Minor adjustments

examples/fishbone_nanowire.py → examples/scattering_map.py

@@ -1,24 +1,22 @@
-"""Config file to simulate a fishbone nanowire in Si at 4K"""
+"""Config file to simulate a scattering map in a fishbone nanowire in Si at 50 K"""
 
 # General parameters:
-OUTPUT_FOLDER_NAME             = 'Fishbone nanowire'
-NUMBER_OF_PHONONS              = 3000
-T                              = 4.0
+OUTPUT_FOLDER_NAME             = 'Scattering map in fishbone nanowire'
+NUMBER_OF_PHONONS              = 300
+T                              = 50.0
+OUTPUT_SCATTERING_MAP          = True
 
 # Multiprocessing
 NUMBER_OF_PROCESSES = 10
 
 # Simulation time parameters:
 TIMESTEP                       = 1.0e-12
-NUMBER_OF_TIMESTEPS            = 100000
-NUMBER_OF_VIRTUAL_TIMESTEPS    = NUMBER_OF_TIMESTEPS*4
-NUMBER_OF_STABILIZATION_TIMEFRAMES = 5
-NUMBER_OF_TIMEFRAMES = 8
+NUMBER_OF_TIMESTEPS            = 10000
 
 # System dimensions [m]:
-THICKNESS                      = 150e-9
+THICKNESS                      = 200e-9
 WIDTH                          = 500e-9
-LENGTH                         = 1500e-9
+LENGTH                         = 600e-9
 
 # Map & profiles parameters:
 pixel_size = 10e-9
@@ -52,7 +50,7 @@
 PERIOD_X                       = WIDTH
 PERIOD_Y                       = 300e-9
 NUMBER_OF_PERIODS_X = 2
-NUMBER_OF_PERIODS_Y = 6
+NUMBER_OF_PERIODS_Y = 3
 for i in range(NUMBER_OF_PERIODS_Y):
     for j in range(NUMBER_OF_PERIODS_X):
         x_coor = -(NUMBER_OF_PERIODS_X - 1) * PERIOD_X / 2 + j * PERIOD_X

freepaths/config.py

@@ -129,7 +129,6 @@ def check_parameter_validity(self):
         """Check if various parameters are valid"""
         if self.number_of_phonons < self.output_trajectories_of_first:
             self.output_trajectories_of_first = self.number_of_phonons
-            print("WARNING: Parameter OUTPUT_TRAJECTORIES_OF_FIRST exceeded NUMBER_OF_PHONONS.\n")
 
         if self.number_of_timeframes <= self.number_of_stabilization_timeframes:
             print("ERROR: Parameter NUMBER_OF_STABILIZATION_TIMEFRAMES exceeds or equal to NUMBER_OF_TIMEFRAMES.")

freepaths/main_mfp_sampling.py

@@ -120,5 +120,5 @@ def main(input_file):
     output_parameter_warnings()
 
     sys.stdout.write(f'\rSee the results in {Fore.GREEN}Results/{cf.output_folder_name}{Style.RESET_ALL}\n')
-    sys.stdout.write(f"\rThermal conductivity = {Fore.GREEN}{total_thermal_conductivity}{Style.RESET_ALL}\n W/m·K")
-    sys.stdout.write(f"\r{Fore.BLUE}Thank you for using FreePATHS{Style.RESET_ALL}\n")
+    sys.stdout.write(f"\rThermal conductivity = {Fore.GREEN}{total_thermal_conductivity:.5f}{Style.RESET_ALL} W/m·K\n")
+    sys.stdout.write(f"\r{Fore.BLUE}Thank you for using FreePATHS{Style.RESET_ALL}\n\n")

freepaths/maps.py

@@ -213,8 +213,9 @@ def calculate_thermal_conductivity(self):
             J_material = np.mean(self.material_heat_flux_profile_y[:, timeframe_number])
 
             # By definition, J = -K * grad(T), so:
-            self.effective_thermal_conductivity[timeframe_number, 1] = J_effective / grad_T
-            self.material_thermal_conductivity[timeframe_number, 1] = J_material / grad_T
+            if grad_T != 0:
+                self.effective_thermal_conductivity[timeframe_number, 1] = J_effective / grad_T
+                self.material_thermal_conductivity[timeframe_number, 1] = J_material / grad_T
 
             # Calculate single averaged value in the steady state range:
             self.av_effective_thermal_conductivity = np.mean(self.effective_thermal_conductivity[cf.number_of_stabilization_timeframes:, 1])

freepaths/output_plots.py

@@ -331,9 +331,9 @@ def plot_scattering_map():
     fig, ax = plt.subplots()
     filename = "Data/Scattering map.csv"
     spec_x, spec_y, diff_x, diff_y, int_x, int_y = np.genfromtxt(filename, unpack=True, delimiter=',', skip_header=1, encoding='utf-8')
-    ax.plot(np.trim_zeros(diff_x, 'b'), np.trim_zeros(diff_y, 'b'), 'o', color='b', markersize=0.1, alpha=0.3)
-    ax.plot(np.trim_zeros(spec_x, 'b'), np.trim_zeros(spec_y, 'b'), 'o', color='g', markersize=0.1, alpha=0.3)
-    ax.plot(np.trim_zeros(int_x, 'b'), np.trim_zeros(int_y, 'b'), 'o', color='r', markersize=0.1, alpha=0.3)
+    ax.plot(np.trim_zeros(diff_x, 'b')*1e6, np.trim_zeros(diff_y, 'b')*1e6, 'o', color='b', markersize=0.1, alpha=0.3)
+    ax.plot(np.trim_zeros(spec_x, 'b')*1e6, np.trim_zeros(spec_y, 'b')*1e6, 'o', color='g', markersize=0.1, alpha=0.3)
+    ax.plot(np.trim_zeros(int_x, 'b')*1e6, np.trim_zeros(int_y, 'b')*1e6, 'o', color='r', markersize=0.1, alpha=0.3)
     ax.set_xlabel('x (μm)')
     ax.set_ylabel('y (μm)')
     ax.legend(["Diffuse", "Specular", "Internal"])