr_perf_surfaces: 5 + spar_cap_ss: Spar_Cap_SS + spar_cap_ps: Spar_Cap_PS + te_ss: TE_reinforcement_SS + te_ps: TE_reinforcement_PS + TowerSE: + flag: False + DriveSE: + flag: False + FloatingSE: + flag: False + rank_and_file: True + frame3dd: + flag: False + BOS: + flag: False + +Level3: # Options for WEIS fidelity level 3 = nonlinear time domain + flag: False + from_openfast: True + openfast_file: MHK_RM1_Floating.fst + openfast_dir: ../01_aeroelasticse/OpenFAST_models/RM1 + simulation: + DT: 0.005 + CompElast: 1 + CompInflow: 1 + CompAero: 2 + CompServo: 1 + # CompHydro: 0 + # CompSub: 0 + # CompMooring: 3 + # CompIce: 0 + OutFileFmt: 3 + NumCrctn: 5 + DT_UJac: 1 + MHK: 2 + WtrDpth: 50 + linearization: + Linearize: False + ElastoDyn: + FlapDOF1: False + FlapDOF2: False + EdgeDOF: False + TeetDOF: False + DrTrDOF: False + GenDOF: True + YawDOF: False + TwFADOF1 : False + TwFADOF2 : False + TwSSDOF1 : False + TwSSDOF2 : False + PtfmSgDOF: True + PtfmSwDOF: False + PtfmHvDOF: True + PtfmRDOF : False + PtfmPDOF : True + PtfmYDOF : False + TwrNodes: 20 + DTTorSpr: 600000 + DTTorDmp: 100000 + HubMass: 140 + HubIner: 79.6 + GenIner: 139.5 + NacMass: 40100 + NacYIner: 244643 + YawBrMass: 0 + HubCM: 0.2222 + NacCMxn: 0.43 + NacCMyn: 0 + NacCMzn: -1.2 + TowerBsHt: -9 + HydroDyn: + # WaveSeed1: 123456789 + # WaveSeed2: 123456789 + # AddBLin3: [0.0, 0.0,0.0, 0.0, 0.0, 0.0] + WaveMod: 2 + # WaveHs: 2.0 + # WaveTp: 6.75 + # WaveTMax: 600 + # WvLowCOff: 0.314159 + # WvHiCOff: 1.570796 + # WvSumQTF: False + # RdtnMod: 0 + # PotMod: 1 + # ExctnMod: 0 + # DiffQTF: 0 + # PotFile: examples/01_aeroelasticse/OpenFAST_models/MHK-RM1/MHK-RM1-Floating/HydroData/MHK_RM1_Floating + # ServoDyn: + # PitManRat(1): 2. + # PitManRat(2): 2. + # PitManRat(3): 2. + # SubDyn: + # SDdeltaT: 0.005 + + # InflowWind: + # RefHt: 150.0 + +DFSM: + flag: True + general_options: + dfsm_file: dfsm_mhk.pkl + save_results: True + usecase: closed-loop-simulation + model_options: + ode_method: ABM4 + interp_type: nearest + reqd_states: [PtfmPitch,PtfmHeave,GenSpeed] + reqd_controls: [RtVAvgxh,GenTq,BldPitch1,Wave1Elev] + reqd_outputs: [TwrBsFxt,TwrBsMyt,YawBrTAxp,NcIMURAys,GenPwr,RtFldCt,RtFldCp] #,RtFldCt,RtFldCp + scale_args: + state_scaling_factor: [1,1,1] + filter_args: + state_filter_flag: [True,True,True] + state_filter_type: [[filtfilt],[filtfilt],[filtfilt]] + state_filter_tf: [[0.1],[0.1],[0.1]] + + +ROSCO: + flag: True + tuning_yaml: ../01_aeroelasticse/OpenFAST_models/RM1/RM1_MHK.yaml + PRC_Mode: 0 + Fl_Mode: 2 + + +DLC_driver: + metocean_conditions: + current_speed: [0.5 , 0.75, 1. , 1.25, 1.5 , 1.75, 2. , 2.25, 2.5 , 2.75, 3.] + current_std: [0.100,0.096,0.093,0.089,0.086,0.082,0.079,0.075,0.071,0.068,0.064] + wave_height_NSS: [0.84,0.84,0.87,0.99,1.15,1.34,1.58,1.82,2.08,2.34,2.66] + wave_period_NSS: [8.3 , 8.3 , 8.3 , 7.7 , 7.1 , 6.3 , 6.1 , 6.2 , 6.2 , 6.7 , 7.1] + wave_height_fatigue: [0.84,0.84,0.87,0.99,1.15,1.34,1.58,1.82,2.08,2.34,2.66] + wave_period_fatigue: [8.3 , 8.3 , 8.3 , 7.7 , 7.1 , 6.3 , 6.1 , 6.2 , 6.2 , 6.7 , 7.1] + wave_height_SSS: [9.7,9.7,9.7,9.7,9.7,9.7,9.7,9.7,9.7,9.7,9.7] + wave_period_SSS: [13.6,13.6,13.6,13.6,13.6,13.6,13.6,13.6,13.6,13.6,13.6,] + wave_height1: 5.9 + wave_period1: 11.2 + wave_height50: 9.7 + wave_period50: 13.6 + current_50: 4.5 + user_probability: + speed: [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, 3.6] + probability: [0.176358711313469, 0.225347242233877, 0.233415941444297, 0.231686934470636, 0.210362515128811, 0.213820529076134, 0.240331969338943, 0.228228920523313, 0.245518990259927, 0.245518990259927, 0.253011353812460, 0.275488444470059, 0.293354849864562, 0.327934989337790, 0.352717422626938, 0.374041841968762, 0.391908247363264, 0.447812806178319, 0.501412022361823, 0.577488329202929, 0.566537951703071, 0.586709699729122, 0.515244078151115, 0.510633392888018, 0.442625785257335, 0.383839548152844, 0.301423549074981, 0.262809059996542, 0.169442683418823, 0.0829923347357504, 0.0547518874992795, 0.0443778456573108, 0.0190190767102761, 0.00806869921042014, 0.00403434960521009, 0.00172900697366146] + DLCs: + - DLC: "1.1" + current_speed: [1.75,2.,2.25,2.5,2.75,3.0] + n_seeds: 1 + analysis_time: 600 + transient_time: 200 + turbsim_inputs: + PLExp: 0 + TurbModel: TIDAL + # - DLC: "1.2" + # # current_speed: [2.2] + # # wave_heading: [0, 30., 60.] + # transient_time: 0. + # analysis_time: 720. + # ws_bin_size: 0.25 + # - DLC: "1.3" + # # current_speed: [2.2] + # transient_time: 0. + # analysis_time: 720. + # ws_bin_size: 0.25 + # - DLC: "6.1" + # transient_time: 0. + # analysis_time: 720. diff --git a/examples/19_DFSM/postprocessing.py b/examples/19_DFSM/postprocessing.py new file mode 100644 index 000000000..4a2a36c81 --- /dev/null +++ b/examples/19_DFSM/postprocessing.py @@ -0,0 +1,118 @@ +import os +import numpy as np +import openmdao.api as om +import pickle +import matplotlib.pyplot as plt +from matplotlib.animation import FuncAnimation + +def extract_results(driver_cases,obj,const = None): + + DV = [];Obj = [];Con = [] + + for idx, case in enumerate(driver_cases): + + dvs = case.get_design_vars(scaled=False) + + Obj.append(case.get_objectives(scaled = False)[obj][0]) + + if not(const == None): + Con.append(case.get_constraints(scaled = False)[const][0]) + + for key in dvs.keys(): + DV.append(dvs[key]) + + n_iter = idx+1 + n_var = len(dvs.keys()) + + Obj = np.array(Obj) + DV = np.array(DV) + + if not(const == None): + Con = np.array(Con) + + + return Obj,DV,Con,n_iter,n_var + + + + +this_dir = os.path.dirname(os.path.realpath(__file__)) # get path to this file + +fol = 'RM1_test' + + +doe_output_dir = this_dir + os.sep + 'outputs' + os.sep + fol +doe_sql_file = doe_output_dir + os.sep + 'log_opt.sql' + +obj = 'aeroelastic.DEL_TwrBsMyt' +con = None +n_samples = 10 + +# load sql file +cr_doe = om.CaseReader(doe_sql_file) +doe_driver_cases = cr_doe.get_cases('driver') + + +# load design variables +DEL,DV,Con,n_iter,n_var = extract_results(doe_driver_cases,obj,con) + + +DEL = np.reshape(DEL,[n_samples,n_samples]) +DV = np.reshape(DV,[n_iter,n_var],order = 'C') + +omega_pc = np.unique(DV[:,0]) +zeta_pc = np.unique(DV[:,1]) + +O,Z = np.meshgrid(omega_pc,zeta_pc) + + +fig,ax = plt.subplots(1) +#ig.suptitle(title) +CP = ax.contourf(O,Z,DEL,25,cmap = plt.cm.viridis) +ax.set_ylabel('Zeta PC') +ax.set_xlabel('Omega PC') +cbar = fig.colorbar(CP) +cbar.ax.set_ylabel('DEL') + +plot_name = 'DEL.png' + +fig.savefig(plot_name) + + + +# scats = [] + +# def animate(i): +# global scats + +# for scat in scats: +# scat.remove() +# scats = [] + +# if i >= n_iter_opt-1: +# scat = ax.scatter(DV_opt[-1,0],DV_opt[-1,1],c = 'tab:green',s = 40) + +# title = 'Iteration {:02d}'.format(n_iter_opt) + +# else: + +# scat = ax.scatter(DV_opt[i,0],DV_opt[i,1],c = 'r',s = 40) + +# title = 'Iteration {:02d}'.format(i+1) + + +# # set picture +# ax.set_title(title) + +# scats.append(scat) + +# return ax + +# # create animation and save +# savename = 'optimization3' + '.gif' +# anim = FuncAnimation(fig,animate,frames = n_iter_opt+200,interval=500,**{'repeat' : False}) +# anim.save(savename, dpi=120, writer="pillow") + + +plt.show() +# breakpoint() \ No newline at end of file diff --git a/examples/19_DFSM/weis_driver_dfsm_mhk.py b/examples/19_DFSM/weis_driver_dfsm_mhk.py new file mode 100644 index 000000000..736c0d8af --- /dev/null +++ b/examples/19_DFSM/weis_driver_dfsm_mhk.py @@ -0,0 +1,15 @@ +import os +from weis.glue_code.runWEIS import run_weis + + +if __name__ == "__main__": + + mydir = os.path.dirname(os.path.realpath(__file__)) # get path to this file + + # get path to modelling, geometry and analysis files + fname_modeling_options = mydir + os.sep + "modeling_options_dfsm_mhk.yaml" + fname_wt_input = mydir + os.sep + "RM1.yaml" + fname_analysis_options = mydir + os.sep + "analysis_options_dfsm_mhk.yaml" + + wt_opt, modeling_options, opt_options = run_weis(fname_wt_input, fname_modeling_options, fname_analysis_options) + diff --git a/weis/aeroelasticse/openmdao_openfast.py b/weis/aeroelasticse/openmdao_openfast.py index 070c6b5bc..8e920583c 100644 --- a/weis/aeroelasticse/openmdao_openfast.py +++ b/weis/aeroelasticse/openmdao_openfast.py @@ -10,7 +10,7 @@ from pathlib import Path from scipy.interpolate import PchipInterpolator from openmdao.api import ExplicitComponent -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI from wisdem.commonse import NFREQ from wisdem.commonse.cylinder_member import get_nfull import wisdem.commonse.utilities as util @@ -36,6 +36,7 @@ from weis.aeroelasticse.StC_defaults import default_StC_vt from weis.aeroelasticse.CaseGen_General import case_naming from wisdem.inputs import load_yaml +from weis.dfsm.dfsm_wrapper import dfsm_wrapper if MPI: from mpi4py import MPI @@ -373,7 +374,7 @@ def setup(self): if MPI: rank = MPI.COMM_WORLD.Get_rank() self.FAST_runDirectory = os.path.join(FAST_directory_base,'rank_%000d'%int(rank)) - self.FAST_namingOut = self.FAST_InputFile+'_%000d'%int(rank) + self.FAST_namingOut = self.FAST_InputFile#+'_%000d'%int(rank) else: self.FAST_runDirectory = FAST_directory_base self.FAST_namingOut = self.FAST_InputFile @@ -617,144 +618,158 @@ def compute(self, inputs, outputs, discrete_inputs, discrete_outputs): fst_vt['Fst']['TStart'] = 0. self.write_FAST(fst_vt, discrete_outputs) else: - # Write OF model and run - summary_stats, extreme_table, DELs, Damage, case_list, case_name, chan_time, dlc_generator = self.run_FAST(inputs, discrete_inputs, fst_vt) - # Set up linear turbine model - if modopt['Level2']['flag']: - try: - LinearTurbine = LinearTurbineModel( - self.FAST_runDirectory, - self.lin_case_name, - nlin=modopt['Level2']['linearization']['NLinTimes'], - reduceControls=True - ) - except FileNotFoundError as e: - logger.warning('FileNotFoundError: {} {}'.format(e.strerror, e.filename)) - return - - # Save linearizations - logger.warning('Saving ABCD matrices!') - ABCD = { - 'sim_idx' : self.sim_idx, - 'A' : LinearTurbine.A_ops, - 'B' : LinearTurbine.B_ops, - 'C' : LinearTurbine.C_ops, - 'D' : LinearTurbine.D_ops, - 'x_ops':LinearTurbine.x_ops, - 'u_ops':LinearTurbine.u_ops, - 'y_ops':LinearTurbine.y_ops, - 'u_h':LinearTurbine.u_h, - 'omega_rpm' : LinearTurbine.omega_rpm, - 'DescCntrlInpt' : LinearTurbine.DescCntrlInpt, - 'DescStates' : LinearTurbine.DescStates, - 'DescOutput' : LinearTurbine.DescOutput, - 'StateDerivOrder' : LinearTurbine.StateDerivOrder, - 'ind_fast_inps' : LinearTurbine.ind_fast_inps, - 'ind_fast_outs' : LinearTurbine.ind_fast_outs, - } - with open(self.lin_pkl_file_name, 'rb') as handle: - ABCD_list = pickle.load(handle) + if modopt['Level3']['flag'] or modopt['Level2']['flag']: + # Write OF model and run + summary_stats, extreme_table, DELs, Damage, case_list, case_name, chan_time, dlc_generator = self.run_FAST(inputs, discrete_inputs, fst_vt) + + # Set up linear turbine model + if modopt['Level2']['flag']: + try: + LinearTurbine = LinearTurbineModel( + self.FAST_runDirectory, + self.lin_case_name, + nlin=modopt['Level2']['linearization']['NLinTimes'], + reduceControls=True + ) + except FileNotFoundError as e: + logger.warning('FileNotFoundError: {} {}'.format(e.strerror, e.filename)) + return + + # Save linearizations + logger.warning('Saving ABCD matrices!') + ABCD = { + 'sim_idx' : self.sim_idx, + 'A' : LinearTurbine.A_ops, + 'B' : LinearTurbine.B_ops, + 'C' : LinearTurbine.C_ops, + 'D' : LinearTurbine.D_ops, + 'x_ops':LinearTurbine.x_ops, + 'u_ops':LinearTurbine.u_ops, + 'y_ops':LinearTurbine.y_ops, + 'u_h':LinearTurbine.u_h, + 'omega_rpm' : LinearTurbine.omega_rpm, + 'DescCntrlInpt' : LinearTurbine.DescCntrlInpt, + 'DescStates' : LinearTurbine.DescStates, + 'DescOutput' : LinearTurbine.DescOutput, + 'StateDerivOrder' : LinearTurbine.StateDerivOrder, + 'ind_fast_inps' : LinearTurbine.ind_fast_inps, + 'ind_fast_outs' : LinearTurbine.ind_fast_outs, + } + with open(self.lin_pkl_file_name, 'rb') as handle: + ABCD_list = pickle.load(handle) - ABCD_list[self.sim_idx] = ABCD + ABCD_list[self.sim_idx] = ABCD - with open(self.lin_pkl_file_name, 'wb') as handle: - pickle.dump(ABCD_list, handle) + with open(self.lin_pkl_file_name, 'wb') as handle: + pickle.dump(ABCD_list, handle) + + lin_files = glob.glob(os.path.join(self.FAST_runDirectory, '*.lin')) - lin_files = glob.glob(os.path.join(self.FAST_runDirectory, '*.lin')) + dest = os.path.join(self.FAST_runDirectory, f'copied_lin_files_{self.lin_idx}') + Path(dest).mkdir(parents=True, exist_ok=True) + for file in lin_files: + shutil.copy2(file, dest) + self.lin_idx += 1 + + # Shorten output names from linearization output to one like level3 openfast output + # This depends on how openfast sets up the linearization output names and may break if that is changed + OutList = [out_name.split()[1][:-1] for out_name in LinearTurbine.DescOutput] + OutOps = {} + for i_out, out in enumerate(OutList): + OutOps[out] = LinearTurbine.y_ops[i_out,:] + + # save to yaml, might want in analysis outputs + FileTools.save_yaml( + self.FAST_runDirectory, + 'OutOps.yaml',OutOps) + + # Set up Level 2 disturbance (simulation or DTQP) + if modopt['Level2']['simulation']['flag'] or modopt['Level2']['DTQP']['flag']: + # Extract disturbance(s) + level2_disturbance = [] + for case in case_list: + ts_file = TurbSimFile(case[('InflowWind','FileName_BTS')]) + ts_file.compute_rot_avg(fst_vt['ElastoDyn']['TipRad']) + u_h = ts_file['rot_avg'][0,:] + tt = ts_file['t'] + level2_disturbance.append({'Time':tt, 'Wind': u_h}) + + # Run linear simulation: + + # Get case list, wind inputs should have already been generated + if modopt['Level2']['simulation']['flag']: - dest = os.path.join(self.FAST_runDirectory, f'copied_lin_files_{self.lin_idx}') - Path(dest).mkdir(parents=True, exist_ok=True) - for file in lin_files: - shutil.copy2(file, dest) - self.lin_idx += 1 - - # Shorten output names from linearization output to one like level3 openfast output - # This depends on how openfast sets up the linearization output names and may break if that is changed - OutList = [out_name.split()[1][:-1] for out_name in LinearTurbine.DescOutput] - OutOps = {} - for i_out, out in enumerate(OutList): - OutOps[out] = LinearTurbine.y_ops[i_out,:] - - # save to yaml, might want in analysis outputs - FileTools.save_yaml( - self.FAST_runDirectory, - 'OutOps.yaml',OutOps) - - # Set up Level 2 disturbance (simulation or DTQP) - if modopt['Level2']['simulation']['flag'] or modopt['Level2']['DTQP']['flag']: - # Extract disturbance(s) - level2_disturbance = [] - for case in case_list: - ts_file = TurbSimFile(case[('InflowWind','FileName_BTS')]) - ts_file.compute_rot_avg(fst_vt['ElastoDyn']['TipRad']) - u_h = ts_file['rot_avg'][0,:] - tt = ts_file['t'] - level2_disturbance.append({'Time':tt, 'Wind': u_h}) - - # Run linear simulation: - - # Get case list, wind inputs should have already been generated - if modopt['Level2']['simulation']['flag']: - - if modopt['Level2']['DTQP']['flag']: - raise Exception('Only DTQP or simulation flag can be set to true in Level2 modeling options') - - # This is going to use the last discon_in file of the linearization set as the simulation file - # Currently fine because openfast is executed (or not executed if overwrite=False) after the file writing - if 'DLL_InFile' in self.fst_vt['ServoDyn']: # if using file inputs - discon_in_file = os.path.join(self.FAST_runDirectory, self.fst_vt['ServoDyn']['DLL_InFile']) - else: # if using fst_vt inputs from openfast_openmdao - discon_in_file = os.path.join(self.FAST_runDirectory, self.lin_case_name[0] + '_DISCON.IN') - - lib_name = modopt['General']['openfast_configuration']['path2dll'] - - ss = {} - et = {} - dl = {} - dam = {} - ct = [] - for i_dist, dist in enumerate(level2_disturbance): - sim_name = 'l2_sim_{}'.format(i_dist) - controller_int = ROSCO_ci.ControllerInterface( - lib_name, - param_filename=discon_in_file, - DT=1/80, # modelling input? - sim_name = os.path.join(self.FAST_runDirectory,sim_name) - ) - - l2_out, _, P_op = LinearTurbine.solve(dist,Plot=False,controller=controller_int) - - output = OpenFASTOutput.from_dict(l2_out, sim_name, magnitude_channels=self.magnitude_channels) - - _name, _ss, _et, _dl, _dam = self.la._process_output(output) - ss[_name] = _ss - et[_name] = _et - dl[_name] = _dl - dam[_name] = _dam - ct.append(l2_out) - - output.df.to_pickle(os.path.join(self.FAST_runDirectory,sim_name+'.p')) - - summary_stats, extreme_table, DELs, Damage = self.la.post_process(ss, et, dl, dam) - - # Overwrite timeseries with simulated data instead of saved linearization timeseries - chan_time = ct - - elif modopt['Level2']['DTQP']['flag']: - - summary_stats, extreme_table, DELs, Damage = dtqp_wrapper( - LinearTurbine, - level2_disturbance, - self.options['opt_options'], - self.options['modeling_options'], - self.fst_vt, - self.la, - self.magnitude_channels, - self.FAST_runDirectory - ) - - # TODO: pull chan_time out of here + if modopt['Level2']['DTQP']['flag']: + raise Exception('Only DTQP or simulation flag can be set to true in Level2 modeling options') + + # This is going to use the last discon_in file of the linearization set as the simulation file + # Currently fine because openfast is executed (or not executed if overwrite=False) after the file writing + if 'DLL_InFile' in self.fst_vt['ServoDyn']: # if using file inputs + discon_in_file = os.path.join(self.FAST_runDirectory, self.fst_vt['ServoDyn']['DLL_InFile']) + else: # if using fst_vt inputs from openfast_openmdao + discon_in_file = os.path.join(self.FAST_runDirectory, self.lin_case_name[0] + '_DISCON.IN') + + lib_name = modopt['General']['openfast_configuration']['path2dll'] + + ss = {} + et = {} + dl = {} + dam = {} + ct = [] + for i_dist, dist in enumerate(level2_disturbance): + sim_name = 'l2_sim_{}'.format(i_dist) + controller_int = ROSCO_ci.ControllerInterface( + lib_name, + param_filename=discon_in_file, + DT=1/80, # modelling input? + sim_name = os.path.join(self.FAST_runDirectory,sim_name) + ) + + l2_out, _, P_op = LinearTurbine.solve(dist,Plot=False,controller=controller_int) + + output = OpenFASTOutput.from_dict(l2_out, sim_name, magnitude_channels=self.magnitude_channels) + + _name, _ss, _et, _dl, _dam = self.la._process_output(output) + ss[_name] = _ss + et[_name] = _et + dl[_name] = _dl + dam[_name] = _dam + ct.append(l2_out) + + output.df.to_pickle(os.path.join(self.FAST_runDirectory,sim_name+'.p')) + + summary_stats, extreme_table, DELs, Damage = self.la.post_process(ss, et, dl, dam) + + # Overwrite timeseries with simulated data instead of saved linearization timeseries + chan_time = ct + + elif modopt['Level2']['DTQP']['flag']: + + summary_stats, extreme_table, DELs, Damage = dtqp_wrapper( + LinearTurbine, + level2_disturbance, + self.options['opt_options'], + self.options['modeling_options'], + self.fst_vt, + self.la, + self.magnitude_channels, + self.FAST_runDirectory + ) + + # TODO: pull chan_time out of here + + elif (modopt['DFSM']['flag']): + + mpi_options = {} + mpi_options['mpi_run'] = modopt['General']['openfast_configuration']['mpi_run'] + if mpi_options['mpi_run']: + mpi_options['mpi_comm_map_down'] = modopt['General']['openfast_configuration']['mpi_comm_map_down'] + + # Call DFSM wrapper + summary_stats, extreme_table, DELs, Damage,case_list,case_name, chan_time,dlc_generator,TMax,TStart = dfsm_wrapper(fst_vt, modopt, inputs, discrete_inputs,self.FAST_runDirectory,self.FAST_namingOut,mpi_options) + self.fst_vt = fst_vt + self.TMax = TMax;self.TStart = TStart # Post process regardless of level self.post_process(summary_stats, extreme_table, DELs, Damage, case_list, dlc_generator, chan_time, inputs, discrete_inputs, outputs, discrete_outputs) diff --git a/weis/control/dac.py b/weis/control/dac.py index 629bd9eab..33f9d8a5b 100644 --- a/weis/control/dac.py +++ b/weis/control/dac.py @@ -10,7 +10,7 @@ import multiprocessing as mp from functools import partial -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI def runXfoil(xfoil_path, x, y, Re, AoA_min=-9, AoA_max=25, AoA_inc=0.5, Ma=0.0, multi_run=False, MPI_run=False): #This function is used to create and run xfoil simulations for a given set of airfoil coordinates diff --git a/weis/control/tune_rosco.py b/weis/control/tune_rosco.py index b4232eb00..380de3c1c 100644 --- a/weis/control/tune_rosco.py +++ b/weis/control/tune_rosco.py @@ -379,7 +379,7 @@ def compute(self,inputs,outputs, discrete_inputs, discrete_outputs): ROSCO_input['Ct'] = WISDEM_turbine.Ct ROSCO_input['Cq'] = WISDEM_turbine.Cq - if (self.modeling_options['Level2']['flag'] or self.modeling_options['Level3']['flag']): + if (self.modeling_options['Level2']['flag'] or self.modeling_options['DFSM']['flag'] or self.modeling_options['Level3']['flag']): self.modeling_options['General']['openfast_configuration']['fst_vt']['DISCON_in'] = ROSCO_input # Outputs diff --git a/weis/dfsm/__init__.py b/weis/dfsm/__init__.py new file mode 100644 index 000000000..b588f3a97 --- /dev/null +++ b/weis/dfsm/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: utf-8 -*- +"""Top-level package for DFSM Repo.""" + +__author__ = """Athul K. Sundarrajan and Daniel R. Herber""" +__email__ = 'Athul.Sundarrajan@colostate.edu' +__version__ = '1.0.0' diff --git a/weis/dfsm/construct_dfsm.py b/weis/dfsm/construct_dfsm.py new file mode 100755 index 000000000..cf7e75501 --- /dev/null +++ b/weis/dfsm/construct_dfsm.py @@ -0,0 +1,441 @@ +import os +import numpy as np + +from scipy.interpolate import CubicSpline,interp1d +import matplotlib.pyplot as plt + +from scipy.integrate import solve_ivp +from numpy.linalg import lstsq,qr,inv,norm +import pickle + +from sklearn.gaussian_process import GaussianProcessRegressor +from sklearn.gaussian_process.kernels import RBF as RBFsk +from sklearn.gaussian_process.kernels import ExpSineSquared +import time as timer +from sklearn.cluster import KMeans +from sklearn.preprocessing import StandardScaler + +from weis.dfsm.dfsm_sample_data import sample_data +from sklearn.neural_network import MLPRegressor +from mat4py import loadmat +from weis.dfsm.wrapper_LTI import wrapper_LTI +PYOPT = True +try: + from pyoptsparse import IPOPT, Optimization +except Exception: + PYOPT = False +import argparse + + +class DFSM: + + def __init__(self,SimulationDetails,L_type = 'LTI',N_type = None,n_samples = 300,sampling_method = 'KM',train_split = 0.8,A_array = None,B_array = None,C_array = None, D_array = None,W = None,interp_type = 'linear'): + + self.L_type = L_type + self.N_type = N_type + self.n_samples = n_samples + self.sampling_method = sampling_method + self.train_split = train_split + + self.A_array = A_array + self.B_array = B_array + self.C_array = C_array + self.D_array = D_array + self.W = W + self.interp_type = interp_type + + FAST_sim = SimulationDetails.FAST_sim + + self.n_model_inputs = SimulationDetails.n_model_inputs + self.n_deriv = SimulationDetails.n_deriv + self.n_outputs = SimulationDetails.n_outputs + self.gen_speed_ind = SimulationDetails.gen_speed_ind + self.FA_Acc_ind_s = SimulationDetails.FA_Acc_ind_s + self.NacIMU_FA_Acc_ind_s = SimulationDetails.NacIMU_FA_Acc_ind_s + self.FA_Acc_ind_o = SimulationDetails.FA_Acc_ind_o + self.NacIMU_FA_Acc_ind_o = SimulationDetails.NacIMU_FA_Acc_ind_o + self.wind_speed_ind = SimulationDetails.wind_speed_ind + self.gen_torque_ind = SimulationDetails.gen_torque_ind + self.blade_pitch_ind = SimulationDetails.blade_pitch_ind + self.wave_elev_ind = SimulationDetails.wave_elev_ind + self.linear_model_file = SimulationDetails.linear_model_file + self.region = SimulationDetails.region + + n_sim = SimulationDetails.n_sim + + train_index = int(np.floor(train_split*n_sim)) + self.train_data = FAST_sim[0:train_index] + + self.test_data = FAST_sim[train_index:] + + def setup_LPV(self,interp_type = None): + + if interp_type == None: + interp_type = self.interp_type + + A_array = self.A_array;B_array = self.B_array;C_array = self.C_array;D_array = self.D_array + W = self.W + + A0 = np.squeeze(A_array[0,:,:]);A1 = np.squeeze(A_array[-1,:,:]) + B0 = np.squeeze(B_array[0,:,:]);B1 = np.squeeze(B_array[-1,:,:]) + C0 = np.squeeze(C_array[0,:,:]);C1 = np.squeeze(C_array[-1,:,:]) + D0 = np.squeeze(D_array[0,:,:]);D1 = np.squeeze(D_array[-1,:,:]) + + self.A_fun = interp1d(W,A_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (A0,A1)) + self.B_fun = interp1d(W,B_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (B0,B1)) + self.C_fun = interp1d(W,C_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (C0,C1)) + self.D_fun = interp1d(W,D_array,kind = interp_type,axis = 0,bounds_error=False,fill_value = (D0,D1)) + + + + + + def construct_nonlinear(self,inputs,outputs,N_type,error_ind,n_inputs,n_outputs,ftype = 'deriv',scaling = True): + + sm = [] + + if N_type == 'GPR': + + + if scaling: + # scale inputs + scaler = StandardScaler().fit(inputs) + inputs_scaled = scaler.transform(inputs) + + self.scaler = scaler + + + else: + + inputs_scaled = inputs + self.scaler = None + + # store inputs + self.inputs_scaled = inputs_scaled + + + for ifunc in range(n_outputs): + + if error_ind[ifunc]: + + # train a gaussian process model + kernel = 1*RBFsk(length_scale = [1]*n_inputs,length_scale_bounds=(1e-5, 1e5)) + smi = GaussianProcessRegressor(kernel = kernel,n_restarts_optimizer = 5,n_targets = 1, optimizer = 'fmin_l_bfgs_b') + smi.fit(inputs_scaled,outputs[:,ifunc]) + + sm.append(smi) + + else: + sm.append(None) + + elif N_type == 'NN': + + + # train a neural network to predict the errors + sm = MLPRegressor(hidden_layer_sizes = (100,50,10),max_iter = 1000,activation = 'tanh',solver = 'adam',verbose = True,tol = 1e-4) + sm.fit(inputs,outputs[:,error_ind]) + + if ftype == 'deriv': + + self.nonlin_deriv = sm + + elif ftype == 'outputs': + + self.nonlin_outputs = sm + + + def construct_surrogate(self): + + # extract samples + t1 = timer.time() + inputs_sampled,dx_sampled,outputs_sampled,model_inputs,state_derivatives,outputs = sample_data(self.train_data,self.sampling_method,self.n_samples,grouping = 'together') + t2 = timer.time() + + self.sampling_time = t2 - t1 + + # depending on the type of L and N construct the surrogate model + if self.L_type == None: + + # set the AB and CD matrices as empty + self.AB = [] + self.CD = [] + self.lin_construct = 0 + + self.error_ind_deriv = np.full(self.n_deriv,True) + + if self.n_outputs > 0: + self.error_ind_outputs = np.full(self.n_outputs,True) + + t1 = timer.time() + self.construct_nonlinear(inputs_sampled,dx_sampled,self.N_type,self.error_ind_deriv,self.n_model_inputs,self.n_deriv,'deriv') + + if self.n_outputs > 0: + self.construct_nonlinear(inputs_sampled,outputs_sampled,self.N_type,self.error_ind_outputs,self.n_model_inputs,self.n_outputs,'outputs') + + t2 = timer.time() + + self.nonlin_construct_time = t2-t1 + self.linear_construct_time = 0 + self.inputs_sampled = inputs_sampled + self.dx_sampled = dx_sampled + + else: + + if self.L_type == 'LTI': + + if self.linear_model_file == None: + + n_states = self.n_deriv + n_controls = self.n_model_inputs - n_states + + # start timer + t1 = timer.time() + + # construct ta least squares approximation + AB = lstsq(model_inputs,state_derivatives,rcond = -1) + AB = AB[0] + + # extract the A matrix + AB_ = AB.T + + A = AB_[:,n_controls:] + Aeig = np.linalg.eig(A)[0] + Aeig_real = Aeig.real + + if all(Aeig_real < 0): + print('') + print('The least squares estimate is stable, using this as the linear model') + print('') + else: + print('') + print('The linear model identified using least-squares estimation is unstable, using grey-box estimation to identify a stable model') + print('') + + # extract the identified parameters + par0 = AB_[int(n_states/2):,:] + + # reshape + par0 = np.squeeze(par0.reshape([-1,1],order = 'F')) + + # get the length and specify upper and lower bounds + nx = len(par0) + lb = [-100]*nx + ub = [100]*nx + + # instantiate wrapper class + LTI = wrapper_LTI(inputs = model_inputs,outputs = state_derivatives,nstates = int(n_states),ncontrols = int(n_controls)) + + # # set default options + parser = argparse.ArgumentParser() + parser.add_argument("--opt",help = "optimizer",type = str) + args = parser.parse_args() + + # set specific solver options + optOptions_ipopt = {'max_iter':500,'tol':1e-8,'print_level':1, + 'file_print_level':5,'dual_inf_tol':float(1e-8),'linear_solver':'mumps'} + + # options for nsga + optOptions_nsga = {'maxGen':200,'seed':34534,'PopSize':200} + + # options for SLSQP + optOptions_slsqp = {'ACC':float(1e-6),'MAXIT':500,'IPRINT':1} + + # optimization problem + prob = wrapper_LTI(inputs = model_inputs,outputs = state_derivatives, + nstates = n_states,ncontrols = n_controls) + + # add relevant fields + optProb = Optimization('LTI',prob.objective_function) + optProb.addObj('obj') + optProb.addConGroup('con',n_states,lower = [None]*n_states,upper = -0*np.ones((n_states,))) + optProb.addVarGroup("xvars", nx, lower=lb, upper=ub, value=par0) + + hybrid_flag = True + + if hybrid_flag: + print('') + print('Using a hybrid-optimization approach to estimate the linear model parameters') + print('Using the NSGA2 algorithm to get a good starting point') + print('') + # solve the problem using NSGA2 algorithm + opt = NSGA2(options = optOptions_nsga) + sol = opt(optProb) + + # extract the solution + X_nsga_dict = sol.xStar + X_nsga = X_nsga_dict['xvars'] + F_ga = sol.fStar + self.X_nsga = X_nsga + self.X_nsga_dict = X_nsga_dict + else: + X_nsga = par0 + self.X_nsga = X_nsga + self.X_nsga_dict = {'xvars':X_nsga} + + + grad_flag = True + solver = 'SLSQP' + # Hack for pyoptsparse segfault + + if grad_flag: + + print('') + print('Using a gradient-based algorithm to estimate the model parameters') + print('') + + # add problem elements for IPOPT + optProb2 = Optimization('LTI',prob.objective_function) + optProb2.addObj('obj') + optProb2.addConGroup('con',n_states,lower = [None]*n_states,upper = -0*np.ones((n_states,))) + optProb2.addVarGroup("xvars", nx, lower=lb, upper=ub, value=X_nsga) + + if solver == 'SLSQP': + opt_ipopt = SLSQP(options = optOptions_slsqp) + sol_ipopt = opt_ipopt(optProb2,sens = 'FD') + + elif solver == 'IPOPT': + # solve + opt_ipopt = IPOPT(args,options = optOptions_ipopt) + sol_ipopt = opt_ipopt(optProb2,sens = 'FD') + + + # extract ipopt solution + F_ipopt = sol_ipopt.fStar + + X_ipopt_dict = sol_ipopt.xStar + X_ipopt = X_ipopt_dict['xvars'] + self.X_ipopt = X_ipopt + self.X_ipopt_dict = X_ipopt_dict + + else: + X_ipopt = X_nsga + self.X_ipopt = X_ipopt + self.X_ipopt_dict = {'xvars':X_ipopt} + + + # linear model + A,B = LTI.linear_model(X_ipopt) + func_ip,sens = LTI.objective_function(self.X_ipopt_dict) + + AB = np.hstack([B,A]).T + + else: + print('') + print('Loading linear model from mat file') + print('') + + + AB_dict = loadmat(self.linear_model_file) + + if self.region == 'BR': + AB = AB_dict['AB_br'] + + elif self.region == 'TR': + AB = AB_dict['AB_tr'] + + elif self.region == 'R': + AB = AB_dict['AB_r'] + + AB = np.array(AB) + + self.AB = AB + + if (self.n_outputs > 0): + CD = lstsq(model_inputs,outputs,rcond = -1) + + self.CD = CD[0] + + else: + + self.CD = [] + + # end timer + t2 = timer.time() + + # training time + self.linear_construct_time = t2-t1 + self.inputs_sampled = inputs_sampled + self.dx_sampled = dx_sampled + self.model_inputs = model_inputs + self.state_derivatives = state_derivatives + + + # evaluate the error between the linear model and actual derivatives + dx_error = dx_sampled - np.dot(inputs_sampled,self.AB) + + # evaluate mean + error_mean = np.mean(dx_error,0) + + # find indices with error > 1e-5 + error_ind_deriv = np.array((np.abs(error_mean) > 1e-5)) + + # store indices and error + self.error_ind_deriv = error_ind_deriv + self.dx_error = dx_error + + + if self.n_outputs > 0: + + # error between outputs + outputs_error = outputs_sampled - np.dot(inputs_sampled,self.CD) + + # store indices + self.error_ind_outputs = (np.abs(np.mean(outputs_error,0)) > 1e-5) + + elif self.L_type == 'LPV': + + AB_dict = loadmat(self.linear_model_file) + + A_list = AB_dict['A_cell'] + B_list = AB_dict['B_cell'] + C_list = AB_dict['C_cell'] + D_list = AB_dict['D_cell'] + + A = np.squeeze(np.array(A_list)) + B = np.squeeze(np.array(B_list)) + C = np.squeeze(np.array(C_list)) + D = np.squeeze(np.array(D_list)) + + W = np.squeeze(np.array(AB_dict['W'])) + + A_fun = interp1d(W,A,kind = 'nearest',axis = 0,fill_value = 'extrapolate') + B_fun = interp1d(W,B,kind = 'nearest',axis = 0,fill_value = 'extrapolate') + C_fun = interp1d(W,C,kind = 'nearest',axis = 0,fill_value = 'extrapolate') + D_fun = interp1d(W,D,kind = 'nearest',axis = 0,fill_value = 'extrapolate') + + self.A_fun = A_fun + self.B_fun = B_fun + self.C_fun = C_fun + self.D_fun = D_fun + + + self.AB = [] + self.CD = [] + self.error_ind_deriv = [] + self.error_ind_outputs = [] + #breakpoint() + + if self.N_type == None: + + # if no nonlinear corrective function is specified, do nothing + self.nonlin_deriv = None + self.nonlin_outputs = None + self.nonlin_construct_time = 0 + + else: + print('') + print('Constructing nonlinear corrective function') + print('') + + t1 = timer.time() + + self.construct_nonlinear(inputs_sampled,dx_error,self.N_type,self.error_ind_deriv,self.n_model_inputs,self.n_deriv,'deriv') + + if self.n_outputs > 0: + self.construct_nonlinear(inputs_sampled,outputs_error,self.N_type,self.error_ind_outputs,self.n_model_inputs,self.n_outputs,'outputs') + + t2 = timer.time() + + self.nonlin_construct_time = t2-t1 + + diff --git a/weis/dfsm/dfsm_plotting_scripts.py b/weis/dfsm/dfsm_plotting_scripts.py new file mode 100755 index 000000000..0b976971a --- /dev/null +++ b/weis/dfsm/dfsm_plotting_scripts.py @@ -0,0 +1,247 @@ +import os +import numpy as np +import matplotlib.pyplot as plt +import pickle +from pCrunch.io import load_FAST_out +from rosco.toolbox.ofTools.fast_io import output_processing +from rosco.toolbox.ofTools.util import spectral + + +def plot_signal(signal_dict,save_flag,save_path): + + time = signal_dict['time'] + signals_act = signal_dict['OpenFAST'] + signals_dfsm = signal_dict['DFSM'] + n_signals = signal_dict['n'] + signal_names = signal_dict['names'] + units = signal_dict['units'] + + if 'key_freq_name' in signal_dict: + key_freq_name = signal_dict['key_freq_name'] + key_freq_val = signal_dict['key_freq_val'] + + dx_flag = [not(name[0] == 'd') for name in signal_names] + + t0 = time[0];tf = time[-1] + + wd = os. getcwd() + + plot_path = wd + os.sep + save_path + + + for idx,qty in enumerate(signal_names): + + if not(qty[0] == 'd'): + + # plot controls + fig,ax = plt.subplots(2,1) + if qty == 'RtVAvgxh' or qty == 'Wind1VelX': + qty = 'Current Speed' + fig.suptitle(qty + ' '+units[idx]) + fig.subplots_adjust(hspace = 0.4) + + ax[0].set_xlabel('Time [s]') + ax[0].plot(time,signals_act[:,idx],label = 'OpenFAST') + ax[0].plot(time,signals_dfsm[:,idx],label = 'DFSM') + ax[0].legend(ncol = 2,bbox_to_anchor = (0.7,1.26)) + ax[0].set_xlim([t0,tf]) + + xf,FFT_act,_ = spectral.fft_wrap(time,signals_act[:,idx],averaging = 'Welch',averaging_window= 'hamming') + xf,FFT_dfsm,_ = spectral.fft_wrap(time,signals_dfsm[:,idx],averaging = 'Welch',averaging_window= 'hamming') + + col_list = ['k','tab:red'] + + if 'key_freq_name' in signal_dict: + for i,val in enumerate(key_freq_val[idx]): + ax[1].axvline(val,color = col_list[i],label = key_freq_name[idx][i],linestyle = ':') + ax[1].legend() + + ax[1].loglog(xf,np.sqrt(FFT_act)) + ax[1].loglog(xf,np.sqrt(FFT_dfsm)) + + ax[1].set_xlabel('Freq [Hz]') + ax[1].set_xlim([np.min(xf),np.max(xf)]) + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ qty + '_comp.svg') + + + +def plot_dfsm_results(U_list,X_list,dx_list,Y_list,simulation_flag,outputs_flag,control_flag = False,save_flag = False,save_path = 'plots'): + + n_results = len(U_list) + + for ix in range(n_results): + + # plot controls + if control_flag: + u_dict = U_list[ix] + plot_signal(u_dict,save_flag,save_path) + + + if simulation_flag: + x_dict = X_list[ix] + plot_signal(x_dict,save_flag,save_path) + + if outputs_flag: + y_dict = Y_list[ix] + plot_signal(y_dict,save_flag,save_path) + + + + +def plot_inputs(SimulationDetails,index,plot_type,save_flag = False,save_path = 'plots'): + + # extract + sim_details = SimulationDetails.FAST_sim[index] + wd = os. getcwd() + + time = sim_details['time'] + controls = sim_details['controls'] + states = sim_details['states'] + outputs = sim_details['outputs'] + + control_names = sim_details['control_names'] + + for iu,qty in enumerate(control_names): + if (qty == 'RtVAvgxh') or (qty == 'Wind1VelX'): + control_names[iu] = 'Current Speed' + + state_names = sim_details['state_names'] + output_names = sim_details['output_names'] + + n_controls = sim_details['n_controls'] + n_outputs = sim_details['n_outputs'] + n_states = sim_details['n_states'] + + + t0 = time[0]; tf = time[-1] + print(state_names) + state_flag = [not(name[0] == 'd') for name in state_names] + n_states_ = sum(state_flag) + states_ = states[:,state_flag] + state_names_ = [] + + for idx,flag in enumerate(state_flag): + if flag: + state_names_.append(state_names[idx]) + + # depending on plot type, plot the time series quantities + if plot_type == 'vertical': + + if not(len(outputs) > 0): + + # combine all signals into a single array + quantities = np.hstack([controls,states_]) + + # get the names + quantity_names = control_names + state_names_ + + + else: + + # combine all signals into a single array + quantities = np.hstack([controls,states_,outputs]) + + # names of the quantities + quantity_names = control_names + state_names_ + output_names + + n_qty = len(quantity_names) + + # intialize plot + fig,ax = plt.subplots(n_qty,1) + + ax[-1].set_xlabel('Time [s]') + + fig.subplots_adjust(hspace = 1) + + for idx,qty in enumerate(quantity_names): + + if not(qty[0] == 'd'): + ax[idx].plot(time,quantities[:,idx]) + ax[idx].set_title(qty) + ax[idx].set_xlim([t0,tf]) + + if not(idx == n_qty-1): + ax[idx].tick_params( + axis='x', # changes apply to the x-axis + which='both', # both major and minor ticks are affected + bottom=False, # ticks along the bottom edge are off + top=False, # ticks along the top edge are off + labelbottom=False) # labels along the bottom edge are off + + + + plot_path = wd + os.sep + save_path + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ 'inputs.svg') + + elif plot_type == 'separate': + + # plot controls + fig,axc = plt.subplots(n_controls,1) + if n_controls == 1: + axc = [axc] + axc[-1].set_xlabel('Time [s]') + fig.subplots_adjust(hspace = 0.65) + + for idx,qty in enumerate(control_names): + + axc[idx].plot(time,controls[:,idx]) + axc[idx].set_title(qty) + axc[idx].set_xlim([t0,tf]) + + plot_path = wd + os.sep + save_path + + if save_flag: + if not os.path.exists(plot_path): + os.makedirs(plot_path) + + fig.savefig(plot_path +os.sep+ 'inputs.svg') + + # plot states + fig,axs = plt.subplots(n_states,1) + if n_states == 1: + axs = [axs] + fig.subplots_adjust(hspace = 1) + axc[-1].set_xlabel('Time [s]') + + for idx,qty in enumerate(state_names): + + axs[idx].plot(time,states[:,idx]) + axs[idx].set_title(qty) + axs[idx].set_xlim([t0,tf]) + + # plot outputs + if n_outputs > 0: + + fig,axo = plt.subplots(n_outputs,1) + axc[-1].set_xlabel('Time [s]') + fig.subplots_adjust(hspace = 0.65) + + for idx,qty in enumerate(output_names): + + axo[idx].plot(time,outputs[:,idx]) + axo[idx].set_title(qty) + axo[idx].set_xlim([t0,tf]) + + + + + + + + + + + + + + \ No newline at end of file diff --git a/weis/dfsm/dfsm_rosco_simulation.py b/weis/dfsm/dfsm_rosco_simulation.py new file mode 100644 index 000000000..3dc70cf20 --- /dev/null +++ b/weis/dfsm/dfsm_rosco_simulation.py @@ -0,0 +1,74 @@ +import numpy as np +from weis.dfsm.evaluate_dfsm import evaluate_dfsm + +def run_sim_ROSCO(t,x,DFSM,param): + + turbine_state = {} + dt = t - param['time'][-1] + param['dt'].append(dt) + param['time'].append(t) + + if dt == 0: + dt = 1e-4 + + # extract data from param dict + w = param['w_fun'](t) + rpm2RadSec = 2.0*(np.pi)/60.0 + gen_speed_scaling = param['gen_speed_scaling'] + + + + # populate turbine state dictionary + if t == param['tf']: + turbine_state['iStatus'] = -1 + else: + turbine_state['iStatus'] = 1 + + + # else: + turbine_state['bld_pitch'] = np.deg2rad(param['blade_pitch'][-1]) + turbine_state['gen_torque'] = param['gen_torque'][-1]*1000 + + + turbine_state['t'] = t + turbine_state['dt'] = np.abs(dt) + turbine_state['ws'] = w + turbine_state['num_blades'] = int(2) + turbine_state['gen_speed'] = x[DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling + turbine_state['gen_eff'] = param['VS_GenEff']/100 + turbine_state['rot_speed'] = x[DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling/param['WE_GearboxRatio'] + turbine_state['Yaw_fromNorth'] = 0 + turbine_state['Y_MeasErr'] = 0 + + if not(DFSM.FA_Acc_ind == None): + turbine_state['FA_Acc'] = x[DFSM.FA_Acc_ind] + + if not(DFSM.NacIMU_FA_Acc_ind == None): + turbine_state['NacIMU_FA_Acc'] = x[DFSM.NacIMU_FA_Acc_ind] + + # call ROSCO to get control values + gen_torque, bld_pitch, nac_yawrate = param['controller_interface'].call_controller(turbine_state) + + # convert to right units + gen_torque = gen_torque/1000 + bld_pitch = np.rad2deg(bld_pitch) + + if param['wave_fun'] == None: + u = np.array([w,gen_torque,bld_pitch]) + + else: + wv = param['wave_fun'](t) + u = np.array([w,gen_torque,bld_pitch,wv]) + + # update param list + param['gen_torque'].append(gen_torque) + param['blade_pitch'].append(bld_pitch) + #param['time'].append(t) + + # combine + inputs = np.hstack([u,x]) + + # evaluate dfsm + dx = evaluate_dfsm(DFSM,inputs,'deriv') + + return dx \ No newline at end of file diff --git a/weis/dfsm/dfsm_sample_data.py b/weis/dfsm/dfsm_sample_data.py new file mode 100755 index 000000000..e60db3e4e --- /dev/null +++ b/weis/dfsm/dfsm_sample_data.py @@ -0,0 +1,134 @@ +import numpy as np +from sklearn.cluster import KMeans + +def dict2array(data): + + + # initialize storage array + input_list = [] + state_dx_list = [] + output_list = [] + + # loop through and extract data from each dictionary + for isim in data: + + # extract + controls = isim['controls'] + states = isim['states'] + state_derivatives = isim['state_derivatives'] + outputs = isim['outputs'] + + # number of outputs + n_outputs = isim['n_outputs'] + + # stack controls and states + model_inputs = np.hstack([controls,states]) + + # add to list + input_list.append(model_inputs) + state_dx_list.append(state_derivatives) + + # if there are outputs, then append + if n_outputs > 0: + output_list.append(outputs) + + # vertically stack input and outputs + model_inputs = np.vstack(input_list) + state_derivatives = np.vstack(state_dx_list) + + if n_outputs > 0: + outputs = np.vstack(output_list) + else: + outputs = [] + + + return model_inputs,state_derivatives,outputs + +def sample_data(data,sampling_type,n_samples,grouping = 'together'): + + # extract data from dict + model_inputs,state_derivatives,outputs = dict2array(data) + + # get the number of state derivatives and outputs + n_deriv = data[0]['n_deriv'] + n_outputs = data[0]['n_outputs'] + n_model_inputs = data[0]['n_model_inputs'] + + + if sampling_type == 'KM': + + if grouping == 'separate': + + # initialize storage array + dx_sampled = np.zeros((n_samples,n_deriv)) + + # if there are outputs, then initialize + if n_outputs > 0: + outputs_sampled = np.zeros((n_samples,n_outputs)) + else: + outputs_sampled = [] + + # perform kmeans clustering algorithm + kmeans = KMeans(n_clusters = n_samples,n_init = 10).fit(X = model_inputs) + + # extract centroid centers and centroid index + inputs_sampled = kmeans.cluster_centers_ + labels_ = kmeans.labels_ + + + # loop through and find the state derrivative values at the cluster centroids + for icluster in range(n_samples): + + # index of elements of the original array present in the current cluster + cluster_ind = (labels_ == icluster) + + # extract the sate derivative values at these indices + dx_ind = state_derivatives[cluster_ind,:] + + # evaluate the state derivative values at the centroid of this cluster + dx_sampled[icluster,:] = np.mean(dx_ind,axis=0) + + # if there are outputs, then extract + if n_outputs > 0: + outputs_ind = outputs[cluster_ind,:] + + outputs_sampled[icluster,:] = np.mean(outputs_ind, axis = 0) + + elif grouping == 'together': + + + if n_outputs > 0: + model_inputs_ = np.hstack([model_inputs,state_derivatives,outputs]) + else: + model_inputs_ = np.hstack([model_inputs,state_derivatives]) + + # scale the inputs/outputs + inputs_max = np.max(abs(model_inputs_),0) + model_inputs_ = model_inputs_/inputs_max + + + # perform kmeans clustering algorithm + kmeans = KMeans(n_clusters = n_samples,n_init = 10).fit(X = model_inputs_) + + # extract centroid centers and centroid index + inputs_sampled_ = kmeans.cluster_centers_ + + inputs_sampled_ = inputs_sampled_*inputs_max + + inputs_sampled = inputs_sampled_[:,0:n_model_inputs] + dx_sampled = inputs_sampled_[:,n_model_inputs:n_model_inputs+n_deriv] + + if n_outputs >0: + outputs_sampled = inputs_sampled_[:,n_model_inputs+n_deriv:] + else: + outputs_sampled = [] + + + return inputs_sampled,dx_sampled,outputs_sampled,model_inputs,state_derivatives,outputs + + + + + + + diff --git a/weis/dfsm/dfsm_utilities.py b/weis/dfsm/dfsm_utilities.py new file mode 100755 index 000000000..dac78d6e2 --- /dev/null +++ b/weis/dfsm/dfsm_utilities.py @@ -0,0 +1,161 @@ +""" +Script containing utility functions required in the DFSM modules + +""" +import numpy as np +from numpy.linalg import norm +import fnmatch + + +def valid_extension(fp): + return any([fnmatch.fnmatch(fp,ext) for ext in ['*.outb']]) + +def valid_extension_DISCON(fp): + return any([fnmatch.fnmatch(fp,ext) for ext in ['*.IN']]) + +def valid_extension_pickle(fp): + return any([fnmatch.fnmatch(fp,ext) for ext in ['*.p']]) + + +def calculate_MSE(x1,x2): + + ''' + Function to calculate the mean square error between two signals + ''' + + # number of samples + nt = len(x1) + + # MSE + MSE = np.sum((x1-x2)**2)/nt + + return MSE + +def calculate_time(DFSM): + + sampling_time = DFSM.sampling_time/60 + construction_time = (DFSM.linear_construct_time + DFSM.nonlin_construct_time)/60 + simulation_time = DFSM.simulation_time/60 + + return {'sampling_time':sampling_time,'construction_time':construction_time,'simulation_time':simulation_time} + +def calculate_SNE(x1,x2): + + ''' + Function to calculate the sum of normed errors (SNE) as defined in https://doi.org/10.1115/1.4037407 + ''' + + # number of samples + nt = len(x1) + + # initialize + SNE = np.zeros((nt,)) + + #SNE + for i in range(nt): + SNE[i] = norm(x1[i,:]-x2[i,:]) + + # sum + SNE = np.sum(SNE) + + return SNE + +def compile_dfsm_results(time,states_dfsm,controls_dfsm,outputs_dfsm,state_names,control_names,output_names, GB_ratio,tmin = 0): + + # initialize + OutData = {} + + time_ind = (time >= tmin) + + # add time + OutData['Time'] = time[time_ind] + + nt = np.sum(time_ind) + gs_ind = state_names.index('GenSpeed') + + # add states + for i,name_ in enumerate(state_names): + + if name_[0] == 'd': + pass + else: + OutData[name_] = states_dfsm[time_ind,i] + + # add controls + for i,name_ in enumerate(control_names): + + OutData[name_] = controls_dfsm[time_ind,i] + + # add controls + if len(output_names) >= 1: + for i,name_ in enumerate(output_names): + + OutData[name_] = outputs_dfsm[time_ind,i] + + if not('PtfmPitch' in state_names): + OutData['PtfmPitch'] = np.zeros((nt,)) + + if not('PtfmSurge' in state_names): + OutData['PtfmSurge'] = np.zeros((nt,)) + + if not('PtfmSway' in state_names): + OutData['PtfmSway'] = np.zeros((nt,)) + + if not('RotSpeed' in output_names): + OutData['RotSpeed'] = states_dfsm[time_ind,gs_ind]/GB_ratio + + for i_blade in range(2): + OutData[f'dBldPitch{i_blade+1}'] = np.zeros((nt,)) + + return OutData + + + +def extrapolate_controls(t_out,u, t): + + # extract time + t1 = t[0] + + # scale time + t -= t1 + t_out = t_out - t1 + + if len(t) == 2: + # evaluate slope + u1 = u[0]; u2 = u[1] + b0 = (u2 - u1)/t[1] + + # extrapolate + u_out = u1 + b0*t_out + + elif len(t) == 3: + + u1 = u[0];u2 = u[1]; u3 = u[2] + + b0 = (t[2]**2*(u1 - u2) + t[1]**2*(-u1 + u3))/(t[1]*t[2]*(t[1] - t[2])) + c0 = ( (t[1]-t[2])*u1+ t[2]*u2 - t[1]*u3 ) / (t[1]*t[2]*(t[1] - t[2])) + + u_out = u1 + b0*t_out + c0*t_out**2 + + + return u_out + + +def reorganize_data(FAST_sim): + + n_sim = len(FAST_sim) + + wind_mean = np.zeros((n_sim,)) + + for i in range(n_sim): + wind_mean[i] = FAST_sim[i]['w_mean'] + + w_unique = np.unique(wind_mean) + + n_unique = len(w_unique) + + FAST_sim = np.array(FAST_sim,dtype = object) + + FAST_sim = np.reshape(FAST_sim,[int(n_sim/n_unique),n_unique],order = 'F') + + return FAST_sim,w_unique,n_unique diff --git a/weis/dfsm/dfsm_wrapper.py b/weis/dfsm/dfsm_wrapper.py new file mode 100644 index 000000000..600d660a9 --- /dev/null +++ b/weis/dfsm/dfsm_wrapper.py @@ -0,0 +1,814 @@ +import os +import numpy as np +import matplotlib.pyplot as plt +import warnings +import time as timer +from mat4py import loadmat +import pickle +import shutil + +from rosco.toolbox import control_interface as ROSCO_ci +from weis.aeroelasticse.CaseGen_General import case_naming +from weis.aeroelasticse.FAST_writer import InputWriter_OpenFAST +from pCrunch.io import OpenFASTOutput +from pCrunch import LoadsAnalysis, PowerProduction, FatigueParams +from weis.dlc_driver.dlc_generator import DLCGenerator +from weis.aeroelasticse.turbsim_file import TurbSimFile +from weis.aeroelasticse.turbsim_util import generate_wind_files +from weis.aeroelasticse.CaseGen_General import CaseGen_General + +from weis.dfsm.simulation_details import SimulationDetails +from weis.dfsm.dfsm_utilities import valid_extension,calculate_time,valid_extension_DISCON,compile_dfsm_results +from weis.dfsm.construct_dfsm import DFSM +from weis.dfsm.ode_algorithms import RK4,ABM4 +from weis.dfsm.generate_wave_elev import generate_wave_elev + +from scipy.interpolate import CubicSpline, interp1d + +def evaluate_multi(case_data): + + # initialize controller interface + case_data['param']['controller_interface'] = ROSCO_ci.ControllerInterface(case_data['param']['lib_name'],param_filename=case_data['param']['param_filename'],**case_data['param']['args']) + + # run simulation + if case_data['ode_method'] == 'RK4': + + output = RK4(case_data['x0'],case_data['dt'],case_data['tspan'],case_data['dfsm'],case_data['param']) + + elif case_data['ode_method'] == 'ABM4': + + output = ABM4(case_data['x0'],case_data['dt'],case_data['tspan'],case_data['dfsm'],case_data['param']) + + + # shut down controller + case_data['param']['controller_interface'].kill_discon() + + + return output + + +def run_serial(case_data_all): + + # initialize storage array for results + outputs = [] + + # loop through and evaluate simulations + for case_data in case_data_all: + + # initialize controller interface + case_data['param']['controller_interface'] = ROSCO_ci.ControllerInterface(case_data['param']['lib_name'],param_filename=case_data['param']['param_filename'],**case_data['param']['args']) + + # run simulation + t1 = timer.time() + + if case_data['ode_method'] == 'RK4': + t,x,u,y = RK4(case_data['x0'],case_data['dt'],case_data['tspan'],case_data['dfsm'],case_data['param']) + + elif case_data['ode_method'] == 'ABM4': + t,x,u,y = ABM4(case_data['x0'],case_data['dt'],case_data['tspan'],case_data['dfsm'],case_data['param']) + t2 = timer.time() + + print(t2-t1) + # shut down controller + case_data['param']['controller_interface'].kill_discon() + + # initialize + sim_results = {} + sim_results['case_no'] = case_data['case'] + sim_results['T_dfsm'] = t + sim_results['states_dfsm'] = x + sim_results['controls_dfsm'] = u + sim_results['outputs_dfsm'] = y + + outputs.append(sim_results) + + return outputs + +def run_mpi(case_data_all,mpi_options): + + from mpi4py import MPI + + # mpi comm management + comm = MPI.COMM_WORLD + rank = comm.Get_rank() + sub_ranks = mpi_options['mpi_comm_map_down'][rank] + + size = len(sub_ranks) + + N_cases = len(case_data_all) + N_loops = int(np.ceil(float(N_cases)/float(size))) + + sim_results = [] + for i in range(N_loops): + idx_s = i*size + idx_e = min((i+1)*size, N_cases) + + for j, case_data in enumerate(case_data_all[idx_s:idx_e]): + data = [evaluate_multi, case_data] + rank_j = sub_ranks[j] + comm.send(data, dest=rank_j, tag=0) + + # for rank_j in sub_ranks: + for j, case_data in enumerate(case_data_all[idx_s:idx_e]): + rank_j = sub_ranks[j] + data_out = comm.recv(source=rank_j, tag=1) + sim_results.append(data_out) + + # compile and store results + outputs = [] + + for icase,result_ in enumerate(sim_results): + t = result_[0] + x = result_[1] + u = result_[2] + y = result_[3] + + # initialize + sim_results = {} + + # store + sim_results['case_no'] = icase + sim_results['T_dfsm'] = t + sim_results['states_dfsm'] = x + sim_results['controls_dfsm'] = u + sim_results['outputs_dfsm'] = y + + outputs.append(sim_results) + + + + return outputs + + + + +def generate_wind_files_local(fst_vt, modopt, inputs, discrete_inputs, FAST_runDirectory, FAST_namingOut, wind_directory): + + DLCs = modopt['DLC_driver']['DLCs'] + # Initialize the DLC generator + cut_in = float(inputs['V_cutin']) + cut_out = float(inputs['V_cutout']) + rated = float(inputs['Vrated']) + ws_class = discrete_inputs['turbine_class'] + wt_class = discrete_inputs['turbulence_class'] + hub_height = float(inputs['hub_height']) + rotorD = float(inputs['Rtip'])*2. + PLExp = float(inputs['shearExp']) + fix_wind_seeds = modopt['DLC_driver']['fix_wind_seeds'] + fix_wave_seeds = modopt['DLC_driver']['fix_wave_seeds'] + metocean = modopt['DLC_driver']['metocean_conditions'] + dlc_generator = DLCGenerator( + metocean = metocean, + **{ + 'ws_cut_in': cut_in, + 'ws_cut_out':cut_out, + 'MHK': True, + } + ) + # dlc_generator = DLCGenerator(cut_in, cut_out, rated, ws_class, wt_class, fix_wind_seeds, fix_wave_seeds, metocean) + # Generate cases from user inputs + for i_DLC in range(len(DLCs)): + DLCopt = DLCs[i_DLC] + dlc_generator.generate(DLCopt['DLC'], DLCopt) + + # Initialize parametric inputs + WindFile_type = np.zeros(dlc_generator.n_cases, dtype=int) + WindFile_name = [''] * dlc_generator.n_cases + rot_speed_initial = np.zeros(dlc_generator.n_cases) + pitch_initial = np.zeros(dlc_generator.n_cases) + shutdown_time = np.full(dlc_generator.n_cases, fill_value = 9999) + azimuth_init = np.full(dlc_generator.n_cases, fill_value = 0) + WindHd = np.zeros(dlc_generator.n_cases) + WaveHs = np.zeros(dlc_generator.n_cases) + WaveTp = np.zeros(dlc_generator.n_cases) + WaveHd = np.zeros(dlc_generator.n_cases) + WaveGamma = np.zeros(dlc_generator.n_cases) + WaveSeed1 = np.zeros(dlc_generator.n_cases, dtype=int) + TMax = np.zeros(dlc_generator.n_cases) + TStart = np.zeros(dlc_generator.n_cases) + dlc_label = [''] * dlc_generator.n_cases + wind_seed = np.zeros(dlc_generator.n_cases, dtype=int) + mean_wind_speed = np.zeros(dlc_generator.n_cases) + yaw_misalignment = np.zeros(dlc_generator.n_cases) + DT = np.full(dlc_generator.n_cases, fill_value = fst_vt['Fst']['DT']) + aero_mod = np.full(dlc_generator.n_cases, fill_value = fst_vt['AeroDyn15']['AFAeroMod']) + wake_mod = np.full(dlc_generator.n_cases, fill_value = fst_vt['AeroDyn15']['WakeMod']) + dt_fvw = np.zeros(dlc_generator.n_cases) + tMin = np.zeros(dlc_generator.n_cases) + nNWPanels = np.zeros(dlc_generator.n_cases, dtype=int) + nNWPanelsFree = np.zeros(dlc_generator.n_cases, dtype=int) + nFWPanels = np.zeros(dlc_generator.n_cases, dtype=int) + nFWPanelsFree = np.zeros(dlc_generator.n_cases, dtype=int) + + # fix hub height if MHK + if modopt['flags']['marine_hydro']: + # make grid span whole water depth for now, ref height will be actual hub height to get speed right + # in deeper water, will need something better than this + grid_height = 2. * np.abs(hub_height) - 1.e-3 + hub_height = grid_height/ 2 + ref_height = float(inputs['water_depth'] - np.abs(inputs['hub_height'])) + + # Inflow wind wants these relative to sea bed + fst_vt['InflowWind']['WindVziList'] = ref_height + + else: + ref_height = hub_height + + + for i_case in range(dlc_generator.n_cases): + if dlc_generator.cases[i_case].turbulent: + # Assign values common to all DLCs + # Wind turbulence class + if dlc_generator.cases[i_case].IECturbc > 0: # use custom TI for DLC case + dlc_generator.cases[i_case].IECturbc = str(dlc_generator.cases[i_case].IECturbc) + dlc_generator.cases[i_case].IEC_WindType = 'NTM' + else: + dlc_generator.cases[i_case].IECturbc = wt_class + # Reference height for wind speed + if not dlc_generator.cases[i_case].RefHt: # default RefHt is 0, use hub_height if not set + dlc_generator.cases[i_case].RefHt = ref_height + # Center of wind grid (TurbSim confusingly calls it HubHt) + dlc_generator.cases[i_case].HubHt = np.abs(hub_height) + # Height of wind grid, it stops 1 mm above the ground + dlc_generator.cases[i_case].GridHeight = 2. * np.abs(hub_height) - 1.e-3 + # If OLAF is called, make wind grid 3x higher, taller, and wider + if fst_vt['AeroDyn15']['WakeMod'] == 3: + dlc_generator.cases[i_case].HubHt *= 3. + dlc_generator.cases[i_case].GridHeight *= 3. + # Width of wind grid, same of height + dlc_generator.cases[i_case].GridWidth = dlc_generator.cases[i_case].GridHeight + # Power law exponent of wind shear + if dlc_generator.cases[i_case].PLExp < 0: # use PLExp based on environment options (shear_exp), otherwise use custom DLC PLExp + dlc_generator.cases[i_case].PLExp = PLExp + # Length of wind grids + dlc_generator.cases[i_case].AnalysisTime = dlc_generator.cases[i_case].analysis_time + dlc_generator.cases[i_case].transient_time + turbsim_exe = shutil.which('turbsim') + print('generating wind files') + for i_case in range(dlc_generator.n_cases): + WindFile_type[i_case] , WindFile_name[i_case] = generate_wind_files( + dlc_generator, FAST_namingOut, wind_directory, rotorD, hub_height, turbsim_exe,i_case) + + # Set initial rotor speed and pitch if the WT operates in this DLC and available, + # otherwise set pitch to 90 deg and rotor speed to 0 rpm when not operating + # set rotor speed to rated and pitch to 15 deg if operating + for i_case in range(dlc_generator.n_cases): + if 'operating' in dlc_generator.cases[i_case].turbine_status: + # We have initial conditions from WISDEM + if ('U' in inputs) and ('Omega' in inputs) and ('pitch' in inputs): + rot_speed_initial[i_case] = np.interp(dlc_generator.cases[i_case].URef, inputs['U'], inputs['Omega']) + pitch_initial[i_case] = np.interp(dlc_generator.cases[i_case].URef, inputs['U'], inputs['pitch']) + else: + rot_speed_initial[i_case] = fst_vt['DISCON_in']['PC_RefSpd'] * 30 / np.pi / fst_vt['ElastoDyn']['GBRatio'] + pitch_initial[i_case] = 15 + + if dlc_generator.cases[i_case].turbine_status == 'operating-shutdown': + shutdown_time[i_case] = dlc_generator.cases[i_case].shutdown_time + else: + rot_speed_initial[i_case] = 0. + pitch_initial[i_case] = 90. + shutdown_time[i_case] = 0 + aero_mod[i_case] = 1 + wake_mod[i_case] = 0 + + # Wave inputs to HydroDyn + WindHd[i_case] = dlc_generator.cases[i_case].wind_heading + WaveHs[i_case] = dlc_generator.cases[i_case].wave_height + WaveTp[i_case] = dlc_generator.cases[i_case].wave_period + WaveHd[i_case] = dlc_generator.cases[i_case].wave_heading + WaveGamma[i_case] = dlc_generator.cases[i_case].wave_gamma + WaveSeed1[i_case] = dlc_generator.cases[i_case].wave_seed1 + + # Other case info + TMax[i_case] = dlc_generator.cases[i_case].analysis_time + dlc_generator.cases[i_case].transient_time + TStart[i_case] = dlc_generator.cases[i_case].transient_time + dlc_label[i_case] = dlc_generator.cases[i_case].label + wind_seed[i_case] = dlc_generator.cases[i_case].RandSeed1 + mean_wind_speed[i_case] = dlc_generator.cases[i_case].URef + yaw_misalignment[i_case] = dlc_generator.cases[i_case].yaw_misalign + azimuth_init[i_case] = dlc_generator.cases[i_case].azimuth_init + + # Current + # CurrMod = np.zeros(dlc_generator.n_cases,dtype=int) + # CurrDIV = np.array([c.current for c in dlc_generator.cases]) + # CurrMod[CurrDIV > 0] = 1 + + # Parameteric inputs + case_inputs = {} + # Main fst + case_inputs[("Fst","DT")] = {'vals':DT, 'group':1} + case_inputs[("Fst","TMax")] = {'vals':TMax, 'group':1} + case_inputs[("Fst","TStart")] = {'vals':TStart, 'group':1} + + # Inflow wind + case_inputs[("InflowWind","WindType")] = {'vals':WindFile_type, 'group':1} + case_inputs[("InflowWind","HWindSpeed")] = {'vals':mean_wind_speed, 'group':1} + case_inputs[("InflowWind","FileName_BTS")] = {'vals':WindFile_name, 'group':1} + case_inputs[("InflowWind","Filename_Uni")] = {'vals':WindFile_name, 'group':1} + case_inputs[("InflowWind","RefLength")] = {'vals':[rotorD], 'group':0} + case_inputs[("InflowWind","PropagationDir")] = {'vals':WindHd, 'group':1} + case_inputs[("InflowWind","RefHt_Uni")] = {'vals':[np.abs(hub_height)], 'group':0} #TODO: check if this is the distance from sea level or bottom + + # Initial conditions for rotor speed, pitch, and azimuth + case_inputs[("ElastoDyn","RotSpeed")] = {'vals':rot_speed_initial, 'group':1} + case_inputs[("ElastoDyn","BlPitch1")] = {'vals':pitch_initial, 'group':1} + case_inputs[("ElastoDyn","BlPitch2")] = case_inputs[("ElastoDyn","BlPitch1")] + case_inputs[("ElastoDyn","BlPitch3")] = case_inputs[("ElastoDyn","BlPitch1")] + case_inputs[("ElastoDyn","Azimuth")] = {'vals':azimuth_init, 'group':1} + + # Yaw offset + case_inputs[("ElastoDyn","NacYaw")] = {'vals':yaw_misalignment, 'group':1} + + # Inputs to HydroDyn + case_inputs[("HydroDyn","WaveHs")] = {'vals':WaveHs, 'group':1} + case_inputs[("HydroDyn","WaveTp")] = {'vals':WaveTp, 'group':1} + case_inputs[("HydroDyn","WaveDir")] = {'vals':WaveHd, 'group':1} + case_inputs[("HydroDyn","WavePkShp")] = {'vals':WaveGamma, 'group':1} + case_inputs[("HydroDyn","WaveSeed1")] = {'vals':WaveSeed1, 'group':1} + + # Inputs to ServoDyn (parking), PitManRat and BlPitchF are ServoDyn modeling_options + case_inputs[("ServoDyn","TPitManS1")] = {'vals':shutdown_time, 'group':1} + case_inputs[("ServoDyn","TPitManS2")] = {'vals':shutdown_time, 'group':1} + case_inputs[("ServoDyn","TPitManS3")] = {'vals':shutdown_time, 'group':1} + + # case_inputs[("HydroDyn","CurrMod")] = {'vals':CurrMod, 'group':1} + # case_inputs[("HydroDyn","CurrDIV")] = {'vals':CurrDIV, 'group':1} + + # Inputs to AeroDyn (parking) + case_inputs[("AeroDyn15","AFAeroMod")] = {'vals':aero_mod, 'group':1} + case_inputs[("AeroDyn15","WakeMod")] = {'vals':wake_mod, 'group':1} + + # Inputs to OLAF + case_inputs[("AeroDyn15","OLAF","DTfvw")] = {'vals':dt_fvw, 'group':1} + case_inputs[("AeroDyn15","OLAF","nNWPanels")] = {'vals':nNWPanels, 'group':1} + case_inputs[("AeroDyn15","OLAF","nNWPanelsFree")] = {'vals':nNWPanelsFree, 'group':1} + case_inputs[("AeroDyn15","OLAF","nFWPanels")] = {'vals':nFWPanels, 'group':1} + case_inputs[("AeroDyn15","OLAF","nFWPanelsFree")] = {'vals':nFWPanelsFree, 'group':1} + + # DLC Label add these for the case matrix and delete from the case_list + case_inputs[("DLC","Label")] = {'vals':dlc_label, 'group':1} + case_inputs[("DLC","WindSeed")] = {'vals':wind_seed, 'group':1} + case_inputs[("DLC","MeanWS")] = {'vals':mean_wind_speed, 'group':1} + fst_vt['DLC'] = [] + + # Append current DLC to full list of cases + FAST_InputFile = modopt['General']['openfast_configuration']['OF_run_fst'] + case_list, case_name = CaseGen_General(case_inputs, FAST_runDirectory, FAST_InputFile) + + + # Now delete the DLC-based case_inputs because they don't play nicely with aeroelasticse + for case in case_list: + for key in list(case): + if key[0] == 'DLC': + del case[key] + + return dlc_generator, case_list, case_name,TMax, TStart + + + +def write_FAST_files(fst_vt, FAST_runDirectory, FAST_namingOut): + writer = InputWriter_OpenFAST() + writer.fst_vt = fst_vt + writer.FAST_runDirectory = FAST_runDirectory + writer.FAST_namingOut = FAST_namingOut + + writer.execute() + + + +def extract_simulation_results(modeling_data_path, model_options): + + # Get the path to the outb files in the folder, and arrange them + outfiles = [os.path.join(modeling_data_path,f) for f in os.listdir(modeling_data_path) if valid_extension(f)] + outfiles = sorted(outfiles) + + # Get the states, controls and outputs of the DFSM model + reqd_states = model_options['reqd_states'] + reqd_controls = model_options['reqd_controls'] + reqd_outputs = model_options['reqd_outputs'] + + + # Get scaling and filtering arguments + # --these arguments are used to scale and filter specific signals before constructing the DFSM model + scale_args = model_options['scale_args'] + filter_args = model_options['filter_args'] + + # Get additional options + + # 1. file name: Referes to file that has the linear models are available + filename = model_options['linear_model_file'] + if filename == 'none': + filename = None + + # 2. region: The region for which the linear model stored in the file is constructed + region = model_options['region'] + + # 3. add_dx2: Flag to add the first time/second time state derivatives to the model + add_dx2 = model_options['add_dx2'] + + # 4. tmin and tmax: If specified, the simulation data corresponding to t \in [tmin,tmax] is used to construct the DFSM + # -- tmin is by default 0, and tmax is none + tmin = model_options['tmin']; tmax = model_options['tmax'] + + # Extract the simulation + # instantiate class + sim_detail = SimulationDetails(outfiles, + reqd_states,reqd_controls,reqd_outputs, + scale_args, filter_args, + tmin = tmin, add_dx2 = add_dx2, + linear_model_file = filename, region = region) + + # load and process data + sim_detail.load_openfast_sim() + + return sim_detail + +def construct_dfsm_helper(sim_detail, construction_options): + + # Extract options + n_samples = construction_options['n_samples'] + + # Sampling method + sampling_method = construction_options['sampling_method'] + + # Linear model type + L_type = construction_options['L_type'] + + # Nonlinear model type + N_type = construction_options['N_type'] + + if N_type == 'None': + N_type = None + + # Train split + train_split = construction_options['train_split'] + + + # instantiate DFSM class + dfsm = DFSM(sim_detail, + n_samples = n_samples, + sampling_method = sampling_method, + L_type = L_type, + N_type = N_type, + train_split = train_split) + + # construct + dfsm.construct_surrogate() + + return dfsm + + + +def dfsm_wrapper(fst_vt, modopt, inputs, discrete_inputs, FAST_runDirectory = None, FAST_namingOut = None,mpi_options = None): + + print('----------------------------------------------------') + print('Running DFSM') + print('Derivative Function Surrogate Model') + print('This program is licensed under Apache License Version 2.0 and comes with ABSOLUTELY NO WARRANTY.') + print('----------------------------------------------------') + + # Load the stored DFSM model and run simulations with it + general_options = modopt['DFSM']['general_options'] + model_options = modopt['DFSM']['model_options'] + + reqd_states = model_options['reqd_states'] + reqd_controls = model_options['reqd_controls'] + reqd_outputs = model_options['reqd_outputs'] + scale_args = model_options['scale_args'] + + # set run dir. THis is the directory where OpenFAST files are stored + general_options['run_dir'] = modopt['General']['openfast_configuration']['OF_run_dir'] + + # load DFSM model + dfsm_file = general_options['dfsm_file'] + + with open(dfsm_file,'rb') as handle: + dfsm = pickle.load(handle) + + interp_type = model_options['interp_type'] + + ode_method = model_options['ode_method'] + + # setup interpolation method for LPV model + dfsm.setup_LPV(interp_type) + + # If the test data is online, this implies, the DFSM will be used to run simulations + # and save them in the current openfast run directory + testing_data_path = FAST_runDirectory + + # folder to save dfsm results + dfsm_save_folder = testing_data_path + os.sep + 'dfsm_results' + + + #------------------------------------------------------ + # Use case + #------------------------------------------------------ + + # Get usecase + usecase = general_options['usecase'] + + if usecase == 'closed-loop-simulation' : + + # generate wind files + wind_directory = FAST_runDirectory + os.sep + 'wind' + dlc_generator, case_list, case_name, TMax, TStart = generate_wind_files_local(fst_vt, modopt, inputs, discrete_inputs, FAST_runDirectory, FAST_namingOut, wind_directory) + fst_vt['Fst']['TMax'] = TMax[0] + fst_vt['Fst']['TStart'] = TStart[0] + # Extract disturbance(s) + test_dataset = [] + + # initialize random number generator + rng = np.random.default_rng(12345) + + for i_case,case in enumerate(case_list): + ts_file = TurbSimFile(case[('InflowWind','FileName_BTS')]) + ts_file.compute_rot_avg(fst_vt['ElastoDyn']['TipRad']) + u_h = ts_file['rot_avg'][0,:] + tt = ts_file['t'] + dt = case[('Fst','DT')] + + eta = generate_wave_elev(tt,dlc_generator.cases[i_case].wave_height,dlc_generator.cases[i_case].wave_period,rng) + + test_dataset.append({'time':tt, 'wind_speed': u_h,'wave_elev':eta}) + + # number of test cases + test_ind = np.arange(len(case_list)) + + # generate OpenFAST files + # This step generates the DISCON.IN and cp-ct-cq.txt files which are need to run closed-loop simulations + + for case in case_name: + write_FAST_files(fst_vt, FAST_runDirectory, case) + + case_names = case_naming(len(case_name),'dfsm') + + + # initialize storage lists + output_list = [] + ct = [] + + # required maginitude and fatigue channels + #-- note: Some of these quantities are not always modeled using the DFSM. The corresponding outputs for these quantities will be 'Nan' + magnitude_channels = {'LSShftF': ['RotThrust', 'LSShftFys', 'LSShftFzs'], + 'LSShftM': ['RotTorq', 'LSSTipMys', 'LSSTipMzs'], + 'RootMc1': ['RootMxc1', 'RootMyc1', 'RootMzc1'], + 'RootMc2': ['RootMxc2', 'RootMyc2', 'RootMzc2'], + 'RootMc3': ['RootMxc3', 'RootMyc3', 'RootMzc3'], + 'TipDc1': ['TipDxc1', 'TipDyc1', 'TipDzc1'], + 'TipDc2': ['TipDxc2', 'TipDyc2', 'TipDzc2'], + 'TipDc3': ['TipDxc3', 'TipDyc3', 'TipDzc3'], + 'TwrBsM': [ 'TwrBsMyt'], + 'NcIMUTA': ['NcIMUTAxs', 'NcIMUTAys', 'NcIMUTAzs']} + + fatigue_channels = { + 'RootMc1': FatigueParams(slope=10), + 'RootMc2': FatigueParams(slope=10), + 'RootMc3': FatigueParams(slope=10), + 'RootMyb1': FatigueParams(slope=10), + 'RootMyb2': FatigueParams(slope=10), + 'RootMyb3': FatigueParams(slope=10), + 'TwrBsM': FatigueParams(slope=4), + 'LSShftM': FatigueParams(slope=4), + } + + DISCON_file = [os.path.join(testing_data_path,f) for f in os.listdir(testing_data_path) if valid_extension_DISCON(f)] + GB_ratio = fst_vt['DISCON_in']['WE_GearboxRatio'] + case_data_all = [] + + for idx,ind in enumerate(test_ind): + + case_data = {} + case_data['case'] = idx + + test_data = test_dataset[ind] + + time = test_data['time'] + wind_speed = test_data['wind_speed'] + wind_fun = CubicSpline(time, wind_speed) + + x0 = np.zeros((dfsm.n_deriv,)) + #x0[0] = 20 + #x0[dfsm.gen_speed_ind] = 600 + + t0 = time[0]; + tf = time[-1] + tspan = [t0,tf] + + args = {'DT':dt, + 'num_blade':2,'pitch':0} + + + if 'Wave1Elev' in model_options['reqd_controls']: + wave_elev = test_data['wave_elev'] + wave_fun = CubicSpline(time,wave_elev) + + # initialize param dict + param = {} + + # populate dictonary with relevant info + param['VS_GenEff'] = fst_vt['DISCON_in']['VS_GenEff'] + param['WE_GearboxRatio'] = fst_vt['DISCON_in']['WE_GearboxRatio'] + param['VS_RtPwr'] = fst_vt['DISCON_in']['VS_RtPwr'] + param['time'] = [t0] + param['dt']= dt + param['blade_pitch'] = [0] + param['gen_torque'] = [7] + param['t0'] = t0 + param['tf'] = tf + param['w_fun'] = wind_fun + param['gen_speed_scaling'] = scale_args['state_scaling_factor'][-1] + param['lib_name'] = fst_vt['ServoDyn']['DLL_FileName'] + if 'Wave1Elev' in model_options['reqd_controls']: + param['wave_fun'] = wave_fun + else: + param['wave_fun'] = None + + param['num_blade'] = 2 + param['ny'] = dfsm.n_outputs + param['args'] = args + param['param_filename'] = DISCON_file[idx] + + + + + # # start timer and solve for the states and controls + # t1 = timer.time() + # T_dfsm, states_dfsm, controls_dfsm,outputs_dfsm = RK4(x0, dt, tspan, dfsm, param) + # t2 = timer.time() + # dfsm.simulation_time = (t2-t1) + + case_data = {} + case_data['case'] = idx + case_data['param'] = param + case_data['dt'] = dt + case_data['x0'] = x0 + case_data['tspan'] = tspan + case_data['dfsm'] = dfsm + case_data['ode_method'] = ode_method + + case_data_all.append(case_data) + + if mpi_options['mpi_run']: + + # evaluate the closed loop simulations in parallel using MPI + sim_outputs = run_mpi(case_data_all,mpi_options) + + else: + + # evaluate the closed loop simulations serially + sim_outputs = run_serial(case_data_all) + + # plot properties + markersize = 10 + linewidth = 1.5 + fontsize_legend = 16 + fontsize_axlabel = 18 + fontsize_tick = 12 + + # save results + if not os.path.exists(dfsm_save_folder): + os.makedirs(dfsm_save_folder) + + for icase,sim_result in enumerate(sim_outputs): + + # extract results + T_dfsm = sim_result['T_dfsm'] + states_dfsm = sim_result['states_dfsm'] + controls_dfsm = sim_result['controls_dfsm'] + outputs_dfsm = sim_result['outputs_dfsm'] + + + + for iu,control in enumerate(reqd_controls): + + fig,ax = plt.subplots(1) + + ax.plot(T_dfsm,controls_dfsm[:,iu],label = 'DFSM') + + ax.set_title(control,fontsize = fontsize_axlabel) + ax.set_xlim(tspan) + ax.tick_params(labelsize=fontsize_tick) + ax.legend(ncol = 2,fontsize = fontsize_legend) + ax.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + + if general_options['save_results']: + fig.savefig(dfsm_save_folder +os.sep+ control + '_' + str(icase) +'_comp.pdf') + + plt.close(fig) + + + + #------------------------------------------------------ + # Plot States + #------------------------------------------------------ + for ix,state in enumerate(reqd_states): + + fig,ax = plt.subplots(1) + + ax.plot(T_dfsm,states_dfsm[:,ix],label = 'DFSM') + + ax.set_title(state,fontsize = fontsize_axlabel) + ax.set_xlim(tspan) + ax.tick_params(labelsize=fontsize_tick) + ax.legend(ncol = 2,fontsize = fontsize_legend) + ax.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + + if general_options['save_results']: + + fig.savefig(dfsm_save_folder +os.sep+ state+ '_'+str(icase) +'_comp.pdf') + + plt.close(fig) + + #------------------------------------------ + # Plot Outputs + #------------------------------------------ + for iy,output_ in enumerate(reqd_outputs): + + fig,ax = plt.subplots(1) + + ax.plot(T_dfsm,outputs_dfsm[:,iy],label = 'DFSM') + + + ax.set_title(output_,fontsize = fontsize_axlabel) + ax.set_xlim(tspan) + ax.tick_params(labelsize=fontsize_tick) + ax.legend(ncol = 2,fontsize = fontsize_legend) + ax.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + + if general_options['save_results']: + + fig.savefig(dfsm_save_folder +os.sep+ output_+ '_'+str(icase) + '_comp.pdf') + + plt.close(fig) + + # fig, ((ax1,ax2,ax3)) = plt.subplots(3,1,) + + # # wind + # ax1.plot(T_dfsm,controls_dfsm[:,0]) + # ax1.set_title('Wind Speed [m/s]') + # ax1.set_xlim([t0,tf]) + + # # torue + # ax2.plot(T_dfsm,controls_dfsm[:,1]) + # #ax2.set_ylim([1.8,2]) + # ax2.set_title('Gen Torque [KWm]') + # ax2.set_xlim([t0,tf]) + + # # blade pitch + # ax3.plot(T_dfsm,controls_dfsm[:,2]) + # #ax3.set_ylim([0.2, 0.3]) + # ax3.set_title('Bld Pitch [deg]') + # ax3.set_xlim([t0,tf]) + + # fig.subplots_adjust(hspace = 0.65) + + # if general_options['save_results']: + # # save results + # if not os.path.exists(dfsm_save_folder): + # os.makedirs(dfsm_save_folder) + + # fig.savefig(dfsm_save_folder +os.sep+ 'Controls' +str(idx) + '.pdf') + + + # post processing + case_name = case_names[icase] + + # compile results from DFSM + OutData = compile_dfsm_results(T_dfsm,states_dfsm,controls_dfsm,outputs_dfsm,model_options['reqd_states'], + model_options['reqd_controls'],model_options['reqd_outputs'],GB_ratio,TStart[icase]) + + ct.append(OutData) + + # get output + output = OpenFASTOutput.from_dict(OutData,case_name,magnitude_channels = magnitude_channels) + + output_list.append(output) + + # Collect outputs + ss = {} + et = {} + dl = {} + dam = {} + + + loads_analysis = LoadsAnalysis( + outputs = [], + magnitude_channels = magnitude_channels, + fatigue_channels = fatigue_channels + ) + + for output in output_list: + _name, _ss, _et, _dl, _dam = loads_analysis._process_output(output) + ss[_name] = _ss + et[_name] = _et + dl[_name] = _dl + dam[_name] = _dam + + summary_stats, extreme_table, DELs, Damage = loads_analysis.post_process(ss, et, dl, dam) + + return summary_stats,extreme_table,DELs,Damage,case_list, case_name, ct, dlc_generator, TMax, TStart + + + + + + diff --git a/weis/dfsm/evaluate_dfsm.py b/weis/dfsm/evaluate_dfsm.py new file mode 100755 index 000000000..2b134d50c --- /dev/null +++ b/weis/dfsm/evaluate_dfsm.py @@ -0,0 +1,234 @@ +import numpy as np + + +def evaluate_dfsm(DFSM,inputs,fun_type = 'deriv'): + + # get number of points + n_points = len(np.shape(inputs)) + + if n_points == 1: + inputs = inputs.reshape(1,-1) + nt = 1 + else: + nt = np.shape(inputs)[0] + + # based on the function type extract the info + if fun_type == 'deriv': + + # state derivative function + lin = DFSM.AB + nonlin = DFSM.nonlin_deriv + no = DFSM.n_deriv + error_ind = DFSM.error_ind_deriv + scaler_outputs = DFSM.scaler_dx + + elif fun_type == 'outputs': + + # output function + lin = DFSM.CD + nonlin = DFSM.nonlin_outputs + no = DFSM.n_outputs + error_ind = DFSM.error_ind_outputs + scaler_outputs = DFSM.scaler_outputs + + if DFSM.L_type == None: + + dx_lin = np.zeros((nt,no)) + + else: + + # if LTI model + if DFSM.L_type == 'LTI': + + dx_lin = np.dot(inputs,lin) + + elif DFSM.L_type == 'LPV': + + A_fun = DFSM.A_fun + B_fun = DFSM.B_fun + C_fun = DFSM.C_fun + D_fun = DFSM.D_fun + + nu = DFSM.n_model_inputs - DFSM.n_deriv + + if nt == 1: + + inputs = np.squeeze(inputs) + + # extract inputs and outputs + u = inputs[:nu] + x = inputs[nu:] + + # extract wind speed + w = u[0] + + # evaluate the LPV function to get the system matrices + if fun_type == 'deriv': + + # A and B matrices for the derivative function + A_ = A_fun(w) + B_ = B_fun(w) + + + + dx_lin = np.dot(A_,x) + np.dot(B_,u) + + elif fun_type == 'outputs': + + # C and D matrices for outputs + C_ = C_fun(w) + D_ = D_fun(w) + + dx_lin = np.dot(C_,x) + np.dot(D_,u) + + elif nt > 1: + + # initialize + dx_lin = np.zeros((nt,no)) + + # extract inputs and outputs + u = inputs[:,:nu] + x = inputs[:,nu:] + + for i in range(nt): + + w = u[i,0] + x_ = x[i,:] + u_ = u[i,:] + + # evaluate the LPV function to get the system matrices + if fun_type == 'deriv': + + # A and B matrices for the derivative function + A_ = A_fun(w) + B_ = B_fun(w) + + + dx_lin[i,:] = np.dot(A_,x_) + np.dot(B_,u_) + + elif fun_type == 'outputs': + + # C and D matrices for outputs + C_ = C_fun(w) + D_ = D_fun(w) + + dx_lin[i,:] = np.dot(C_,x_) + np.dot(D_,u_) + + # initialize nonlinear part + dx_nonlin = np.zeros((nt,no)) + + # loop through and evaluate + if not(all(nonlin == None)): + + if DFSM.N_type == 'GPR': + + for io in range(no): + + if error_ind[io]: + + nonlin_func = nonlin[io] + + scaler = DFSM.scaler_inputs + + # scale + if scaler == None: + inputs_ = inputs + else: + if nt == 1: + inputs_ = scaler.transform(inputs.reshape(1,-1)) + else: + inputs_ = scaler.transform(inputs) + + if DFSM.ensemble_flag: + n_committee = len(nonlin_func) + nonlin_pred = np.zeros((nt,n_committee)) + + for i_comm,committee_member in enumerate(nonlin_func): + nonlin_pred[:,i_comm] = committee_member.predict(inputs_) + + dx_nonlin[:,io] = np.mean(nonlin_pred,axis = 1) + + elif DFSM.N_type == 'KPLS': + + for io in range(no): + + if error_ind[io]: + + nonlin_func = nonlin[io] + + scaler_inputs = DFSM.scaler_inputs + + # scale + if scaler_inputs == None: + inputs_ = inputs + else: + if nt == 1: + inputs_ = scaler_inputs.transform(inputs.reshape(1,-1)) + else: + inputs_ = scaler_inputs.transform(inputs) + + if DFSM.ensemble_flag: + + n_committee = len(nonlin_func) + nonlin_pred = np.zeros((nt,n_committee)) + + for i_comm,committee_member in enumerate(nonlin_func): + nonlin_pred[:,i_comm] = np.squeeze(committee_member.predict_values(inputs_)) + + dx_nonlin[:,io] = np.mean(nonlin_pred,axis = 1) + #breakpoint() + + + + + elif DFSM.N_type == 'NN': + + for io in range(no): + + if error_ind[io]: + + nonlin_func = nonlin[io] + + scaler_inputs = DFSM.scaler_inputs + + # scale + if scaler_inputs == None: + inputs_ = inputs + else: + + if nt == 1: + inputs_ = scaler_inputs.transform(inputs.reshape(1,-1)) + else: + inputs_ = scaler_inputs.transform(inputs) + + if DFSM.ensemble_flag: + n_committee = len(nonlin_func) + nonlin_pred = np.zeros((nt,n_committee)) + + for i_comm,committee_member in enumerate(nonlin_func): + nonlin_pred[:,i_comm] = committee_member.predict(inputs_) + + dx_nonlin[:,io] = np.mean(nonlin_pred,axis = 1) + + if not(scaler_outputs == None): + + dx_nonlin = scaler_outputs.inverse_transform(dx_nonlin) + + + + + dx = dx_lin + dx_nonlin + + if n_points == 1: + dx = np.squeeze(dx) + + return dx + + + + + + + + + diff --git a/weis/dfsm/generate_wave_elev.py b/weis/dfsm/generate_wave_elev.py new file mode 100644 index 000000000..28a679337 --- /dev/null +++ b/weis/dfsm/generate_wave_elev.py @@ -0,0 +1,150 @@ +import numpy as np +import matplotlib.pyplot as plt + +from numpy import pi +from rosco.toolbox.ofTools.util import spectral + + +def jonswap(Hs,Tp,df,f_high,f_low): + + fp = 1/Tp + + Tp_o_Hs = Tp/np.sqrt(Hs) + + if (Tp_o_Hs) < 3.6: + gamma = 5 + elif (Tp_o_Hs >= 3.6) and (Tp_o_Hs <= 5): + gamma = np.exp(5.75 -1.15*Tp_o_Hs) + elif (Tp_o_Hs > 5): + gamma = 1 + + v_freq = np.arange(f_low,f_high,df) + nf = len(v_freq) + + S = np.zeros((nf,)) + + for i in range(nf): + + freq = v_freq[i] + + if freq <= fp : + sigma = 0.07 + else: + sigma = 0.09 + + S[i] = 0.3125*(Hs**2)*Tp*((freq/fp)**(-5))*np.exp(-1.25*(freq/fp)**(-4))*(1 -0.287*np.log (gamma))*(gamma)**(np.exp(-0.5*(((freq/fp )-1)*(1/sigma))**2)) + + return v_freq,S + + +def generate_wave_elev(time,Hs,Tp,rng): + ''' + Function to generate wave profiles for the given significant height and period + using the JONSWAP spectrum + + Code adopted from: http://emmanuel.branlard.free.fr/work/papers/html/2010wral4/Branlard-2010-WindTimesSeriesGeneration.pdf + + inputs: + time: time mesh + Hs: significant wave height + Tp: significant period + rng: random number generator with specified seed + + returns + eta: wave elevation time series + + ''' + + # number of time points + nt = len(time) + + # hard code frequency cut off + f_high = 0.5 # [hz] + f_low = 0.0250000585 #[hz] + + df = 0.0005 + + v_freq,S = jonswap(Hs,Tp,df,f_high,f_low) + + nf = len(v_freq) + + v_phase = rng.random((nf,))*2*pi + + v_amp = np.sqrt(2*S*df) + + eta = np.zeros((nt,)) + + for i in range(nt): + + eta[i] = np.sum(v_amp*np.cos(2*pi*v_freq*time[i] + v_phase)) + + return eta + + +if __name__ == '__main__': + + # test for function 'generate_wave_elev' + + # wave height and perisod corresponding to SSS + Hs = 6.3 # meter + Tp = 11.5 # seconds + + # generate time profile + t0 = 0 + dt = 0.01 + tf = 600 + + time = np.arange(t0,tf+dt,dt) + + # number of waves + n_wave = 3 + + # plot properties + markersize = 10 + linewidth = 1.5 + fontsize_legend = 14 + fontsize_axlabel = 18 + fontsize_tick = 12 + + # initialize two plots. one for time series of the wave profile, and one for PSD of the generated wave + + # initialize time series plot + fig,ax1 = plt.subplots(1) + ax1.tick_params(labelsize=fontsize_tick) + ax1.set_xlabel('Time [s]',fontsize = fontsize_axlabel) + ax1.set_ylabel('Wave Elevation [m]',fontsize = fontsize_axlabel) + ax1.set_xlim([t0,tf]) + + # initialize PSD plot + fig,ax2 = plt.subplots(1) + ax2.tick_params(labelsize=fontsize_tick) + ax2.set_xscale('log') + ax2.set_yscale('log') + ax2.set_xlabel('Freq. [Hz]',fontsize = fontsize_axlabel) + ax2.set_ylabel('PSD',fontsize = fontsize_axlabel) + + # initialize random number generator + rng = np.random.default_rng(12345) + + # loop through and generate wave profiles + for i in range(n_wave): + + # generate wave profile + eta = generate_wave_elev(time,Hs,Tp,rng) + + # plot time series + ax1.plot(time,eta,label = 'wave_' + str(i)) + + # eavluate PSD response + fq,y,_ = spectral.fft_wrap(time,eta,averaging = 'welch',averaging_window='Hamming', output_type='psd') + + # plot PSD + ax2.plot(fq,np.sqrt(y),label = 'wave_' + str(i)) + + # add legend + ax1.legend(ncol = 1,fontsize = fontsize_legend) + ax2.legend(ncol = 1,fontsize = fontsize_legend) + + plt.show() + + diff --git a/weis/dfsm/ode_algorithms.py b/weis/dfsm/ode_algorithms.py new file mode 100644 index 000000000..991daf469 --- /dev/null +++ b/weis/dfsm/ode_algorithms.py @@ -0,0 +1,374 @@ +import numpy as np +from weis.dfsm.evaluate_dfsm import evaluate_dfsm +from weis.dfsm.dfsm_utilities import extrapolate_controls + +# convert rpm to rad/s +# OpenFAST stores genspeed as rpm, whearas ROSCO requiers genspeed in rad/s +rpm2RadSec = 2.0*(np.pi)/60.0 +KWatt2Watt = 1000 + +def RK4(x0, dt, tspan, DFSM, param): + + # Implementation of the 4th order runge-kutta method + # calculate intervals + t0 = tspan[0]; tf = tspan[1] + + # initialize + T = np.arange(t0,tf+dt,dt) + nt = len(T); nx = len(x0) + + # states array + X = np.zeros((nt,nx)) + + # Check the number of outputs in the DFSM model + ny = param['ny'] + + # initalize storage array for outputs + if ny == 0: + Y = [] + else: + Y = np.zeros((nt,ny)) + + # starting point + X[0,:] = x0 + + # parameter to scale generator speed + gen_speed_scaling = param['gen_speed_scaling'] + + # extract current speed and wave function + wave_fun = param['wave_fun'] + wind_fun = param['w_fun'] + + # evaluate wind/current speed for the time stencil + WS = wind_fun(T) + + # initialize extrapolation arrays + if wave_fun == None: + U_extrap = np.zeros((2*nt-1,3)) + U = np.zeros((nt,3)) + + else: + WE = wave_fun(T) + U_extrap = np.zeros((2*nt-1,4)) + U = np.zeros((nt,4)) + + # storage array + ind_extrap = 0 + T_extrap = np.zeros((2*nt-1,)) + T_extrap[ind_extrap] = T[0] + + # set first entry in the extrapolation + U_extrap[ind_extrap,0] = WS[0] + U_extrap[ind_extrap,1] = param['gen_torque'][0] + U_extrap[ind_extrap,2] = param['blade_pitch'][0] + + # store control + U[0,DFSM.wind_speed_ind] = WS[0] + U[0,DFSM.gen_torque_ind] = param['gen_torque'][0] + U[0,DFSM.blade_pitch_ind] = param['blade_pitch'][0] + + + if not(wave_fun == None): + U[0,DFSM.wave_elev_ind] = WE[0] + U_extrap[ind_extrap,DFSM.wave_elev_ind] = WE[0] + + # loop through and evaluate states + for h in range(1,nt): + + t_n = T[h-1] + x_n = X[h-1,:] + + # get from previous time step + u_k1 = U[h-1,:] + + # inputs for DFSM + inputs = np.hstack([u_k1,x_n]) + + k1 = evaluate_dfsm(DFSM,inputs,'deriv') + + # zero order hold beteen time steps + u_k2 = u_k1 + + # add values to exrap array + ind_extrap += 1 + U_extrap[ind_extrap] = u_k2 + T_extrap[ind_extrap] = t_n + dt/2 + + # second step + # k2 = f(t_n + h/2,x_n + h*k1/2) + xn_k2 = x_n + dt/2*k1 + inputs = np.hstack([u_k2,xn_k2]) + k2 = evaluate_dfsm(DFSM,inputs,'deriv') + + # Third step + # k3 = f(t_n + h/2,x_n + h*k2/2) + u_k3 = u_k2 + xn_k3 = x_n + dt/2*k2 + inputs = np.hstack([u_k3,xn_k3]) + k3 = evaluate_dfsm(DFSM,inputs,'deriv') + + # extrapolate and find the value of the controls at tn + dt + u2_ = U_extrap[ind_extrap,:] + u1_ = U_extrap[ind_extrap-1,:] + t2_ = T_extrap[ind_extrap] + t1_ = T_extrap[ind_extrap-1] + u_k4 = extrapolate_controls(t_n + dt,[u1_, u2_],[t1_, t2_]) + u_k4[DFSM.wind_speed_ind] = wind_fun(t_n + dt) + + if not(wave_fun == None): + u_k4[DFSM.wave_elev_ind] = wave_fun(t_n + dt) + + # fourth step + # k3 = f(t_n + h,x_n + hk3) + xn_k4 = x_n + dt*k3 + inputs = np.hstack([u_k4,xn_k4]) + k4 = evaluate_dfsm(DFSM,inputs,'deriv') + + # runge kutta step + # x_n+1 = x_n + h/6*(k1 + 2k2 + 2k3 + k4) + x_step = x_n + dt/6*(k1 + 2*k2 + 2*k3 + k4) + + # evaluate outputs at t_n+1 using the stimate states and extrapolated controls + inputs = np.hstack([u_k4,x_step]) + + if ny > 0: + Y[h,:] = evaluate_dfsm(DFSM,inputs,'outputs') + + X[h,:] = x_step + + # Initialize turbine_state dict to pass the necessary information to ROSCO + turbine_state = {} + + # operating status of the turbine + if h == nt: + turbine_state['iStatus'] = -1 + else: + turbine_state['iStatus'] = 1 + + turbine_state['bld_pitch'] = np.deg2rad(U[h-1,DFSM.blade_pitch_ind]) # blade pitch + turbine_state['gen_torque'] = U[h-1,DFSM.gen_torque_ind]*KWatt2Watt # generator torque + turbine_state['t'] = t_n # previous time step + turbine_state['dt'] = dt # step size + turbine_state['ws'] = WS[h] # estimate wind speed + turbine_state['num_blades'] = int(3) # number of blades + turbine_state['gen_speed'] = X[h,DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling # generator speed + turbine_state['gen_eff'] = param['VS_GenEff']/100 # generator efficiency + turbine_state['rot_speed'] = X[h,DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling/param['WE_GearboxRatio'] # rotor speed + turbine_state['Yaw_fromNorth'] = 0 # yaw + turbine_state['Y_MeasErr'] = 0 + + if not(DFSM.FA_Acc_ind_s == None): + turbine_state['FA_Acc'] = X[h,DFSM.FA_Acc_ind_s] # tower-top acceleration if it is modelled as a state + + elif not(DFSM.FA_Acc_ind_o == None): + turbine_state['FA_Acc'] = Y[h,DFSM.FA_Acc_ind_o] # tower top acceleration if it is modelled as an output + + if not(DFSM.NacIMU_FA_Acc_ind_s == None): + turbine_state['NacIMU_FA_Acc'] = X[h,DFSM.NacIMU_FA_Acc_ind_s]*np.deg2rad(1) + + elif not(DFSM.NacIMU_FA_Acc_ind_o == None): + turbine_state['NacIMU_FA_Acc'] = Y[h,DFSM.NacIMU_FA_Acc_ind_o]*np.deg2rad(1) + + # call ROSCO to get control values + gen_torque, bld_pitch, nac_yawrate = param['controller_interface'].call_controller(turbine_state) + + # convert to right units + gen_torque = gen_torque/KWatt2Watt + bld_pitch = np.rad2deg(bld_pitch) + + # store + U[h,DFSM.wind_speed_ind] = WS[h] + U[h,DFSM.gen_torque_ind] = gen_torque + U[h,DFSM.blade_pitch_ind] = bld_pitch + + if not(wave_fun == None): + U[h,DFSM.wave_elev_ind] = WE[h] + + # update the extrapolation array with the control values calculated using ROSCO + ind_extrap += 1 + U_extrap[ind_extrap,:] = U[h,:] + + return T,X,U,Y + + + +def ABM4(x0, dt, tspan, DFSM, param): + +#---------------------------------------------------------------------------------------------------------------------------------- +# This subroutine implements the fourth-order Adams-Bashforth-Moulton Method (RK4) for numerically integrating ordinary +# differential equations: +# +# Let f(t, x) = xdot denote the time (t) derivative of the continuous states (x). +# +# Adams-Bashforth Predictor: \n +# x^p(t+dt) = x(t) + (dt / 24.) * ( 55.*f(t,x) - 59.*f(t-dt,x) + 37.*f(t-2.*dt,x) - 9.*f(t-3.*dt,x) ) +# +# Adams-Moulton Corrector: \n +# x(t+dt) = x(t) + (dt / 24.) * ( 9.*f(t+dt,x^p) + 19.*f(t,x) - 5.*f(t-dt,x) + 1.*f(t-2.*dt,x) ) +# +# See, e.g., +# https://en.wikiversity.org/wiki/Adams-Bashforth_and_Adams-Moulton_methods#Predictor%E2%80%93corrector_method +# +# or +# +# K. E. Atkinson, "An Introduction to Numerical Analysis", 1989, John Wiley & Sons, Inc, Second Edition. +#---------------------------------------------------------------------------------------------------------------------------------- + + # calculate intervals + t0 = tspan[0]; tf = tspan[1] + + # initialize + T = np.arange(t0,tf+dt,dt) + nt = len(T); nx = len(x0) + + # array to store states + X = np.zeros((nt,nx)) + + # array to store derivative function evaluations + F = np.zeros((nt,nx)) + + + # Check the number of outputs in the DFSM model + ny = param['ny'] + + # initalize storage array for outputs + if ny == 0: + Y = [] + else: + Y = np.zeros((nt,ny)) + + # starting point + X[0,:] = x0 + + # parameter to scale generator speed + gen_speed_scaling = param['gen_speed_scaling'] + + # extract current speed and wave function + wave_fun = param['wave_fun'] + wind_fun = param['w_fun'] + + # evaluate wind/current speed for the time stencil + WS = wind_fun(T) + + # initialize control arrays + if wave_fun == None: + U = np.zeros((nt,3)) + else: + WE = wave_fun(T) + U = np.zeros((nt,4)) + + # store control + U[0,DFSM.wind_speed_ind] = WS[0] + U[0,DFSM.gen_torque_ind] = param['gen_torque'][0] + U[0,DFSM.blade_pitch_ind] = param['blade_pitch'][0] + + if not(wave_fun == None): + U[0,DFSM.wave_elev_ind] = WE[0] + + inputs = np.hstack([U[0,:],X[0,:]]) + + # evaluate the derivative function value and store + F[0,:] = evaluate_dfsm(DFSM,inputs,'deriv') + + + # loop through and evaluate states + for h in range(1,nt): + + # for the first three time steps use Euler forward + + if h <=3: + + t_n = T[h-1] + x_n = X[h-1,:] + + # extract derivative function value + f_n = F[h-1,:] + + # calculate X at the next time step + x_step = x_n + dt*f_n + + u_ = U[h-1,:] + inputs = np.hstack([u_,x_step]) + + else: + + t_n = T[h-1] + x_n = X[h-1,:] + + f_n = F[h-1,:] + f_n1 = F[h-2,:] + f_n2 = F[h-3,:] + f_n3 = F[h-4,:] + + # predictor step + P = x_n + dt/24*(55*f_n -59*f_n1 + 37*f_n2 - 9*f_n3) + + # corrector step + u_ = U[h-1,:] + inputs_ = np.hstack([u_,P]) + f_nP = evaluate_dfsm(DFSM,inputs_,'deriv') + + x_step = x_n + dt/24*(9*f_nP + 19*f_n - 5*f_n1 + f_n2) + + inputs = np.hstack([u_,x_step]) + + if ny > 0: + Y[h,:] = evaluate_dfsm(DFSM,inputs,'outputs') + + X[h,:] = x_step + + # Initialize turbine_state dict to pass the necessary information to ROSCO + turbine_state = {} + + # operating status of the turbine + if h == nt: + turbine_state['iStatus'] = -1 + else: + turbine_state['iStatus'] = 1 + + turbine_state['bld_pitch'] = np.deg2rad(U[h-1,DFSM.blade_pitch_ind]) # blade pitch + turbine_state['gen_torque'] = U[h-1,DFSM.gen_torque_ind]*KWatt2Watt # generator torque + turbine_state['t'] = t_n # previous time step + turbine_state['dt'] = dt # step size + turbine_state['ws'] = WS[h] # estimate wind speed + turbine_state['num_blades'] = int(3) # number of blades + turbine_state['gen_speed'] = X[h,DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling # generator speed + turbine_state['gen_eff'] = param['VS_GenEff']/100 # generator efficiency + turbine_state['rot_speed'] = X[h,DFSM.gen_speed_ind]*rpm2RadSec*gen_speed_scaling/param['WE_GearboxRatio'] # rotor speed + turbine_state['Yaw_fromNorth'] = 0 # yaw + turbine_state['Y_MeasErr'] = 0 + + if not(DFSM.FA_Acc_ind_s == None): + turbine_state['FA_Acc'] = X[h,DFSM.FA_Acc_ind_s] # tower-top acceleration if it is modelled as a state + + elif not(DFSM.FA_Acc_ind_o == None): + turbine_state['FA_Acc'] = Y[h,DFSM.FA_Acc_ind_o] # tower top acceleration if it is modelled as an output + + if not(DFSM.NacIMU_FA_Acc_ind_s == None): + turbine_state['NacIMU_FA_Acc'] = X[h,DFSM.NacIMU_FA_Acc_ind_s]*np.deg2rad(1) + + elif not(DFSM.NacIMU_FA_Acc_ind_o == None): + turbine_state['NacIMU_FA_Acc'] = Y[h,DFSM.NacIMU_FA_Acc_ind_o]*np.deg2rad(1) + + # call ROSCO to get control values + gen_torque, bld_pitch, nac_yawrate = param['controller_interface'].call_controller(turbine_state) + + # convert to right units + gen_torque = gen_torque/KWatt2Watt + bld_pitch = np.rad2deg(bld_pitch) + + # store + U[h,DFSM.wind_speed_ind] = WS[h] + U[h,DFSM.gen_torque_ind] = gen_torque + U[h,DFSM.blade_pitch_ind] = bld_pitch + + if not(wave_fun == None): + U[h,DFSM.wave_elev_ind] = WE[h] + + # evaluate the derivative function + inputs = np.hstack([U[h,:],X[h,:]]) + f_step = evaluate_dfsm(DFSM,inputs,'deriv') + + F[h,:] = f_step + + return T,X,U,Y \ No newline at end of file diff --git a/weis/dfsm/simulation_details.py b/weis/dfsm/simulation_details.py new file mode 100755 index 000000000..ba5a06da7 --- /dev/null +++ b/weis/dfsm/simulation_details.py @@ -0,0 +1,331 @@ +import os +import numpy as np +from pCrunch.io import load_FAST_out +from scipy.interpolate import CubicSpline +from scipy.signal import filtfilt +import time as timer +import pickle + + +class SimulationDetails: + + def __init__(self,OF_output_files,reqd_states,reqd_controls,reqd_outputs,scale_args = {} + ,filter_args = {},add_dx2 = True,tmin = 0,tmax = None,linear_model_file = None,region = None,OF_file_type = 'outb'): + + # initialize + self.OF_output_files = OF_output_files + self.OF_file_type = OF_file_type + self.reqd_states = reqd_states + self.reqd_controls = reqd_controls + self.reqd_outputs = reqd_outputs + self.scale_args = scale_args + self.filter_args = filter_args + self.add_dx2 = add_dx2 + self.tmin = tmin + self.tmax = tmax + self.linear_model_file = linear_model_file + self.region = region + + # get number of simulations + self.n_sim = len(OF_output_files) + + # get number of states,inputs and outputs + self.n_states = len(reqd_states) + self.n_controls = len(reqd_controls) + self.n_outputs = len(reqd_outputs) + + # number of inputs and outputs for the DFSM model + + # if we add the second derivatives + if add_dx2: + self.n_states = self.n_states*2 + + # model inputs + self.n_model_inputs = self.n_states + self.n_controls + self.n_deriv = self.n_states + + def scale_quantities(self,states,controls,outputs): + + # extract scale arguments + scale_args = self.scale_args + + # scale states + if 'state_scaling_factor' in scale_args.keys(): + states = states/scale_args['state_scaling_factor'] + + # scale controls + if 'control_scaling_factor' in scale_args.keys(): + controls = controls/scale_args['control_scaling_factor'] + + # scale outputs + if self.n_outputs > 0: + if 'output_scaling_factor' in scale_args.keys(): + outputs = outputs/scale_args['output_scaling_factor'] + + return states,controls,outputs + + def filter_signal(self,t_f,time,signal): + + ''' + Function to filter the given signal + ''' + + dt = time[1]-time[0] + nb = int(np.floor(t_f/dt)) + b = np.ones((nb,))/nb;a = 1 + signal = filtfilt(b,a,signal,axis = 0) + + return signal + + def notch_filter(self,omega,time,signal): + + nt = len(time) + dt = time[1]-time[0] + BetaDen = 0.25 + BetaNum = 0 + signal_filt = np.zeros(nt) + + K = 2/dt + b2 = (K**2 + 2*omega*BetaNum*K + omega**2)/(K**2+2*omega*BetaDen*K+omega**2) + b1 = (2*omega**2 - 2*K**2)/(K**2 + 2*omega*BetaDen*K + omega**2) + b0 = (K**2 - 2*omega*BetaNum*K + omega**2)/(K**2 + 2*omega*BetaDen*K + omega**2) + a1 = (2*omega**2 - 2*K**2)/(K**2 + 2*omega*BetaDen*K + omega**2) + a0 = (K**2 - 2*omega*BetaDen*K + omega**2)/(K**2 + 2*omega*BetaDen*K + omega**2) + + for i_ in range(nt): + + # first iteration + if i_ == 0: + + OutputSignalLast1 = signal[i_] + OutputSignalLast2 = signal[i_] + + InputSignalLast1 = signal[i_] + InputSignalLast2 = signal[i_] + + signal_filt[i_] = b2*signal[i_] + b1*InputSignalLast1 + b0*InputSignalLast2 - a1*OutputSignalLast1 - a0*OutputSignalLast2 + + # update + InputSignalLast2 = InputSignalLast1 + InputSignalLast1 = signal[i_] + + OutputSignalLast2 = OutputSignalLast1 + OutputSignalLast1 = signal_filt[i_] + + return signal_filt + + def filter_quantities(self,time,states,controls,outputs): + + # extract filter arguments + filter_args = self.filter_args + + if 'state_filter_flag' in filter_args.keys(): + for idx,flag in enumerate(filter_args['state_filter_flag']): + if flag: + + for ifilt,filt_type in enumerate(filter_args['state_filter_type'][idx]): + + if filt_type == 'filtfilt': + t_f = filter_args['state_filter_tf'][idx][ifilt] + states[:,idx] = self.filter_signal(t_f,time,states[:,idx]) + + elif ifilt == 'notch': + + corner_freq = filter_args['state_filter_tf'][idx][ifilt] + states[:,idx] = self.notch_filter(corner_freq, time, states[:,idx]) + + if 'control_filter_flag' in filter_args.keys(): + for idx,flag in enumerate(filter_args['control_filter_flag']): + if flag: + t_f = filter_args['control_filter_tf'][idx] + controls[:,idx] = self.filter_signal(t_f,time,controls[:,idx]) + + if 'output_filter_flag' in filter_args.keys(): + if self.n_outputs > 0: + for idx,flag in enumerate(filter_args['output_filter_flag']): + if flag: + t_f = filter_args['output_filter_tf'][idx] + outputs[:,idx] = self.filter_signal(t_f,time,outputs[:,idx]) + + return states,controls,outputs + + + + def load_openfast_sim(self): + + FAST_sim = [] + + # loop through and extract openfast file + for sim_idx,file_name in enumerate(self.OF_output_files): + + # load output file + if self.OF_file_type == 'outb': + FAST_out = load_FAST_out(file_name)[0] + + elif self.OF_file_type == 'pkl': + with open(file_name,'rb') as handle: + FAST_out = pickle.load(handle) + + # extract time + time = FAST_out['Time'] + time = time - np.min(time) + + tmin = self.tmin + tmax = self.tmax + + t_ind = time>=tmin + + time = time[t_ind] + + + # number of points + nt = len(time) + + # extract states + states = np.zeros((nt,len(self.reqd_states))) + + for ix,state_name in enumerate(self.reqd_states): + states[:,ix] = FAST_out[state_name][t_ind] + + # extract controls + controls = np.zeros((nt,self.n_controls)) + + for iu,control_name in enumerate(self.reqd_controls): + controls[:,iu] = FAST_out[control_name][t_ind] + + # extract outputs + if self.n_outputs > 0: + + outputs = np.zeros((nt,self.n_outputs)) + + for iy,output_name in enumerate(self.reqd_outputs): + outputs[:,iy] = FAST_out[output_name][t_ind] + + else: + + outputs = [] + + # scale the inputs and outputs according to the options present in scale_args + if len(self.scale_args) > 0: + states,controls,outputs = self.scale_quantities(states,controls,outputs) + + if len(self.filter_args) > 0: + states,controls,outputs = self.filter_quantities(time,states,controls,outputs) + + # construct polynomial approximation + states_pp = CubicSpline(time,states) + dx_pp = states_pp.derivative + + # evaluate first time derivative + dx_pp1 = dx_pp(nu = 1) + + # evaluate second time derivative + dx_pp2 = dx_pp(nu = 2) + + # evaluate state derivatives + state_derivatives = dx_pp1(time) + + # evaluate the second time derivatives + state_derivatives2 = dx_pp2(time) + + if self.add_dx2: + + states = np.hstack([states,state_derivatives]) + state_derivatives = np.hstack([state_derivatives,state_derivatives2]) + + dx1_names = ['d' + s_name for s_name in self.reqd_states] + dx2_names = ['d' + dx for dx in dx1_names] + dx_names = dx1_names + dx2_names + + else: + + dx1_names = [] + dx_names = ['d' + s_name for s_name in self.reqd_states] + + #self.reqd_states = self.reqd_states + dx_names + + # find index of genspeed + try: + self.gen_speed_ind = self.reqd_states.index('GenSpeed') + + except ValueError: + self.gen_speed_ind = None + + # find index of FA_Acc + try: + self.FA_Acc_ind_s = self.reqd_states.index('YawBrTAxp') + + except ValueError: + self.FA_Acc_ind_s = None + + # find index of genspeed + try: + self.NacIMU_FA_Acc_ind_s = self.reqd_states.index('NcIMURAys') + + except ValueError: + self.NacIMU_FA_Acc_ind_s = None + + # find index of FA_Acc + try: + self.FA_Acc_ind_o = self.reqd_outputs.index('YawBrTAxp') + + except ValueError: + self.FA_Acc_ind_o = None + + # find index of genspeed + try: + self.NacIMU_FA_Acc_ind_o = self.reqd_outputs.index('NcIMURAys') + + except ValueError: + self.NacIMU_FA_Acc_ind_o = None + + # find the index of wind/current speed + try: + self.wind_speed_ind = self.reqd_controls.index('RtVAvgxh') + + except ValueError: + self.wind_speed_ind = None + + # find gen torque + try: + self.gen_torque_ind = self.reqd_controls.index('GenTq') + + except ValueError: + self.gen_torque_ind = None + + # find blade pitch index + try: + self.blade_pitch_ind = self.reqd_controls.index('BldPitch1') + + except ValueError: + self.blade_pitch_ind = None + + # find wave elev index + try: + self.wave_elev_ind = self.reqd_controls.index('Wave1Elev') + + except ValueError: + self.wave_elev_ind = None + + # initialize storage dict + sim_detail = {'sim_idx': sim_idx, + 'n_states': self.n_states, + 'states': states, + 'state_names': self.reqd_states + dx1_names, + 'n_controls': self.n_controls, + 'controls': controls, + 'control_names': self.reqd_controls, + 'n_model_inputs': self.n_model_inputs, + 'n_outputs': self.n_outputs, + 'outputs': outputs, + 'output_names': self.reqd_outputs, + 'n_deriv': self.n_states, + 'state_derivatives': state_derivatives, + 'dx_names':dx_names, + 'nt': len(time), + 'time': time + } + + FAST_sim.append(sim_detail) + + self.FAST_sim = FAST_sim diff --git a/weis/dfsm/test_dfsm.py b/weis/dfsm/test_dfsm.py new file mode 100755 index 000000000..2f4079757 --- /dev/null +++ b/weis/dfsm/test_dfsm.py @@ -0,0 +1,130 @@ +import numpy as np +from scipy.integrate import solve_ivp +from scipy.interpolate import CubicSpline,interp1d +from weis.dfsm.evaluate_dfsm import evaluate_dfsm +import time as timer + +def odefun(t,x,u_fun,DFSM): + + u = u_fun(t) + + # combine + inputs = np.hstack([u,x]) + + # evaluate dfsm + dx = evaluate_dfsm(DFSM,inputs,'deriv') + + return dx + + + +def test_dfsm(DFSM,test_cases,test_ind,simulation_flag = True,plot_flag = True, solver_options = None): + + n_cases = len(test_ind) + + U_list = [] + X_list = [] + dx_list = [] + Y_list = [] + simulation_time = np.zeros(n_cases) + + + + # loop through and evaluate test cases using the dfsm model + for idx,ind in enumerate(test_ind): + + # extract test case + case = test_cases[ind] + + # extract data + time = case['time'] + controls = case['controls'] + states = case['states'] + state_derivatives = case['state_derivatives'] + outputs = case ['outputs'] + n_outputs = case['n_outputs'] + + inputs = np.hstack([controls,states]) + + t0 = time[0];tf = time[-1] + tspan = [t0,tf] + + x0 = states[0,:] + + + + # create interpolating function for controls + u_pp = CubicSpline(time,controls) + u_fun = lambda t: u_pp(t) + + # solver method and options + if solver_options == None: + solver_options = {'method':'RK45','rtol':1e-9,'atol':1e-9} + + + if simulation_flag: + + t1 = timer.time() + sol = solve_ivp(odefun,tspan,x0,method=solver_options['method'],args = (u_fun,DFSM),rtol = solver_options['rtol'],atol = solver_options['atol']) + t2 = timer.time() + + # extract solution + + T = sol['t'] + states_dfsm = sol['y'] + states_dfsm = states_dfsm.T + + states = interp1d(time,states,axis = 0)(T) + controls = u_fun(T) + state_derivatives = interp1d(time,state_derivatives,axis = 0)(T) + + if n_outputs > 0: + outputs = interp1d(time,outputs,axis = 0)(T) + + inputs_dfsm = np.hstack([controls,states_dfsm]) + inputs = np.hstack([controls,states]) + time = T + + simulation_time[idx] = t2-t1 + + X_dict = {'time':time,'names':case['state_names'],'n':case['n_states'],'OpenFAST':states,'DFSM':states_dfsm} + U_dict = {'time': time, 'names': case['control_names'],'n': case['n_controls'],'OpenFAST':controls,'DFSM':controls} + + X_list.append(X_dict) + U_list.append(U_dict) + else: + + simulation_time = 0 + + # evaluate state derivatives predicted by the DFSM + fun_type = 'deriv' + dx_dfsm = evaluate_dfsm(DFSM,inputs,fun_type) + + dx_dict = {'time':time,'names':case['dx_names'],'n':case['n_deriv'],'OpenFAST':state_derivatives,'DFSM':dx_dfsm} + + dx_list.append(dx_dict) + + if n_outputs > 0 and simulation_flag: + + fun_type = 'outputs' + outputs_dfsm = evaluate_dfsm(DFSM,inputs_dfsm,fun_type) + + Y_dict = {'time':time,'names':case['output_names'],'n':case['n_outputs'],'OpenFAST':outputs,'DFSM':outputs_dfsm} + + Y_list.append(Y_dict) + + DFSM.simulation_time = np.mean(simulation_time) + + return DFSM,U_list,X_list,dx_list,Y_list + + + + + + + + + + + + diff --git a/weis/dfsm/wrapper_LTI.py b/weis/dfsm/wrapper_LTI.py new file mode 100755 index 000000000..bdb90811a --- /dev/null +++ b/weis/dfsm/wrapper_LTI.py @@ -0,0 +1,77 @@ +import numpy as np + +class wrapper_LTI(): + + def __init__(self,inputs = None,outputs = None,nstates = None,ncontrols = None): + self.inputs = inputs + self.outputs = outputs + + self.nstates = nstates + self.ncontrols = ncontrols + + + + def linear_model(self,x): + + nstates = self.nstates + ncontrols = self.ncontrols + + # reshape the parameters + x = np.reshape(x,[int(nstates/2),nstates+ncontrols],order = 'F') + + # extract elements corresponding to A and B matrices + B_par = x[:,:ncontrols] + A_par = x[:,ncontrols:ncontrols+nstates] + + # construct A and B matrices + A = np.hstack([np.zeros((int(nstates/2),int(nstates/2))),np.eye(int(nstates/2))]) + A = np.vstack([A,A_par]) + + B = np.vstack([np.zeros((int(nstates/2),ncontrols)),B_par]) + + + return A,B + + def objective_function(self,xdict): + + # extract inputs + inputs = self.inputs + outputs = self.outputs + + # extract parameters + x = xdict['xvars'] + + # reshape + A,B = self.linear_model(x) + + Aeig = np.linalg.eig(A) + Aeig = Aeig[0] + + LM = np.hstack([B,A]).T + + # prediction + dx_predicted = np.dot(inputs,LM) + + # evaluate error + error = outputs - dx_predicted + + # number of samples + N = len(error) + + # calculate loss + V = 1/N*(np.trace(np.dot(error.T,error))) + + # initialize + funcs = {} + + # objective + funcs['obj'] = V + #print(Aeig.real) + + # constraints + funcs['con'] = Aeig.real + + fail = False + + return funcs,fail + diff --git a/weis/glue_code/gc_LoadInputs.py b/weis/glue_code/gc_LoadInputs.py index 27ffceb79..b0e537487 100644 --- a/weis/glue_code/gc_LoadInputs.py +++ b/weis/glue_code/gc_LoadInputs.py @@ -7,7 +7,7 @@ from weis.aeroelasticse.FAST_reader import InputReader_OpenFAST from wisdem.glue_code.gc_LoadInputs import WindTurbineOntologyPython from weis.dlc_driver.dlc_generator import DLCGenerator -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI def update_options(options,override): for key, value in override.items(): @@ -84,7 +84,7 @@ def set_weis_data(self): # Openfast - if self.modeling_options['Level2']['flag'] or self.modeling_options['Level3']['flag']: + if self.modeling_options['Level2']['flag'] or self.modeling_options['DFSM']['flag'] or self.modeling_options['Level3']['flag']: fast = InputReader_OpenFAST() self.modeling_options['General']['openfast_configuration']['fst_vt'] = {} self.modeling_options['General']['openfast_configuration']['fst_vt']['outlist'] = fast.fst_vt['outlist'] @@ -169,8 +169,11 @@ def set_weis_data(self): # ROSCO self.modeling_options['ROSCO']['flag'] = (self.modeling_options['Level1']['flag'] or self.modeling_options['Level2']['flag'] or + self.modeling_options['DFSM']['flag'] or self.modeling_options['Level3']['flag']) + self.modeling_options['flags']['marine_hydro'] = self.wt_init['assembly']['marine_hydro'] + if self.modeling_options['ROSCO']['tuning_yaml'] != 'none': # default is empty # Make path absolute if not, relative to modeling options input if not osp.isabs(self.modeling_options['ROSCO']['tuning_yaml']): @@ -194,6 +197,7 @@ def set_weis_data(self): 'ws_cut_out':cut_out, 'MHK': self.wt_init['assembly']['marine_hydro'], }) + # Generate cases from user inputs for i_DLC in range(len(DLCs)): DLCopt = DLCs[i_DLC] diff --git a/weis/glue_code/gc_PoseOptimization.py b/weis/glue_code/gc_PoseOptimization.py index 53ac0daf0..26127f240 100644 --- a/weis/glue_code/gc_PoseOptimization.py +++ b/weis/glue_code/gc_PoseOptimization.py @@ -5,14 +5,14 @@ class PoseOptimizationWEIS(PoseOptimization): def __init__(self, wt_init, modeling_options, analysis_options): - self.level_flags = np.array([modeling_options[level]['flag'] for level in ['Level1','Level2','Level3']]) + self.level_flags = np.array([modeling_options[level]['flag'] for level in ['Level1','Level2','DFSM','Level3']]) # if sum(self.level_flags) > 1: # raise Exception('Only one level in WEIS can be enabled at the same time') super(PoseOptimizationWEIS, self).__init__(wt_init, modeling_options, analysis_options) # Set solve component for some optimization constraints, and merit figures (RAFT or openfast) - if modeling_options['Level3']['flag']: + if modeling_options['Level3']['flag'] or modeling_options['DFSM']['flag']: self.floating_solve_component = 'aeroelastic' elif modeling_options['Level1']['flag']: self.floating_solve_component = 'raft' @@ -28,7 +28,7 @@ def __init__(self, wt_init, modeling_options, analysis_options): def get_number_design_variables(self): # Determine the number of design variables - n_DV = super(PoseOptimizationWEIS, self).get_number_design_variables() + n_DV = 0 #super(PoseOptimizationWEIS, self).get_number_design_variables() n_add = 0 if self.opt['design_variables']['control']['servo']['pitch_control']['omega']['flag']: diff --git a/weis/glue_code/gc_RunTools.py b/weis/glue_code/gc_RunTools.py index 816a7132f..b6dc7c462 100644 --- a/weis/glue_code/gc_RunTools.py +++ b/weis/glue_code/gc_RunTools.py @@ -2,7 +2,7 @@ import matplotlib.pyplot as plt import openmdao.api as om import numpy as np -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI class Outputs_2_Screen(om.ExplicitComponent): # Class to print outputs on screen diff --git a/weis/glue_code/glue_code.py b/weis/glue_code/glue_code.py index 9df50f419..e0c8e8569 100644 --- a/weis/glue_code/glue_code.py +++ b/weis/glue_code/glue_code.py @@ -61,16 +61,16 @@ def setup(self): if os.path.split(modeling_options['ROSCO']['tuning_yaml'])[1].lower() != 'none': # default is none inps = load_rosco_yaml(modeling_options['ROSCO']['tuning_yaml']) # tuning yaml validated in here modeling_options['ROSCO'].update(inps['controller_params']) - + # Apply changes in modeling options, should have already been validated - modopts_no_defaults = load_yaml(modeling_options['fname_input_modeling']) - skip_options = ['tuning_yaml','DISCON'] # Options to skip loading, tuning_yaml path has been updated, don't overwrite - for option, value in modopts_no_defaults['ROSCO'].items(): - if option not in skip_options: - modeling_options['ROSCO'][option] = value - # Handle DISCON inputs separately - for option, value in modopts_no_defaults['ROSCO']['DISCON'].items(): - modeling_options['ROSCO']['DISCON'][option] = value + # modopts_no_defaults = load_yaml(modeling_options['fname_input_modeling']) + # skip_options = ['tuning_yaml','DISCON'] # Options to skip loading, tuning_yaml path has been updated, don't overwrite + # for option, value in modopts_no_defaults['ROSCO'].items(): + # if option not in skip_options: + # modeling_options['ROSCO'][option] = value + # # Handle DISCON inputs separately + # for option, value in modopts_no_defaults['ROSCO']['DISCON'].items(): + # modeling_options['ROSCO']['DISCON'][option] = value tune_rosco_ivc = om.IndepVarComp() if modeling_options['ROSCO']['linmodel_tuning']['type'] == 'robust': @@ -368,7 +368,7 @@ def setup(self): self.connect('TMDs.stiffness', 'aeroelastic.TMD_stiffness') self.connect('TMDs.damping', 'aeroelastic.TMD_damping') - if modeling_options['Level3']['flag'] or modeling_options['Level2']['flag']: + if modeling_options['Level3']['flag'] or modeling_options['DFSM']['flag'] or modeling_options['Level2']['flag']: self.add_subsystem('aeroelastic', FASTLoadCases(modeling_options = modeling_options, opt_options = opt_options)) self.add_subsystem('stall_check_of', NoStallConstraint(modeling_options = modeling_options)) diff --git a/weis/glue_code/mpi_tools.py b/weis/glue_code/mpi_tools.py new file mode 100644 index 000000000..e53fcc852 --- /dev/null +++ b/weis/glue_code/mpi_tools.py @@ -0,0 +1,139 @@ +import os +import sys + +from openmdao.utils.mpi import MPI + + +def under_mpirun(): + """Return True if we're being executed under mpirun.""" + # this is a bit of a hack, but there appears to be + # no consistent set of environment vars between MPI + # implementations. + for name in os.environ.keys(): + if ( + name == "OMPI_COMM_WORLD_RANK" + or name == "MPIEXEC_HOSTNAME" + or name.startswith("MPIR_") + or name.startswith("MPICH_") + or name.startswith("INTEL_ONEAPI_MPI_") + or name.startswith("I_MPI_") + ): + return True + return False + + +if under_mpirun(): + + def debug(*msg): # pragma: no cover + newmsg = ["%d: " % MPI.COMM_WORLD.rank] + list(msg) + for m in newmsg: + sys.stdout.write("%s " % m) + sys.stdout.write("\n") + sys.stdout.flush() + +else: + MPI = None + + +def map_comm_heirarchical(n_DV, n_OF, openmp=False): + """ + Heirarchical parallelization communicator mapping. Assumes a number of top level processes + equal to the number of design variables (x2 if central finite differencing is used), each + with its associated number of openfast simulations. + When openmp flag is turned on, the code spreads the openfast simulations across nodes to + lavereage the opnemp parallelization of OpenFAST. The cores that will run under openmp, are marked + in the color map as 1000000. The ones handling python and the DV are marked as 0, and + finally the master ones for each openfast run are marked with a 1. + """ + if openmp: + n_procs_per_node = 36 # Number of + num_procs = MPI.COMM_WORLD.Get_size() + n_nodes = num_procs / n_procs_per_node + + comm_map_down = {} + comm_map_up = {} + color_map = [1000000] * num_procs + + n_DV_per_node = n_DV / n_nodes + + # for m in range(n_DV_per_node): + for nn in range(int(n_nodes)): + for n_dv in range(int(n_DV_per_node)): + comm_map_down[nn * n_procs_per_node + n_dv] = [ + int(n_DV_per_node) + n_dv * n_OF + nn * (n_procs_per_node) + j for j in range(n_OF) + ] + + # This core handles python, so in the colormap the entry is 0 + color_map[nn * n_procs_per_node + n_dv] = int(0) + # These cores handles openfast, so in the colormap the entry is 1 + for k in comm_map_down[nn * n_procs_per_node + n_dv]: + color_map[k] = int(1) + + for j in comm_map_down[nn * n_procs_per_node + n_dv]: + comm_map_up[j] = nn * n_procs_per_node + n_dv + else: + N = n_DV + n_DV * n_OF + comm_map_down = {} + comm_map_up = {} + color_map = [0] * n_DV + + for i in range(n_DV): + comm_map_down[i] = [n_DV + j + i * n_OF for j in range(n_OF)] + color_map.extend([i + 1] * n_OF) + + for j in comm_map_down[i]: + comm_map_up[j] = i + + return comm_map_down, comm_map_up, color_map + + +def subprocessor_loop(comm_map_up): + """ + Subprocessors loop, waiting to receive a function and its arguements to evaluate. + Output of the function is returned. Loops until a stop signal is received + + Input data format: + data[0] = function to be evaluated + data[1] = [list of arguments] + If the function to be evaluated does not fit this format, then a wrapper function + should be created and passed, that handles the setup, argument assignment, etc + for the actual function. + + Stop sigal: + data[0] = False + """ + # comm = impl.world_comm() + rank = MPI.COMM_WORLD.Get_rank() + rank_target = comm_map_up[rank] + + keep_running = True + while keep_running: + data = MPI.COMM_WORLD.recv(source=(rank_target), tag=0) + if data[0] == False: + break + else: + func_execution = data[0] + args = data[1] + output = func_execution(args) + MPI.COMM_WORLD.send(output, dest=(rank_target), tag=1) + + +def subprocessor_stop(comm_map_down): + """ + Send stop signal to subprocessors + """ + # comm = MPI.COMM_WORLD + for rank in comm_map_down.keys(): + subranks = comm_map_down[rank] + for subrank_i in subranks: + MPI.COMM_WORLD.send([False], dest=subrank_i, tag=0) + print("All MPI subranks closed.") + + +if __name__ == "__main__": + + ( + _, + _, + _, + ) = map_comm_heirarchical(2, 4) diff --git a/weis/glue_code/runWEIS.py b/weis/glue_code/runWEIS.py index ec1b59cf0..8046c968e 100644 --- a/weis/glue_code/runWEIS.py +++ b/weis/glue_code/runWEIS.py @@ -5,7 +5,7 @@ from wisdem.glue_code.gc_WT_InitModel import yaml2openmdao from weis.glue_code.gc_PoseOptimization import PoseOptimizationWEIS from weis.glue_code.glue_code import WindPark -from wisdem.commonse.mpi_tools import MPI +from openmdao.utils.mpi import MPI from wisdem.commonse import fileIO from weis.glue_code.gc_ROSCOInputs import assign_ROSCO_values from weis.control.tmd import assign_TMD_values @@ -16,7 +16,8 @@ evolutionary_methods = ['DE', 'NSGA2'] if MPI: - from wisdem.commonse.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop + from weis.glue_code.mpi_tools import map_comm_heirarchical, subprocessor_loop, subprocessor_stop + def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry_override=None, modeling_override=None, analysis_override=None): # Load all yaml inputs and validate (also fills in defaults) @@ -77,6 +78,16 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry n_DV = max([n_DV, 1]) max_parallel_OF_runs = max([int(np.floor((max_cores - n_DV) / n_DV)), 1]) n_OF_runs_parallel = min([int(n_OF_runs), max_parallel_OF_runs]) + + elif modeling_options['DFSM']['flag']: + + # Always set n_DV as 1 + n_DV = 1 + n_FD = 1 + n_OF_runs = modeling_options['DLC_driver']['n_cases'] + n_DV = max([n_DV, 1]) + max_parallel_OF_runs = max([int(np.floor((max_cores - n_DV) / n_DV)), 1]) + n_OF_runs_parallel = min([int(n_OF_runs), max_parallel_OF_runs]) else: # If OpenFAST is not called, the number of parallel calls to compute the FDs is just equal to the minimum of cores available and DV n_FD = min([max_cores, n_DV]) @@ -86,7 +97,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry n_FD = max_cores # Define the color map for the cores (how these are distributed between finite differencing and openfast runs) - if opt_options['driver']['design_of_experiments']['flag']: + if opt_options['driver']['design_of_experiments']['flag'] and not(modeling_options['DFSM']['flag']): n_FD = MPI.COMM_WORLD.Get_size() n_OF_runs_parallel = 1 rank = MPI.COMM_WORLD.Get_rank() @@ -102,6 +113,7 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry olaf = False comm_map_down, comm_map_up, color_map = map_comm_heirarchical(n_FD, n_OF_runs_parallel, openmp=olaf) rank = MPI.COMM_WORLD.Get_rank() + if rank < len(color_map): try: color_i = color_map[rank] @@ -110,7 +122,6 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry else: color_i = max(color_map) + 1 comm_i = MPI.COMM_WORLD.Split(color_i, 1) - else: color_i = 0 rank = 0 @@ -118,15 +129,16 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry # make the folder_output relative to the input, if it's a relative path analysis_input_dir = os.path.dirname(opt_options['fname_input_analysis']) opt_options['general']['folder_output'] = os.path.join(analysis_input_dir,opt_options['general']['folder_output']) - + folder_output = opt_options['general']['folder_output'] if rank == 0 and not os.path.isdir(folder_output): os.makedirs(folder_output,exist_ok=True) if color_i == 0: # the top layer of cores enters, the others sit and wait to run openfast simulations # if MPI and opt_options['driver']['optimization']['flag']: + if MPI: - if modeling_options['Level3']['flag'] or modeling_options['Level2']['flag']: + if modeling_options['Level3']['flag'] or modeling_options['Level2']['flag'] or modeling_options['DFSM']['flag']: # Parallel settings for OpenFAST modeling_options['General']['openfast_configuration']['mpi_run'] = True modeling_options['General']['openfast_configuration']['mpi_comm_map_down'] = comm_map_down @@ -134,9 +146,9 @@ def run_weis(fname_wt_input, fname_modeling_options, fname_opt_options, geometry modeling_options['General']['openfast_configuration']['cores'] = 1 else: modeling_options['General']['openfast_configuration']['cores'] = n_OF_runs_parallel - + # Parallel settings for OpenMDAO - if opt_options['driver']['design_of_experiments']['flag']: + if opt_options['driver']['design_of_experiments']['flag'] and not(modeling_options['DFSM']['flag']): wt_opt = om.Problem(model=WindPark(modeling_options = modeling_options, opt_options = opt_options), reports=False) else: wt_opt = om.Problem(model=om.Group(num_par_fd=n_FD), comm=comm_i, reports=False) @@ -256,8 +268,8 @@ def convert(value): fileIO.save_data(froot_out, wt_opt) if MPI and \ - (modeling_options['Level3']['flag'] or modeling_options['Level2']['flag']) and \ - (not opt_options['driver']['design_of_experiments']['flag']) and \ + (modeling_options['Level3']['flag'] or modeling_options['Level2']['flag'] or modeling_options['DFSM']['flag']) and \ + (opt_options['driver']['design_of_experiments']['flag']) and \ color_i < 1000000: # subprocessor ranks spin, waiting for FAST simulations to run. # Only true for cores actually in use, not the ones supporting openfast openmp (marked as color_i = 1000000) diff --git a/weis/inputs/modeling_schema.yaml b/weis/inputs/modeling_schema.yaml index 2b7ba71d3..3e534653b 100644 --- a/weis/inputs/modeling_schema.yaml +++ b/weis/inputs/modeling_schema.yaml @@ -2042,10 +2042,10 @@ properties: unit: s description: Analysis time for wave radiation kernel calculations (sec) [only used when PotMod=1; determines RdtnDOmega=Pi/RdtnTMax in the cosine transform; MAKE SURE THIS IS LONG ENOUGH FOR THE RADIATION IMPULSE RESPONSE FUNCTIONS TO DECAY TO NEAR-ZERO FOR THE GIVEN PLATFORM!] RdtnDT: - type: [number,string] + type: number minimum: 0.0 maximum: 1e3 - default: DEFAULT + default: 0.0125 unit: s description: Time step for wave radiation kernel calculations, use 0.0 for default (sec) [only used when PotMod=1; DT<=RdtnDT<=0.1 recommended; determines RdtnOmegaMax=Pi/RdtnDT in the cosine transform] MnDrift: @@ -2377,17 +2377,6 @@ properties: minimum: 0.0 maximum: 1.0 description: Threshold for IC convergence (-) - NumSegs: - type: [number, array] - unit: none - items: - type: number - minimum: 10 - maximum: 100 - default: 20 - minimum: 10 - maximum: 100 - description: Number of segments to use in mooring line modeling. Single number or array with the same length as the number of lines. ServoDyn: &ofservodyn type: object default: {} @@ -3047,6 +3036,253 @@ properties: #MoorDyn: *ofmoordyn #ServoDyn: *ofservodyn #outlist: *ofoutlist + DFSM: + type: object + default: {} + description: Scehma that describes the modeling options used for the DFSM model + properties: + flag: + type: boolean + default: False + description: Flag to enable DFSM model for simulation/optimal control + general_options: + type: object + default: {} + description: general options needed for DFSM + properties: + dfsm_file: + type: string + description: Name of the pickle file that contains the DFSM + save_results: + type: boolean + default: False + description: Flag to save the results from DFSM + modeling_data_path: + type: string + description: Name of the folder that contains the .outb/pickle files used to construct the DFSM model. + usecase: + type: string + default: closed-loop-simulation + enum: ['closed-loop-simulation','open-loop-optimal-control'] + description: Use case for the DFSM model. Options are closed-loop simulation, where the DFSM is linked to the ROSCO controller to run simulation, and open-loop-optimal-control, where an optimal-control problem is formulated and solved. + run_dir: + type: string + default: none + description: Path to place FAST output files (e.g. /home/user/myturbines/output) + model_options: + type: object + default: {} + properties: + ode_method: + type: string + default: RK4 + description: ode method used to solve the closed-loop simulation + interp_type: + type: string + default: linear + description: interpolation method used to setup the LPV problem + run_dir: + type: string + default: none + description: Path to place FAST output files (e.g. /home/user/myturbines/output) + reqd_states: + type: array + description: List of the states to be used in the DFSM model. The names of the quantities must be the same as OpenFAST names + default: ['GenSpeed'] + items: + type: string + uniqueItems: True + reqd_controls: + type: array + description: List of the controls to be used in the DFSM model. The names of the quantities must be the same as OpenFAST names + default: ['RtVAvgxh'] + items: + type: string + uniqueItems: True + reqd_outputs: + type: array + description: List of the controls to be used in the DFSM model. The names of the quantities must be the same as OpenFAST names + default: ['GenPwr'] + items: + type: string + uniqueItems: True + state_props: &sigprops + type: object + default: {} + properties: + units: + type: array + description: units for the state quantities (will be automated in the future) + default: ['rpm'] + items: + type: string + key_freq_name: + type: array + description: name of the key frequencis to be plotted in PSD + default: [['2P']] + items: + type: array + key_freq_val: + type: array + description: name of the key frequencis to be plotted in PSD + default: [[0.39]] + items: + type: array + output_props: *sigprops + tmin: + type: number + description: minimum time + default: 0 + tmax: + type: number + description: maximum time + default: 10000 + add_dx2: + type: boolean + default: True + description: Flag to include the second derivatives of the states + linear_model_file: + type: string + default: none + description: Name of the matfile that has the linear model + region: + type: string + default: none + scale_args: + type: object + default: {} + description: Arguments to scale the state, control and output quantities + properties: + state_scaling_factor: + type: array + default: [1] + # control_scaling_factor: + # type: array + # default: [1] + # output_scaling_factor: + # type: array + # default: [1] + filter_args: + type: object + default: {} + description: Arguments to filter the state, control and output quantities + properties: + state_filter_flag: + type: array + default: [False] + state_filter_type: + type: array + default: [filtfilt] + state_filter_tf: + type: array + default: [0.1] + construction_options: + type: object + default: {} + properties: + L_type: + type: string + description: Type of the linear model to be used in the multifidelity DFSM model + default: LTI + enum: [None,LTI,LPV] + LPV_options: + type: object + default: {} + description: Meta options to setup the LPV model + properties: + v_min: + type: number + description: The lower limit of the wind/current speed for the LPV range + default: 1.85 + v_max: + type: number + description: The upper limit of the wind/current speed for the LPV range + default: 2.15 + nv: + type: integer + description: Number of wind/current speeds between v_min and v_max for which the linear models must be constructed + default: 50 + n_samples: + type: number + default: 50 + description: number of samples used to construct the corrective function + sampling_method: + type: string + default: KM + description: sampling method used to extract samples + train_split: + type: number + default: 0.8 + description: fraction of the samples used for constructing surrogate models + N_type: + type: string + description: Type of nonlinear model to be used in the multifidelity DFSM model + default: GPR + enum: [None,GPR,NN] + GPR_options: + url: https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html + type: object + default: {} + description: Options used to construct the GPR based nonlinear function + properties: + kernel: + type: string + description: The kernel used for the GPR model + default: RBF + enum: [RBF,matern,quad,exp-sine,dot] + length_scale: + type: number + default: 1 + description: Length scale for the kernel + lscale_bounds: + type: array + default: [1e-5,1e5] + description: bounds on the length scale + items: + type: number + n_restarts_optimizer: + type: integer + description: Number of times the hyperparameter-optimizer is started for GPR + default: 5 + maximum: 100 + minimum: 1 + random_state: + type: integer + description: Random state to be set for constructing the GPR model + default: 34534 + maximum: 1000000 + minimum: 0 + NN_options: + url: https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html + type: object + default: {} + description: Options used to construct the NN based nonlinear function + properties: + hidden_layer_size: + type: array + description: size/shape of the hidden layer used in the NN network + default: [50,10] + items: + type: integer + max_iter: + type: integer + description: maximum iterations of the solver used to fit the NN + default: 300 + activation: + type: string + description: The type of activation function used in the NN model + default: tanh + enum: [identity, logistic, tanh, relu] + solver: + type: string + description: The type of solver used in the finding th hyper parameters for the NN model + default: adam + enum: [lbfgs, sgd, adam] + tol: + type: number + description: Tolerence for the solver + default: 1e-5 + DLC_driver: type: object default: {}