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arr_tf.py
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arr_tf.py
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import sys
import numpy as np
from tensorflow import keras
#import matplotlib.pyplot as plt
import tensorflow as tf
import cantera as ct
from scipy import optimize as opt
import numpy as np
import itertools
def states_new_init(A):
R_new = ct.Reaction.fromCti('''reaction('O2 + 2 H2 => 2 H2O',
[%e, 0.0, 0.0])'''%(A))
#print(type(R_new))
#print(type(gas.reactions()))
gas2 = ct.Solution(thermo='IdealGas', kinetics='GasKinetics',
species=gas.species(), reactions=[R_new])
gas2.TPX = initial_state
r_new = ct.IdealGasConstPressureReactor(gas2, energy = 'off')
t_new = 0.0
states_new = ct.SolutionArray(gas2, extra=['t'])
sim_new = ct.ReactorNet([r_new])
tt = []
TT = []
for n in range(100):
'''t_new += 1.e-5
sim_new.advance(t_new)
#print(t_new)
tt.append(1000 * t_new*1000)
TT.append(r_new.T)'''
t_new += 1.e-5
sim_new.advance(t_new)
states_new.append(r_new.thermo.state, t=t_new*1e3)
return states_new, gas2
def obj_func(A,states_ref):
ret = 0.
states_new,gas2 = states_new_init(A)
for n in range(100):
ret += (states_new.X[n,gas2.species_index('H2')] - states_ref.X[n,gas2.species_index('H2')])**2/100
return ret
#return abs(a-b)
gas = ct.Solution('gri30.xml')
initial_state = 1500, ct.one_atm, 'H2:2,O2:1'
gas.TPX = 1500.0, ct.one_atm, 'H2:2,O2:1'
r = ct.IdealGasConstPressureReactor(gas,energy = 'off')
sim = ct.ReactorNet([r])
time = 0.0
states = ct.SolutionArray(gas, extra=['t'])
#
i = (j for j in range(0,10))
N = (j for j in range(1,11))
#
for n in range(100):
time += 1.e-5
sim.advance(time)
states.append(r.thermo.state, t=time*1e3)
#print('%10.3e %10.3f %10.3f %14.6e' % (sim.time, r.T,
# r.thermo.P, r.thermo.u))
#min = opt.minimize(lambda x: obj_func(x,states),1000.,method='Nelder-Mead')
#print(min)
min = opt.minimize(lambda x: obj_func(x,states),1000.,method='Nelder-Mead')
states_new,gas2 = states_new_init(min.x[0])
#model init
model = keras.Sequential()
model.add(keras.layers.Dense(60, activation='elu',input_shape=(1,)))
#model.add(keras.layers.Dense(1, activation='elu'))
model.add(keras.layers.Dense(60, activation='softmax'))
#model.add(keras.layers.Dense(1, activation='elu'))
model.add(keras.layers.Dense(60, activation='elu'))
#model.add(keras.layers.Dense(64, activation='sigmoid'))
#model.add(keras.layers.Dense(1, activation='sigmoid'))
model.add(keras.layers.Dense(1, kernel_regularizer=keras.regularizers.l1(0.01)))
#add parameters of model
model.compile(optimizer=tf.train.AdamOptimizer(0.001),
loss='MSLE',
metrics=['mae'])
#add dataset
arren_array = np.random.normal(min.x[0],10e2,200)
states_new_arr = []
gas_new = []
for i in range(len(arren_array)):
value_1, value_2 = states_new_init(arren_array[i])
states_new_arr.append(value_1)
gas_new.append(value_2)
#set_train_data
xtrain = np.array([np.array([i],dtype='float32') for i in arren_array[:100]])
ytrain = np.array([np.array([obj_func(i,states)],dtype='float32') for i in arren_array[:100]])
#set_test_data
xtest = np.array([np.array([i],dtype='float32') for i in arren_array[100:]])
ytest = np.array([np.array([obj_func(i,states)],dtype='float32') for i in arren_array[100:]])
#create_train_dataset
dataset = tf.data.Dataset.from_tensor_slices((xtrain, ytrain))
dataset = dataset.batch(64).repeat()
#create_test_dataset
val_dataset = tf.data.Dataset.from_tensor_slices((xtest, ytest))
val_dataset = val_dataset.batch(64).repeat()
#train_the_model
model.fit(dataset, epochs=300, steps_per_epoch=100,
validation_data=val_dataset,
validation_steps=10)
#predictions
arren_array_pred = np.random.normal(min.x[0],10,10)
states_new_arr_pred = []
gas_new_pred = []
for i in range(len(arren_array)):
value_1, value_2 = states_new_init(arren_array[i])
states_new_arr_pred.append(value_1)
gas_new_pred.append(value_2)
xpred = np.array([np.array([i],dtype='float32') for i in arren_array_pred])
predict_results = model.predict(xpred, steps=30)
predictions = list(itertools.islice(predict_results,len(xpred)))
#output predictions
for k,val in enumerate(predictions):
print(str(xpred[k]) + ' ' + str(val))
'''
C = plt.cm.winter(np.linspace(0,1,10))
plt.clf()
plt.subplot(2, 2, 1)
plt.plot(states.t, states.T,color='red')
plt.plot(states_new.t, states_new.T,color='green')
plt.xlabel('Time (ms)')
plt.ylabel('Temperature (K)')
plt.subplot(2, 2, 2)
plt.plot(states.t, states.X[:,gas.species_index('O2')],color='red')
plt.plot(states_new.t, states_new.X[:,gas2.species_index('O2')],color='green')
plt.xlabel('Time (ms)')
plt.ylabel('O2 Mole Fraction')
plt.subplot(2, 2, 3)
plt.plot(states.t, states.X[:,gas.species_index('H2O')],color='red')
plt.plot(states_new.t, states_new.X[:,gas2.species_index('H2O')],color='green')
plt.xlabel('Time (ms)')
plt.ylabel('H2O Mole Fraction')
plt.subplot(2, 2, 4)
plt.plot(states.t, states.X[:,gas.species_index('H2')],color='red')
plt.plot(states_new.t, states_new.X[:,gas2.species_index('H2')],color='green')
plt.xlabel('Time (ms)')
plt.ylabel('H2 Mole Fraction')
plt.tight_layout()
plt.show()
'''