FM_Burner: preliminary results NIST 2023

.github/workflows/Extinction.yml

@@ -4,17 +4,17 @@ name: Extinction
 
 # Controls when the workflow will run
 on:
-  # # Triggers the workflow on push or pull request events but only for the master branch
-  # push:
-  #   branches: [ master ]
-  #   paths:
-  #     - .github/Extinction.yml
-  #     - Extinction/**
-  # pull_request:
-  #   branches: [ master ]
-  #   paths:
-  #     - .github/Extinction.yml
-  #     - Extinction/**
+  # Triggers the workflow on push or pull request events but only for the master branch
+  push:
+    branches: [ master ]
+    paths:
+      - .github/Extinction.yml
+      - Extinction/**
+  pull_request:
+    branches: [ master ]
+    paths:
+      - .github/Extinction.yml
+      - Extinction/**
 
   # Allows you to run this workflow manually from the Actions tab
   workflow_dispatch:
@@ -40,23 +40,24 @@ jobs:
 
       # When submitting computational results, add your script to the list below (follow the example)
 
-      # # Step 1: generate the plots
-      # - name: NIST Preliminary Results
-      #   run: |
-      #     echo $GITHUB_WORKSPACE
-      #     cd $GITHUB_WORKSPACE/Liquid_Pool_Fires/NIST_Pool_Fires/Computational_Results/2023/NIST
-      #     python NIST_Pool_Fires_plot_cmp.py
-
-      # # Step 2: push the plots to the releases page
-      # - name: Push NIST Results to release
-      #   if: github.event_name == 'push' && github.repository_owner == 'MaCFP'
-      #   run: |
-      #     cd $GITHUB_WORKSPACE/Liquid_Pool_Fires/NIST_Pool_Fires/Computational_Results/2023/NIST/Preliminary_Results
-      #     zip Plots.zip -r Plots
-      #     mv Plots.zip NIST_Pool_Fires_NIST_Plots.zip
-      #     gh release upload SCRIPT_FIGURES NIST_Pool_Fires_NIST_Plots.zip --clobber
-      #   env:
-      #     GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+      # Step 1: generate the plots
+      - name: NIST Preliminary Results
+        run: |
+          echo $GITHUB_WORKSPACE
+          cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/NIST
+          python FM_Burner_post_process.py
+          python FM_Burner_plot_cmp.py
+
+      # Step 2: push the plots to the releases page
+      - name: Push NIST Results to release
+        if: github.event_name == 'push' && github.repository_owner == 'MaCFP'
+        run: |
+          cd $GITHUB_WORKSPACE/Extinction/FM_Burner/Computational_Results/2023/NIST/Preliminary_Results
+          zip Plots.zip -r Plots
+          mv Plots.zip FM_Burner_NIST_Plots.zip
+          gh release upload SCRIPT_FIGURES FM_Burner_NIST_Plots.zip --clobber
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
 
       # ADD YOUR INSTITUTION BELOW, COPY STEPS 1 AND 2, CHANGE INSTITUTE NAME (AFTER 2023 AND WITHIN ZIP FILENAME)
 

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

@@ -0,0 +1,28 @@
+#!/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/')
+
+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='NIST',
+               revision='MaCFP-3, Tsukuba, 2023',
+               expdir='../../../Experimental_Data/',
+               cmpdir='./Preliminary/',
+               pltdir='./Preliminary/Plots/',
+               close_figs=True,
+               verbose=True,
+               plot_list=['all'])
+
+# plt.show()

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

@@ -0,0 +1,58 @@
+#!/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 = './Preliminary_Results/'
+
+# create files with XO2 as independent column
+
+fuel_name = ['C2H4','C3H6','C3H8','CH4']
+res_name  = ['2cm'] #,'1cm','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']
+res_name = ['2cm'] #,'1cm','5mm']
+
+for O2 in O2_name:
+   for res in res_name:
+
+      HRR = pd.read_csv(outdir+'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+'FM_15cm_Burner_C2H4_'+O2+'_'+res+'_chir.csv',index=False,float_format='%5.3f')
+
+