Merge pull request #15532 from cxp484/FireX

FireX: Merge with firemodels/master

Author: cxp484

Manuals/FDS_Verification_Guide/FDS_Verification_Guide.tex

@@ -4123,7 +4123,7 @@ \section{Combustion Load Balancing (\ct{comb\_load\_bal})}
 \includegraphics[height=2.15in]{SCRIPT_FIGURES/EDC_load_bal_Methane_Smooke_TOT} &
 \includegraphics[height=2.15in]{SCRIPT_FIGURES/EDC_load_bal_Methane_Smooke_DEVC}
 \end{tabular*}
-\caption[Results of the \ct{comb\_load\_balance} test cases]{Combustion load balance case for mixing with detailed chemistry using Methane\_Smooke detailed chemical mechanism. The initial unmixed fraction is 1.0 and mixing time is 0.01 s.}
+\caption[Results of the \ct{comb\_load\_balance} test cases]{Combustion load balance case for mixing with detailed chemistry using Methane\_Smooke detailed chemical mechanism. The initial unmixed fraction is 0.98.}
 \label{fig:EDC_load_bal_methane_smooke}
 \end{figure}
 

Utilities/Matlab/FDS_verification_dataplot_inputs.csv

@@ -1,51 +1,43 @@
-"""
-McGrattan
-10-29-2019
-Crown_Fires.py
 
-Read the Crown_Fires *_cat_devc.csv files and determine the rate of spread based on the time history of front position.
-Write the results to a file that will be plotted via dataplot.m.
-"""
+# Read the Crown_Fires *_cat_devc.csv files and determine the rate of spread based on the time history of front position.
+# Write the results to a file that will be plotted via dataplot.py.
 
 import numpy as np
 import pandas as pd
 import os
 
-# Parameters
 outdir = '../../../out/Crown_Fires/'
 
-# Get only *_cat_devc.csv files (like MATLAB dir)
 file_list = [f for f in os.listdir(outdir) if f.endswith('_cat_devc.csv')]
-file_list.sort()  # optional, to keep consistent ordering with MATLAB
+file_list.sort() 
 
 wind_speed = []
 slope = []
 
 for fname in file_list:
     full_path = os.path.join(outdir, fname)
 
-    # Read CSV (skip 2 header rows like MATLAB importdata)
-    M = pd.read_csv(full_path, skiprows=2)
+    M = pd.read_csv(full_path, skiprows=2, header=None)
     M_data = M.to_numpy()
 
-    # Extract rows satisfying conditions (700<=col2<=900, 30<col1<300)
+    # Extract rows satisfying conditions (700<=x<=900, 30<Time<300)
     indices = np.where(
         (M_data[:, 1] >= 700) &
         (M_data[:, 1] <= 900) &
         (M_data[:, 0] > 30) &
         (M_data[:, 0] < 300)
     )[0]
 
-    # Mean wind speed (col3)
+    # Mean wind speed (U10)
     wind_speed.append(np.mean(M_data[indices, 2]))
 
-    # Polyfit slope for col2 vs col1
+    # Polyfit slope for x vs Time
     p = np.polyfit(M_data[indices, 0], M_data[indices, 1], 1)
     slope.append(p[0])
 
-# Write output file
 with open(os.path.join(outdir, 'ROS.csv'), 'w') as fid:
     fid.write('km/h,m/min\n')
     fid.write('U,ROS\n')
     for u, s in zip(wind_speed, slope):
         fid.write(f'{3.6*u:4.1f},{60*s:6.2f}\n')
+

Utilities/Python/fdsplotlib.py

@@ -1,4 +1,6 @@
 
+# For each experiment, make a contour plot of the extent of a single temperature contour at each time during the experiment
+
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -21,8 +23,6 @@
 single_level = [50]
 setpoint = [10000, 400, 399, 338, 240, 322, 390, 420, 360, 10000, 10000]
 
-# For each experiment, make a contour plot of the extent of a single temperature contour at each time during the experiment
-
 for k in range(11):  # Experiments
 
     fig = fdsplotlib.plot_to_fig(x_data=[5.5,5.5], y_data=[0,15], marker_style='k--',

Utilities/Python/scripts/Crown_Fires.py

@@ -1,4 +1,6 @@
 
+# Plot results of the Heskestad_Flame_Height cases against various experimental correlations
+
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd

Utilities/Python/scripts/FHWA_Tunnel.py

@@ -77,4 +77,3 @@
    fid.write('\\end{tabular}\n')
    fid.write('\\end{center}\n')
 
-print(f"\n? Successfully generated LaTeX table in {texname}")

Utilities/Python/scripts/Heskestad_Flame_Height_2.py

@@ -160,7 +160,6 @@
 # Create DataFrame for easy CSV writing with header
 df_expected = pd.DataFrame(burke_expected, columns=header1_expected)
 df_expected.to_csv(base_dir+'burke_schumann_expected.csv', index=False)
-print(f"Written expected data to burke_schumann_expected.csv")
 
 # 2. Write FDS Data
 burke_FDS = np.zeros((len(mix_frac[0, :]), 5)) # 15 rows, 5 columns
@@ -177,4 +176,3 @@
 # Create DataFrame for easy CSV writing with header
 df_FDS = pd.DataFrame(burke_FDS, columns=header1_FDS)
 df_FDS.to_csv(base_dir+'burke_schumann_FDS.csv', index=False)
-print(f"Written FDS data to burke_schumann_FDS.csv")

Utilities/Python/scripts/ashrae_7.py

@@ -70,7 +70,6 @@
 D1_final = pd.concat([H1_df, D1]).reset_index(drop=True)
 
 D1_final.to_csv(outdir+'propane_flame_deposition_cat_wall.csv', header=False, index=False)
-print(f"Written condensed phase aerosol data to propane_flame_deposition_cat_wall.csv")
 
 # List of files for gas phase aerosol (mass loss)
 gas_files = [
@@ -132,7 +131,6 @@
 
 # Write to CSV
 D2_final.to_csv(outdir+'propane_flame_deposition_cat_gas.csv', header=False, index=False)
-print(f"Written gas phase aerosol data to propane_flame_deposition_cat_gas.csv")
 
 # Create DataFrame D3
 D3 = pd.DataFrame({
@@ -156,4 +154,3 @@
 
 # Write to CSV
 D3_final.to_csv(outdir+ 'propane_flame_deposition_cat_total.csv', header=False, index=False)
-print(f"Written total aerosol data to propane_flame_deposition_cat_total.csv")

Utilities/Python/scripts/burke_schumann.py

@@ -49,20 +49,13 @@
 
 header_lp = tuple(h_mf_p.iloc[0,:]) + tuple(h_mf_l.iloc[0,1:])
 
-print(header_lp)
-
 data_combined_lp = pd.concat([mf_p, mf_l.iloc[:, 1:4]], axis=1)
 data_combined_lp.columns = header_lp
 
-print(data_combined_lp)
-
 header_ml =  tuple(h_mf_p_2.iloc[0,:]) +  tuple(h_mf_l_f.iloc[0,1:3]) +  tuple(h_mf_l_o.iloc[0,1:3])
-print(header_ml)
 data_combined_ml = pd.concat([mf_p_2, mf_l_f.iloc[:, 1:3], mf_l_o.iloc[:, 1:3]], axis=1)
 data_combined_ml.columns = header_ml
 
-print(data_combined_ml)
-
 output_file_1 = outdir+'methane_flame_lumpedprimitive.csv'
 
 # The MATLAB script uses a hardcoded units line for the final output.
@@ -76,7 +69,6 @@
 
 # Write to CSV without the pandas index or automatically generated header
 df_out_1.to_csv(output_file_1, header=False, index=False)
-print(f"Successfully wrote combined data to {output_file_1}")
 
 output_file_2 = outdir+'methane_flame_multilumped.csv'
 
@@ -88,4 +80,3 @@
 
 # Write to CSV without the pandas index or automatically generated header
 df_out_2.to_csv(output_file_2, header=False, index=False)
-print(f"Successfully wrote multi-lumped data to {output_file_2}")