def create_jacobian(T, P, y):
n_species = len(y)
dydt = np.zeros_like(y)
pyjacob.py_dydt(0, P, y, dydt)
#create a jacobian vector
jac = np.zeros(n_species * n_species)
#evaluate the Jacobian
pyjacob.py_eval_jacobian(0, P, y, jac)
jac = jac.reshape(n_species, n_species)
return jac
def jacobval(time, state, press):
"""Force the integrator to use the right arguments."""
# Need to get rid of N2 because PyJac doesn't compute it.
# new = state[:-1]
# print('Jacobian function called.')
a = len(state)
jacobian = np.zeros(a**2)
# Obtain the jacobian from pyJac
pyjacob.py_eval_jacobian(time, press, state, jacobian)
jacobian = np.reshape(jacobian, (a, a))
# Re-add the zeros back in
# jacobian = np.insert(jacobian, a, np.zeros(a), axis=1)
# jacobian = np.vstack((jacobian, np.zeros(a+1)))
return jacobian
def max_eig_gas(gas):
# input arg: gas cantera object
# output arg: eigenvalue vector, left and right eigenvector matrices
T = gas.T
P = gas.P
# #setup the state vector
y = np.zeros(gas.n_species)
y[0] = T
for i in range(1, gas.n_species):
if gas.species_name(i) != 'N2':
# print(gas.species_name(i))
y[i] = gas.Y[i]
# y[1:] = gas.Y[:-1]
# #create a dydt vector
dydt = np.zeros_like(y)
pyjacob.py_dydt(0, P, y, dydt)
#create a jacobian vector
jac = np.zeros(gas.n_species * gas.n_species)
#evaluate the Jacobian
pyjacob.py_eval_jacobian(0, P, y, jac)
jac = jac.reshape(gas.n_species, gas.n_species)
# Solve eigenvalue PB > D: eigenvalues
D, vl, vr = LA.eig(jac, left=True)
D = D.real
# vl=vl.real
# vr=vr.real
# introduced to get rid of zeros: careful!
# D = np.delete(D,np.where(D==0.0))
# cannot delete here, must be after (affects EI)
# return D, vl, vr
return np.amax(D)
# rearrange species in solution object
gas = ct.Solution(thermo='IdealGas', kinetics='GasKinetics',
species=specs[:last_idx] + specs[last_idx + 1:] + [specs[last_idx]],
reactions=gas.reactions())
#set the gas state
T = 1200
P = ct.one_atm
gas.TPY = T, P, "CH4:0.2, O2:2, N2:7.52"
# #setup the thermochemical state vector
y = np.zeros(gas.n_species)
y[0] = T
y[1:] = gas.Y[:-1] # all species mass fractions except the one belonging to last_spec
# create a dy/dt vector
dydt = np.zeros_like(y)
pyjacob.py_dydt(0, P, y, dydt)
#create a jacobian vector
jac = np.zeros(gas.n_species * gas.n_species)
#evaluate the Jacobian
pyjacob.py_eval_jacobian(0, P, y, jac)
jac = jac.reshape(gas.n_species,gas.n_species) # reshape into matrix for further calculations
print(jac)
print('test: successful')