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helper_functions.py
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helper_functions.py
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import cantera as ct
import numpy as np
from scipy.stats import binned_statistic_dd
from scipy.interpolate import griddata, RegularGridInterpolator, LinearNDInterpolator
gas = ct.Solution('./Cantera_red_mechs/mechs/Yao_nDodecane/nDodecane_sk54.xml')
# Need a species list because the 2D and 3D data is in this order while cantera species
# are in different order so need a mapping
specs=['aC3H5','C10H20','C12H24','C12H25O2','C12OOH','C2H2','C2H3','C2H3CHO','C2H4','C2H5','C2H6','C3H6','C4H7',
'C4H81','C5H10','C5H9','C6H12','C7H14','C8H16','C9H18','CH2','CH2*','CH2CHO','CH2O','CH3','CH3O','CH4',
'CO','CO2','H','H2','H2O','H2O2','HCO','HO2','N2','NC12H26','nC3H7','O','O2','O2C12H24OOH','OC12H23OOH','OH','pC4H9','PXC10H21','PXC12H25','PXC5H11',
'PXC6H13','PXC7H15','PXC8H17','PXC9H19','S3XC12H25','SXC12H25']
# Species order in nonpremixed flamelets generated from FlameMaster
specs_NP=['N2','H','O2','OH','O','H2','H2O','HO2','H2O2','CO2','CO','HCO','CH2O','CH2','CH3','C2H2','CH2*','CH3O','CH4','C2H4',
'C2H6','C2H5','C2H3','aC3H5','CH2CHO','C2H3CHO','C3H6','nC3H7','C4H7','C4H81','pC4H9','C5H9','C5H10','PXC5H11','C6H12','PXC6H13',
'C7H14','PXC7H15','C8H16','PXC8H17','C9H18','PXC9H19','C10H20','PXC10H21','C12H24','PXC12H25','S3XC12H25','SXC12H25',
'NC12H26','C12H25O2','C12OOH','O2C12H24OOH','OC12H23OOH']
def get_mean_molar_mass(molar_mass,data):
mean_mass = np.zeros((data.shape[0]))
mean_mass = 1.0/np.sum(data[:,0:molar_mass.shape[0]]/molar_mass[None,:],1)
#for i in range(molar_mass.shape[0]):
# mean_mass[i]=mean_mass[i]+np.sum(data[0:molar_mass.shape[0],i]*molar_mass)
return mean_mass
def get_data_2d(file):
f = h5py.File(file,'r')
dset = f['DATA'][:,:,3:54+3]
T = f['DATA'][:,:,2]
dset=np.delete(dset,1,axis=2)
dset = np.reshape(dset,(dset.shape[0]*dset.shape[1],53))
T = np.reshape(T,(T.shape[0]*T.shape[1],1))
data = dset[:,map_spec]
data = np.append(data,T,axis=1)
data = data[0:-1:2,:]
return data
def get_reac_2d(file):
f = h5py.File(file,'r')
dset = f['DATA'][:,:,65:119]
HRR = -f['DATA'][:,:,60]
dset=np.delete(dset,1,axis=2)
dset = np.reshape(dset,(dset.shape[0]*dset.shape[1],53))
HRR = np.reshape(HRR,(HRR.shape[0]*HRR.shape[1],1))
data = dset[:,map_spec]
data = np.append(data,HRR,axis=1)
data = data[0:-1:2,:]
return data
def get_atoms_conservation(data):
out = np.zeros((data.shape[0],4))
out = np.matmul((data[:,0:53]/molar_mass),aij*atom_mass)
return out
def get_cp(data):
cp_mass = np.zeros(data.shape[0])
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
cp_mass[i] = gas.cp_mass
return cp_mass
def get_enthalpy(data):
enth_mass3d_p = np.zeros(data.shape[0])
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
enth_mass3d_p[i] = gas.enthalpy_mass
return enth_mass3d_p
def get_viscosity(data):
visc = np.zeros(data.shape[0])
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
visc[i] = gas.viscosity
return visc
def get_conductivity(data):
cond = np.zeros(data.shape[0])
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
cond[i] = gas.thermal_conductivity
return cond
def get_reaction(data):
rr = np.zeros((data.shape[0],54))
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
q1 = ct.Quantity(gas)
rr[i,:]=q1.net_production_rates
specs_cantera=gas.species_names
specs_cantera.remove('AR')
rr = np.delete(rr,1,1)
map_spec=[]
for i in range(0,53):
for j in range(0,53):
if(specs[i]==specs_cantera[j]):
map_spec.append(j)
rr = rr[:,map_spec]
return rr
def get_diffusion(data):
diff = np.zeros((data.shape[0],54))
for i in range(0,data.shape[0]):
comp = dict(zip(specs,data[i,0:53]))
gas.TPY = data[i,53],6079500,comp
diff[i,:]=gas.mix_diff_coeffs_mass
specs_cantera=gas.species_names
specs_cantera.remove('AR')
diff = np.delete(diff,1,1)
map_spec=[]
for i in range(0,53):
for j in range(0,53):
if(specs[i]==specs_cantera[j]):
map_spec.append(j)
diff = diff[:,map_spec]
return diff
def opt_est(Xt,data,nbins):
cond_mean, _ , bins = binned_statistic_dd(Xt,data,bins=nbins,expand_binnumbers=True)
cond_mean[np.isnan(cond_mean)]=0
bins = bins-1
bins[bins==nbins]=nbins-1
pred = np.zeros(Xt.shape[0])
#for i in range(0,Xt.shape[0]):
pred=cond_mean[bins[0,:],bins[1,:],bins[2,:],bins[3,:],bins[4,:]]
return pred
def read_data(fname,nc):
data = np.fromfile(fname,dtype=np.single)
data = np.reshape(data,(int(data.size/nc),nc))
#HRR = data[:,0]
data = np.delete(data,0,1)
return data
def read_data_mem(fname,nc):
data = np.memmap(fname, dtype=np.single, mode='r')
data = np.reshape(data,(int(data.size/nc),nc))
#HRR = data[:,0]
data = np.delete(data,0,1)
return data
def read_reaction(fname):
data = np.fromfile(fname,dtype=np.single)
data = np.reshape(data,(int(data.size/56),56))
HRR = data[:,0]
data = np.delete(data,0,1)
data[:,53]=HRR
data = np.delete(data,54,1)
return data
def read_reaction_mem(fname):
data = np.memmap(fname, dtype=np.single, mode='r')
data = np.reshape(data,(int(data.size/56),56))
HRR = data[:,0]
data = np.delete(data,0,1)
data[:,53]=HRR
data = np.delete(data,54,1)
return data
def do_normalization(data,data2,which):
if(which=='range'):
datanorm = (data-np.mean(data2,0))/(np.max(data2,0)-np.min(data2,0))
return datanorm
elif(which=='std'):
datanorm = (data-np.mean(data2,0))/(np.std(data2,0))
return datanorm
elif(which=='level'):
datanorm = (data-np.mean(data2,0))/(np.mean(data2,0))
return datanorm
elif(which=='vast'):
datanorm = (data-np.mean(data2,0))/(np.std(data2,0))*np.mean(data2,0)
return datanorm
elif(which=='pareto'):
datanorm = (data-np.mean(data2,0))/np.sqrt(np.std(data2,0))
return datanorm
elif(which=='minmax'):
datanorm = (data-np.min(data2,0))/(np.max(data2,0)-np.min(data2,0))
return datanorm
elif(which=='none'):
return np.copy(data)
def do_inverse_norm(data,datanorm,which):
if(which=='range'):
data_inv = datanorm*(np.max(data,0)-np.min(data,0))+np.mean(data,0)
return data_inv
if(which=='std'):
data_inv = datanorm*(np.std(data,0))+np.mean(data,0)
return data_inv
if(which=='level'):
data_inv = datanorm*(np.mean(data,0))+np.mean(data,0)
return data_inv
if(which=='vast'):
data_inv = datanorm*(np.std(data,0))/np.mean(data,0)+np.mean(data,0)
return data_inv
if(which=='pareto'):
data_inv = datanorm*np.sqrt(np.std(data,0))+np.mean(data,0)
return data_inv
if(which=='minmax'):
data_inv = datanorm*(np.max(data,0)-np.min(data,0))+np.min(data,0)
return data_inv
def opt_est(Xt,data,nbins):
cond_mean, _ , bins = binned_statistic_dd(Xt,data,bins=nbins,expand_binnumbers=True)
cond_mean[np.isnan(cond_mean)]=0
bins = bins-1
bins[bins==nbins]=nbins-1
pred = np.zeros(Xt.shape[0])
pred=cond_mean[bins[0,:],bins[1,:],bins[2,:],bins[3,:],bins[4,:]]
return pred
def get_table_noholes(Xt,data,Xt3d,nbins):
grid = np.linspace(np.min(Xt,0),np.max(Xt,0),nbins)
xi,yi = np.meshgrid(grid[:,0],grid[:,1])
cond_mean = griddata((Xt[:,0],Xt[:,1]),data,(xi,yi),method='linear')
indices = np.argwhere(np.isnan(cond_mean))
cond_mean[indices[:,0],indices[:,1]] = griddata((Xt[:,0],Xt[:,1]),data,(xi[indices[:,0],indices[:,1]],yi[indices[:,0],indices[:,1]]),method='nearest')
cond_mean = np.transpose(cond_mean)
interp=RegularGridInterpolator((grid[:,0], grid[:,1]),cond_mean[0,:,:],method='linear',bounds_error=False,fill_value=0)
return interp(Xt3d)
def cond_mean(Xt,data,nbins):
cond_mean, _ , _ = binned_statistic_dd(Xt,data,bins=nbins,expand_binnumbers=True)
cond_mean[np.isnan(cond_mean)]=0
cond_mean = np.transpose(cond_mean)
return cond_mean
def do_CMA(data):
cmas = np.ones(data.shape[0])*5
idxs = np.logical_or(data[:,53] < 1120, data[:,3] > 0.05*max(data[:,3]))
cmas[idxs]=0
idxs = np.logical_and(np.logical_and(np.logical_and(data[:,53] >=1120, data[:,3] < 0.05*max(data[:,3])),data[:,42] < 0.05*max(data[:,42]))
,data[:,54]>0.046)
cmas[idxs]=1
idxs = np.logical_and(np.logical_and(np.logical_and(data[:,53] >=1120, data[:,3] < 0.05*max(data[:,3])),data[:,42] < 0.05*max(data[:,42]))
,data[:,54]<0.046)
cmas[idxs]=2
idxs = np.logical_and(np.logical_and(data[:,53] >=1120, data[:,3] < 0.05*max(data[:,3])),data[:,42] > 0.05*max(data[:,42]))
cmas[idxs]=3
return cmas