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Merge pull request MaCFP#467 from rmcdermo/master
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FM Burner: update NIST results
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rmcdermo authored Sep 29, 2023
2 parents 7e81ded + 4682bd2 commit d54f23c
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Exp_Filename ,Exp_Header_Row,Exp_Data_Row,Exp_x_Col_Name,Exp_y_Col_Name,Exp_Data_Label,Exp_Marker_Style,Exp_Line_Style,Exp_Line_Width,Cmp_Filename ,Cmp_Header_Row,Cmp_Data_Row,Cmp_x_Col_Name,Cmp_y_Col_Name,Cmp_Data_Label,Cmp_Line_Style,Plot_Title ,Plot_x_Label ,Plot_y_Label,Plot_x_Min,Plot_x_Max,Plot_y_Min,Plot_y_Max,Plot_Flip_Axis,Plot_Legend_Location,Plot_Filename
Combustion_efficiency/FM_Zeng_2017_Eta_C2H4.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C2H4_2cm.csv,1 ,2 ,XO2 ,eta ,FDS (2 cm) ,-. ,Combustion Efficiency C$_2$H$_4$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C2H4.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C2H4.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C2H4_1cm.csv,1 ,2 ,XO2 ,eta ,FDS (1 cm) ,-- ,Combustion Efficiency C$_2$H$_4$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C2H4.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C2H4.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C2H4_5mm.csv,1 ,2 ,XO2 ,eta ,FDS (5 mm) ,- ,Combustion Efficiency C$_2$H$_4$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C2H4.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H8.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H8_2cm.csv,1 ,2 ,XO2 ,eta ,FDS (2 cm) ,-. ,Combustion Efficiency C$_3$H$_8$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H8.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H8.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H8_1cm.csv,1 ,2 ,XO2 ,eta ,FDS (1 cm) ,-- ,Combustion Efficiency C$_3$H$_8$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H8.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H8.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H8_5mm.csv,1 ,2 ,XO2 ,eta ,FDS (5 mm) ,- ,Combustion Efficiency C$_3$H$_8$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H8.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H6.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H6_2cm.csv,1 ,2 ,XO2 ,eta ,FDS (2 cm) ,-. ,Combustion Efficiency C$_3$H$_6$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H6.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H6.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H6_1cm.csv,1 ,2 ,XO2 ,eta ,FDS (1 cm) ,-- ,Combustion Efficiency C$_3$H$_6$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H6.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_C3H6.csv,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_C3H6_5mm.csv,1 ,2 ,XO2 ,eta ,FDS (5 mm) ,- ,Combustion Efficiency C$_3$H$_6$,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_C3H6.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_CH4.csv ,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_CH4_2cm.csv ,1 ,2 ,XO2 ,eta ,FDS (2 cm) ,-. ,Combustion Efficiency CH$_4$ ,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_CH4.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_CH4.csv ,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_CH4_1cm.csv ,1 ,2 ,XO2 ,eta ,FDS (1 cm) ,-- ,Combustion Efficiency CH$_4$ ,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_CH4.pdf
Combustion_efficiency/FM_Zeng_2017_Eta_CH4.csv ,1 ,2 ,X_O2 ,ETA_NORM ,Exp ,o , , ,FM_15cm_Burner_CH4_5mm.csv ,1 ,2 ,XO2 ,eta ,FDS (5 mm) ,- ,Combustion Efficiency CH$_4$ ,O$_2$ Volume Fraction,$\eta$ ,0.08 ,0.22 ,0 ,1.2 , ,lower right ,eta_CH4.pdf
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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',
macfp.dataplot(config_filename='FM_Burner_cmp_config_eta.csv',
institute='NIST',
revision='MaCFP-3, Tsukuba, 2023',
expdir='../../../Experimental_Data/',
cmpdir='./Output/',
cmpdir='./Output_Eta/',
pltdir='./Plots/',
close_figs=True,
verbose=True,
plot_list=['all'])

macfp.dataplot(config_filename='FM_Burner_cmp_config_C2CO_p6.csv',
institute='NIST',
revision='MaCFP-3, Tsukuba, 2023',
expdir='../../../Experimental_Data/',
cmpdir='./Output_C2CO_p6/',
pltdir='./Plots/',
close_figs=True,
verbose=True,
plot_list=['all'])

macfp.dataplot(config_filename='FM_Burner_cmp_config_C2CO_p9.csv',
institute='NIST',
revision='MaCFP-3, Tsukuba, 2023',
expdir='../../../Experimental_Data/',
cmpdir='./Output_C2CO_p9/',
pltdir='./Plots/',
close_figs=True,
verbose=True,
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import numpy as np
import pandas as pd

outdir = './Output/'
outdir = './Output_Eta/'

# create files with XO2 as independent column

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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:]})
# skip first few points to avoid hrr ramp up
df = pd.DataFrame({'XO2': XO2_FDS[4:],
'eta': ETA[4:],
'Chi_R': CHI_R[4:]})

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

outdir = './Output_C2CO_p6/'

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')

outdir = './Output_C2CO_p9/'

O2_name = ['20p9','19p0','16p8','15p2']
res_name = ['2cm','1cm','5mm']

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#!/usr/bin/env python3
# McDermott
# 25 Sep 2023
#
# Integrate vertical radiant power distribution to confirm radiant fraction

import pandas as pd

outdir = './Output4/'


XO2 = ["20p9", "19p0", "16p8", "15p2"]
Q_TOTAL = 15. # kW
for o2 in XO2:
R = pd.read_csv(outdir+'FM_15cm_Burner_C2H4_'+o2+'_5mm_line.csv', sep=',', header=1);
R = R.fillna(0.)
QR = 0.
z_last = 0.
for i in range(1,len(R)):
if i>1:
z_last = float(R["z"].values[i-1])

dz = (float(R["z"].values[i]) - z_last)*0.01
QR += float(R["RHRRPUL"].values[i])*dz

print("Radiant fraction "+o2+" = "+str(QR/Q_TOTAL))

# these values are used in the file Radiant_fraction3.csv
# In [1]: run Integrate_radiant_power.py
# Radiant fraction Air-mean = 0.32916522093333334
# Radiant fraction 19per-mean = 0.31399408879999996
# Radiant fraction 17per-mean = 0.2822435130666666
# Radiant fraction 15per-mean = 0.21575806480000004
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