add minimum area to conditions

examples/heat_sink_optimization.py

@@ -11,7 +11,7 @@
 
 config = hct.OptimizationParameters(
 
-    heat_sink_study_name="trial11",
+    heat_sink_study_name="trial1_area_1",
     heat_sink_optimization_directory=os.path.abspath("example_results"),
 
     height_c_list=[0.02, 0.08],
@@ -22,9 +22,10 @@
     thickness_fin_t_list=[1e-3, 5e-3],
     fan_list=fan_list,
     t_ambient=40,
+    area_min=0.02 * 0.1
 )
 
-hct.Optimization.start_proceed_study(config=config, number_trials=100)
+hct.Optimization.start_proceed_study(config=config, number_trials=10000)
 
 hct.global_plot_settings_font_latex()
 

hct/optimization.py

@@ -46,6 +46,9 @@ def objective(trial: optuna.Trial, config: OptimizationParameters) -> tuple:
         if geometry.fin_distance_s <= 0.1e-3:
             return float('nan'), float('nan')
 
+        if length_l * width_b < config.area_min:
+            return float('nan'), float('nan')
+
         try:
             volume_flow_v_dot, pressure = calc_volume_flow(fan_name, geometry, plot=False)
 
@@ -97,7 +100,7 @@ def start_proceed_study(config: OptimizationParameters, number_trials: int,
         config_on_disk_filepath = f"{config.heat_sink_optimization_directory}/{config.heat_sink_study_name}.pkl"
         if os.path.exists(config_on_disk_filepath):
             config_on_disk = Optimization.load_config(config_on_disk_filepath)
-            difference = deepdiff.DeepDiff(config, config_on_disk, ignore_order=True, significant_digits=10)
+            difference = deepdiff.DeepDiff(config_on_disk, config, ignore_order=True, significant_digits=10)
             if difference:
                 print("Configuration file has changed from previous simulation. Do you want to proceed?")
                 print(f"Difference: {difference}")
@@ -186,8 +189,6 @@ def df_plot_pareto_front(df: pd.DataFrame, figure_size: tuple):
         :param figure_size: figures size as a x/y-tuple in mm, e.g. (160, 80)
         :type figure_size: tuple
         """
-        print(df.head())
-
         names = df["number"].to_numpy()
         # plt.figure()
         fig, ax = plt.subplots(figsize=[x / 25.4 for x in figure_size] if figure_size is not None else None, dpi=80)

hct/thermal_dataclasses.py

@@ -60,6 +60,9 @@ class OptimizationParameters:
     # boundary conditions
     t_ambient: float
 
+    # constraints
+    area_min: float
+
 
 @dataclass
 class FanData: