Merge pull request #427 from fbraennstroem/master

UWuppertal - FM Burner

.github/workflows/Extinction.yml

@@ -122,4 +122,23 @@ jobs:
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
+          # Step 1: generate the plots
+      - name: UWuppertal Results
+        run: |
+          echo $GITHUB_WORKSPACE
+          cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/UWuppertal
+          python FM_Burner_plot_cmp.py
+
+      # Step 2: push the plots to the releases page
+      - name: Push UWuppertal Results to release
+        if: ( github.event_name == 'push' || github.event_name == 'workflow_dispatch' ) && github.repository_owner == 'MaCFP'
+        run: |
+          cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/UWuppertal
+          cp README.md ./Plots/.
+          zip Plots.zip -r Plots
+          mv Plots.zip FM_Burner_UWuppertal_Plots.zip
+          gh release upload SCRIPT_FIGURES FM_Burner_UWuppertal_Plots.zip --clobber
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
 

Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_plot_cmp.py

@@ -0,0 +1,31 @@
+#!/usr/bin/env python3
+# McDermott
+# Feb 2021
+
+# first, make sure the macfp module directory is in your path
+# if not, uncomment the lines below and replace <path to macfp-db>
+# with the path (absolute or relative) to your macfp-db repository
+
+import sys
+# sys.path.append('<path to macfp-db>/macfp-db/Utilities/')
+sys.path.append('../../../../../../macfp-db/Utilities/')
+# sys.path.append('/Users/fbraenns/09_TOOLs_Application/MaCFP/00_Repositories/macfp-db_FORK.github/Utilities/')
+
+import macfp
+import importlib
+importlib.reload(macfp) # use for development (while making changes to macfp.py)
+import matplotlib.pyplot as plt
+
+macfp.dataplot(config_filename='FM_Burner_cmp_config.csv',
+               institute='UWuppertal',
+               revision='MaCFP-3, Tsukuba, 2023',
+               expdir='../../../Experimental_Data/',
+               # cmpdir='./Output/',
+               # cmpdir='../NIST/Output/',
+               cmpdir='./Output/',
+               pltdir='./Plots/',
+               close_figs=True,
+               verbose=True,
+               plot_list=['all'])
+
+# plt.show()

Extinction/FM_Burner/Computational_Results/2023/UWuppertal/FM_Burner_post_process.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python3
+# McDermott
+# 16 Aug 2023
+#
+# Read and process FDS output files for FM_Burner cases to compute
+# Eta (combustion efficiency) and Chi_r (radiative fraction)
+
+import numpy as np
+import pandas as pd
+
+outdir = './Output/'
+
+# create files with XO2 as independent column
+
+fuel_name = ['C2H4','C3H6','C3H8','CH4']
+fuel_name = ['C2H4']
+res_name  = ['5mm']
+
+# for fuel in fuel_name:
+#    for res in res_name:
+
+#       # DEV = pd.read_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'_devc.csv', sep=',', header=1)
+#       HRR = pd.read_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'_hrr.csv', sep=',', header=1)
+
+#       # XO2_FDS  = DEV["XO2"].values[:].astype(float)
+#       Qdot_FDS = HRR["HRR"].values[:].astype(float)
+#       Qrad_FDS = HRR["Q_RADI"].values[:].astype(float)
+
+#       ETA = Qdot_FDS/np.max(Qdot_FDS)
+#       CHI_R = np.minimum(1.,np.maximum(0.,-Qrad_FDS)/np.maximum(0.001,Qdot_FDS))
+
+#       df = pd.DataFrame({'XO2': XO2_FDS[1:],
+#                          'eta': ETA[1:],
+#                          'Chi_R': CHI_R[1:]})
+
+#       df.to_csv(outdir+'FM_15cm_Burner_'+fuel+'_'+res+'.csv',index=False,float_format='%5.3f')
+
+# create files with Time as independent column for C2H4 fuel
+
+# O2_name  = ['20p9','19p0','16p8','15p2']
+O2_name  = ['20p9']
+res_name = ['5mm']
+
+variantL = ["v1_","v2_", "v3_","v4_","v5_","v6_","v7_","v8_","v9_","v10_"]
+for variant in variantL:
+   for O2 in O2_name:
+      for res in res_name:
+
+         HRR = pd.read_csv(outdir+ variant+'FM_15cm_Burner_C2H4_'+O2+'_'+res+'_hrr.csv', sep=',', header=1);
+
+         Time_FDS = HRR["Time"].values[:].astype(float)
+         Qdot_FDS = HRR["HRR"].values[:].astype(float)
+         Qrad_FDS = HRR["Q_RADI"].values[:].astype(float)
+
+         CHI_R = np.minimum(1.,np.maximum(0.,-Qrad_FDS)/np.maximum(0.001,Qdot_FDS))
+
+         df = pd.DataFrame({'Time': Time_FDS[1:],
+                            'Chi_r': CHI_R[1:]})
+
+         df.to_csv(outdir+variant+'FM_15cm_Burner_C2H4_'+O2+'_'+res+'_chir.csv',index=False,float_format='%5.3f')
+
+