del_H_cat, del_S_cat)
#### Shipping
return h**o, cat, nu_homo_index, nu_cat_index
if __name__ == '__main__':
import constants
react_system = 'OCM_two_reaction.txt'
catalyst = 'La_Ce'
homo_basis = 'mole_fraction'
cat_basis = 'mole_fraction'
const, thermo_object = constants.fixed_parameters(react_system)
h**o, cat, \
homo_index, cat_index = instantiate(react_system, const, catalyst)
print(h**o.n_r)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
inlet_species = 'CH4'
inlet_ratio = 4
F_A_in = inlet_ratio / (inlet_ratio + 1)
F_B_in = 1 / (inlet_ratio + 1)
def bif_dia_solver(fixed_dict, bif_par_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, break_val,
step_change, max_val, options):
'''
This function solves for the bifurcation diagram.
fixed_dict= Dictionary of fixed variables and values.
bif_par_dict= Dictionary of Bifurcation variable and value.
react_system= Describes the reaction system chosen, e.g: 'OCM_three_reaction.txt'.
system= Whether the system is catalytic only, homogeneous only, or homogeneous-heterogeneous coupled.
inlet_species= The hydrocarbon species, as of now just hydrocarbon and O2 is taken as inlet.
break_val= break value of the bifurcation parameter.
step_change= step_change value of the bifurcation parameter, necessary in Pseudo-Arc Length Continuation.
max_val= maximum value of the bifurcation parameter.
options= Solver Options.
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Manipulation of input variables
all_dict = dict(fixed_dict, **bif_par_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
#### Initial guess
inlet_ratio = all_dict['inlet_ratio']
N2 = 0 # For the time being, we use pure O2
F_A_in = inlet_ratio / (N2 + inlet_ratio + 1)
F_B_in = 1 / (N2 + inlet_ratio + 1)
F_in = 1e-08 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
F_in[A_index] = F_A_in
F_in[B_index] = F_B_in
state_var_val = np.r_[F_in, F_in, all_dict['T_f_in'], all_dict['T_f_in'],
bif_par_val]
#### Generating the reaction data
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
#### Function name
func = bif_dia_func
jac = bif_dia_jacobian
if options['Testing']:
#### Just testing the function(s)
Ysol = func(state_var_val, bif_par_val, inlet_species, fixed_dict,
const, thermo_object, react_const, system, rate_basis,
rate_units, 0)
return Ysol
else:
if options['continuation'] == 'arc_length':
raise Exception(
'This is not the correct solver to solve the problem')
else:
#### Solving for the bifurcation diagram
iter_count = 0
_run = True
rand_count = 0
while (_run):
_run = False
#### First Intial Guess
while (True):
if (iter_count != 0):
rand_count += 1
print(rand_count)
if (rand_count == 1):
print(
'Working on generating the next initial guess')
### Guessing the initial point through random number generation
T_s = np.random.randint(T_bd[-3, -1], 3000)
T_f = np.random.randint(T_bd[-2, -1], T_s)
state_var_val = np.r_[1e-01 *
np.ones(2 * no_of_species), T_s,
T_s]
bif_par_val = break_val
# while(True):
# #### Generating the new initial guess after the break point
# print('The last solution point is:\n{}'.format(T_bd[:,-1]))
#
# state_var_str = (input('Enter an initial guess close to this first point '
# '[comma-separeted]: ')).split(',')
# state_var_list = []
# for elem in state_var_str:
# try:
# elem_float = float(elem)
# except ValueError:
# print('Enter a valid float-type data input')
# else:
# state_var_list.append(elem_float)
#
# if len(state_var_list) == (2*no_of_species + 2):
# state_var_val = np.array(state_var_list)
# #### Checking the validity of the input
# if (any(state_var_val < 0)) or (state_var_val[-1] < T_bd[-2, -1]):
# print('Enter a valid input')
# else:
# bif_par_val -= step_change
# break
### Solving with first initial guess
sol0 = root(func,
state_var_val,
args=(bif_par_val, inlet_species, fixed_dict,
const, thermo_object, react_const,
system, rate_basis, rate_units))
act_sol = sol0.x
#### Checking the validity of the solution
if (iter_count != 0):
delta = func(act_sol, bif_par_val, inlet_species,
fixed_dict, const, thermo_object,
react_const, system, rate_basis,
rate_units)
if any(act_sol < 0) or any(act_sol.imag != 0) or any(
abs(delta) > 1e-03):
print(
'Didn\'t get a perfect solution, trying again!!!'
)
elif (abs(act_sol[-1] - bif_par_val) <= 1):
print('Oops!!! Got the first point, trying again.')
# elif (abs(act_sol[-1] - T_bd[-2,-1]) <= 5):
# print('Got the previous point, trying again.')
else:
print('The last solution point is:\n{}'.format(
T_bd[:, -1]))
sol_check = input(
'Do you think this is the solution: \n{}'.
format(act_sol))
else:
sol_check = 'y'
if (sol_check == 'y'):
iter_count += 1
sol_check = 'n'
Y0 = np.r_[act_sol, bif_par_val]
try:
T_bd = np.c_[T_bd, Y0[:]]
except NameError:
T_bd = Y0[:]
finally:
break
print('We got the first point, moving on now!')
#### Second initial guess
bif_par_val += step_change
### Solving with second initial guess
sol0 = root(func,
state_var_val,
args=(bif_par_val, inlet_species, fixed_dict,
const, thermo_object, react_const, system,
rate_basis, rate_units))
act_sol = sol0.x
print('We got the second point too')
iter_count += 1
Y1 = np.r_[act_sol, bif_par_val]
T_bd = np.c_[T_bd, Y1[:]]
# print(T_bd)
#### Calculation of the suitable delta_s value
if bif_par_var == 'tau':
plot_flag = 'log'
delta_s = np.log10(Y1[-1] / Y0[-1])
else:
plot_flag = 'norm'
delta_s = np.linalg.norm(Y1 - Y0)
#### Continuation method
while (Y1[-1] <= max_val):
print(Y1[-1])
iter_count += 1
Y_guess = 2 * Y1 - Y0
delta_s = np.linalg.norm(Y1 - Y0)
sol2 = root(func,
Y_guess,
args=(Y1, inlet_species, fixed_dict, const,
thermo_object, react_const, system,
rate_basis, rate_units, delta_s))
Y2 = sol2.x
T_bd = np.c_[T_bd, Y2]
Y0[:] = Y1[:]
Y1[:] = Y2[:]
if (Y1[-1] < break_val):
print('Extinction point is lower than the '
'break point ({}).'.format(break_val))
while (True):
A = input(
'Do you want to start the calculation '
'with a different initial guess [y/n]?: ')
if (A.lower() == 'y') or (A.lower() == 'n'):
break
else:
print('Enter \'y\' for a yes and \'n\' for a '
'no, no other input will be considered')
if (A.lower() == 'y'):
_run = True
break
#### Plotting the figure
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_bd[-1, :], T_bd[-2, :])
else:
plt.plot(T_bd[-1, :], T_bd[-2, :])
plt.show
#### Storing data
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
filename = 'bif_dia'
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
react_filename, ext = os.path.splitext(react_system)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_bd, Y_in, species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_bd
def bif_set_solver(fixed_dict, bif_par_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, break_dict,
step_change, max_val, options):
'''
This function solves for the bifurcation set (or ignition-extinction locus)
The significance of different arguments are same as that of the bif_dia_solver.
'''
# Constant parameters involved with the system
const = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
# Manipulation of input variables
all_dict = dict(fixed_dict, **bif_par_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
# Generating the reaction data
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
# First Dataset
npzfile = _load_file(react_system, system, catalyst, all_dict)
T_bd_arr, Y_in_arr, species_ID_arr = npzfile.files
T_bd_0 = npzfile[T_bd_arr]
index_0 = ig_ext_point_calculator(T_bd_0,
system,
'T_f_in',
break_dict,
fulloutput=True)
# Second Dataset
all_dict[bif_par_var] += step_change
npzfile = _load_file(react_system, system, catalyst, all_dict)
T_bd_arr, Y_in_arr, species_ID_arr = npzfile.files
T_bd_1 = npzfile[T_bd_arr]
index_1 = ig_ext_point_calculator(T_bd_1,
system,
'T_f_in',
break_dict,
fulloutput=True)
# Function names
func = bif_set_func
# jac = bif_set_jacobian
# Solving for ignition-extinction locus
# First Ignition
y0 = np.zeros(2 * (no_of_species + 1))
y0[1] = 1
x0 = T_bd_0[:, index_0[0]]
x_init = np.r_[y0, x0]
J_0 = func(x_init,
bif_par_val,
inlet_species,
fixed_dict,
const,
react_const,
system,
rate_basis,
rate_units,
get_jacobian=True)
y_sol_0 = root(null_space, y0, args=J_0, method='lm')
X_init_0 = np.r_[y_sol_0.x, x0]
sol0 = root(func,
X_init_0,
args=(bif_par_val, inlet_species, fixed_dict, const,
react_const, system, rate_basis, rate_units))
Y0 = np.r_[sol0.x, bif_par_val]
T_bs = Y0[:]
# Second solution
bif_par_val += step_change
x1 = T_bd_1[:, index_1[0]]
x_init = np.r_[y0, x1]
J_1 = func(x_init,
bif_par_val,
inlet_species,
fixed_dict,
const,
react_const,
system,
rate_basis,
rate_units,
get_jacobian=True)
y_sol_1 = root(null_space, y0, args=J_1, method='lm')
X_init_1 = np.r_[y_sol_1.x, x1]
sol1 = root(func,
X_init_1,
args=(bif_par_val, inlet_species, fixed_dict, const,
react_const, system, rate_basis, rate_units))
Y1 = np.r_[sol1.x, bif_par_val]
T_bs = np.c_[T_bs, Y1[:]]
plot_flag = 'norm'
delta_s = np.linalg.norm(Y1 - Y0)
# Continuation method
while (Y1[-1] <= max_val and Y1[-1] >= break_dict[bif_par_var]
and Y1[-2] >= break_dict['T_f_in']):
print(Y1[-1])
Y_guess = 2 * Y1 - Y0
sol2 = root(func,
Y_guess,
args=(Y1, inlet_species, fixed_dict, const, react_const,
system, rate_basis, rate_units, delta_s))
Y2 = sol2.x
T_bs = np.c_[T_bs, Y2]
Y0[:] = Y1[:]
Y1[:] = Y2[:]
#### Plotting the figure
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_bs[-1, :], T_bs[-2, :])
else:
plt.plot(T_bs[-1, :], T_bs[-2, :])
plt.show
#### Storing data
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
filename = 'bif_set'
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
react_filename, ext = os.path.splitext(react_system)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower(
) + '/' + '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_bs, Y_in, species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower(
) + '/' + '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_bs
def low_D_Sh_phi_solver(fixed_dict, T_f_in_span, tspan,
react_system, system, catalyst,
rate_basis, rate_units, inlet_species, n, nT, options):
'''
This function solves the low_D_Sh_phi function to generate the
conversion vs Temperature curve.
fixed_dict : Dictionary of fixed variables and values.
T_f_in_span : Span of inlet temperature
react_system : Name of the reaction system
system : Whether the system is 'cat'-alytic, 'coup'-led, or
'h**o'-geneous
catalyst : Name of the catalyst used
rate_basis : Basis of the rate expressions, whether pressure,
concentration, mole fraction
rate_units : Units of the rate expressions
inlet_species : Name of the fuel, e.g. 'CH4'
n : No of discretized points in x
options : Some options, like whether to save the result, or just
to test the model.
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(react_system,
const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
inlet_ratio = fixed_dict['inlet_ratio']
y_A_in = inlet_ratio/(inlet_ratio + 1)
y_B_in = 1/(inlet_ratio + 1)
#### Function names
func = low_D_Sh_phi
T_f_in = np.linspace(T_f_in_span[0], T_f_in_span[1], nT)
conv_A = np.zeros_like(T_f_in)
conv_B = np.zeros_like(T_f_in)
if options['model'] is 'both':
conv_A_inf = np.zeros_like(T_f_in)
conv_B_inf = np.zeros_like(T_f_in)
for i in range(len(T_f_in)):
#### Inlet values
c_A_in = y_A_in * 101325 * fixed_dict['pressure']/(8.314 * T_f_in[i])
c_B_in = y_B_in * 101325 * fixed_dict['pressure']/(8.314 * T_f_in[i])
C_f_in = 1e-03 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
C_f_in[A_index] = c_A_in
C_f_in[B_index] = c_B_in
C_f_0 = np.tile(C_f_in, n)
C_w_0 = np.tile(C_f_in, n)
C_0 = np.r_[C_f_0, C_w_0]
if options['model'] is not 'both':
#### Calculating for only one model
try:
count = i + 1
print('Solving for T_f_in = {0} with {1} model'
.format(T_f_in[i], options['model']))
Y = odeint(func, C_0, tspan, args=(inlet_species, fixed_dict,
C_f_in, T_f_in[i],
const, thermo_object,
react_const, system,
rate_basis, rate_units,
options['model']))
except RuntimeWarning as e:
msg = ('The concentration of one of the reactants is going '
'close to zero, hence further calculations can result '
'in inaccurate results.')
temp = msg + 'Calculated till {}'.format(T_f_in[i-1])
print(temp)
count = i
break
elif options['model'] is 'both':
#### Comparing two models
print('Solving for T_f_in = {0} with {1} model'
.format(T_f_in[i], 'Sh_phi'))
Y = odeint(func, C_0, tspan, args=(inlet_species, fixed_dict,
C_f_in, T_f_in[i],
const, thermo_object,
react_const, system,
rate_basis, rate_units,
'Sh_phi'))
print('Solving for T_f_in = {0} with {1} model'
.format(T_f_in[i], 'Sh_inf'))
Y_inf = odeint(func, C_0, tspan, args=(inlet_species, fixed_dict,
C_f_in, T_f_in[i],
const, thermo_object,
react_const, system,
rate_basis, rate_units,
'Sh_inf'))
#### Calculating the conversion for Sh_inf model
C_f_ss_inf = Y_inf[-1, :no_of_species*n]
C_f_ss_mat_inf = C_f_ss_inf.reshape((no_of_species, n), order='F')
CH4_exit_inf = C_f_ss_mat_inf[A_index, -1]
O2_exit_inf = C_f_ss_mat_inf[B_index, -1]
conv_A_inf[i] = (1 - CH4_exit_inf/c_A_in)
conv_B_inf[i] = (1 - O2_exit_inf/c_B_in)
else:
raise Exception ('No such model exists!!!')
#### Conversion calculations
C_f_ss = Y[-1, :no_of_species*n]
C_f_ss_mat = C_f_ss.reshape((no_of_species, n), order='F')
CH4_exit = C_f_ss_mat[A_index, -1]
O2_exit = C_f_ss_mat[B_index, -1]
conv_A[i] = (1 - CH4_exit/c_A_in)
conv_B[i] = (1 - O2_exit/c_B_in)
#### Some random plotting
if options['model'] is not 'both':
fig, ax = plt.subplots()
ax.plot(T_f_in[:count], conv_A[:count], color='b', label='CH4 conv')
ax.plot(T_f_in[:count], conv_B[:count], color='r', label='O2 conv')
ax.legend(loc='best')
ax.set_xlim([0, 1])
ax.set_ylim(T_f_in_span)
ax.set_xlabel('Inlet Temperature, in K')
ax.set_ylabel('Conversions')
else:
fig, ax = plt.subplots()
ax.plot(T_f_in, conv_A, color='b', label=r'$\mathbf{Sh_\phi}$ model')
ax.plot(T_f_in, conv_A_inf, color='r', label=r'$\mathbf{Sh_\infty}$ model')
ax.legend(loc='best')
ax.set_ylim([0, 1])
ax.set_xlim(T_f_in_span)
ax.set_xlabel('Inlet Temperature, in K')
ax.set_ylabel(r'Conversion of $\mathbf{CH_4}$')
fig, ax1 = plt.subplots()
ax1.plot(T_f_in, conv_B, color='b', label= r'$\mathbf{Sh_\phi}$ model')
ax1.plot(T_f_in, conv_B_inf, color='r', label= r'$\mathbf{Sh_\infty}$ model')
ax1.legend(loc='best')
ax1.set_ylim([0, 1])
ax1.set_xlim(T_f_in_span)
ax1.set_xlabel('Inlet Temperature, in K')
ax1.set_ylabel(r'Conversion of $\mathbf{O_2}$')
plt.show()
return Y
def low_D_Sh_phi_solver(fixed_dict, T_f_in_span, tspan, react_system, system,
catalyst, rate_basis, rate_units, inlet_species, nT,
options):
'''
We will write this thing later
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
inlet_ratio = fixed_dict['inlet_ratio']
y_A_in = inlet_ratio / (inlet_ratio + 1)
y_B_in = 1 / (inlet_ratio + 1)
#### Function names
func = zero_D_sh_phi
T_f_in = np.linspace(T_f_in_span[0], T_f_in_span[1], nT)
conv_A = np.zeros_like(T_f_in)
conv_B = np.zeros_like(T_f_in)
if options['model'] is 'both':
conv_A_inf = np.zeros_like(T_f_in)
conv_B_inf = np.zeros_like(T_f_in)
for i in range(len(T_f_in)):
#### Inlet values
c_A_in = y_A_in * 101325 * fixed_dict['pressure'] / (8.314 * T_f_in[i])
c_B_in = y_B_in * 101325 * fixed_dict['pressure'] / (8.314 * T_f_in[i])
C_f_in = 1e-03 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
C_f_in[A_index] = c_A_in
C_f_in[B_index] = c_B_in
C_0 = np.r_[C_f_in, C_f_in]
if options['model'] is not 'both':
#### Calculating for only one model
try:
count = i + 1
print('Solving for T_f_in = {0} with {1} model'.format(
T_f_in[i], options['model']))
Y = odeint(func,
C_0,
tspan,
args=(inlet_species, fixed_dict, C_f_in, T_f_in[i],
const, thermo_object, react_const, system,
rate_basis, rate_units, options['model']))
except RuntimeWarning as e:
msg = ('The concentration of one of the reactants is going '
'close to zero, hence further calculations can result '
'in inaccurate results.')
temp = msg + 'Calculated till {}'.format(T_f_in[i - 1])
print(temp)
count = i
break
elif options['model'] is 'both':
#### Comparing two models
print('Solving for T_f_in = {0} with {1} model'.format(
T_f_in[i], 'Sh_phi'))
Y = odeint(func,
C_0,
tspan,
args=(inlet_species, fixed_dict, C_f_in, T_f_in[i],
const, thermo_object, react_const, system,
rate_basis, rate_units, 'Sh_phi'))
print('Solving for T_f_in = {0} with {1} model'.format(
T_f_in[i], 'Sh_inf'))
Y_inf = odeint(func,
C_0,
tspan,
args=(inlet_species, fixed_dict, C_f_in, T_f_in[i],
const, thermo_object, react_const, system,
rate_basis, rate_units, 'Sh_inf'))
#### Calculating the conversion for Sh_inf model
C_f_ss_inf = Y_inf[-1, :no_of_species]
CH4_exit_inf = C_f_ss_inf[A_index, ]
O2_exit_inf = C_f_ss_inf[B_index]
conv_A_inf[i] = (1 - CH4_exit_inf / c_A_in)
conv_B_inf[i] = (1 - O2_exit_inf / c_B_in)
else:
raise Exception('No such model exists!!!')
#### Conversion calculations
C_f_ss = Y[-1, :no_of_species]
CH4_exit = C_f_ss[A_index]
O2_exit = C_f_ss[B_index]
conv_A[i] = (1 - CH4_exit / c_A_in)
conv_B[i] = (1 - O2_exit / c_B_in)
#### Some random plotting
if options['model'] is not 'both':
fig, ax = plt.subplots()
ax.plot(T_f_in[:count], conv_A[:count], color='b', label='CH4 conv')
ax.plot(T_f_in[:count], conv_B[:count], color='r', label='O2 conv')
ax.legend(loc='best')
ax.set_xlabel('Inlet Temperature, in K')
ax.set_ylabel('Conversions')
else:
fig, ax = plt.subplots()
ax.plot(T_f_in, conv_A, color='b', label='Sh(Phi)')
ax.plot(T_f_in, conv_A_inf, color='r', label='Sh_inf')
ax.legend(loc='best')
ax.set_xlabel('Inlet Temperature, in K')
ax.set_ylabel('Conversion of CH4')
fig, ax1 = plt.subplots()
ax1.plot(T_f_in, conv_B, color='b', label='Sh(Phi)')
ax1.plot(T_f_in, conv_B_inf, color='r', label='Sh_inf')
ax1.legend(loc='best')
ax1.set_xlabel('Inlet Temperature, in K')
ax1.set_ylabel('Conversion of O2')
plt.show()
return Y
def bif_dia_plot(fixed_dict, bif_par_var, react_system, system, catalyst,
inlet_species, b_options, a_options):
'''
This function will load the correct file in order to plot the
different bifurcation diagrams.'''
#### Specifying linestyles and colors
lines = [':', '-', '--', ':', '-.']
no_of_lines = len(lines)
units = {
'inlet_ratio': '',
'pressure': ' atm',
'tau': 's',
'R_omega': 'mm',
'R_omega_wc': r'$\mathbf{\mu m}$',
'particle_density': 'kg/m3'
}
symbol = {
'inlet_ratio': r'$\mathbf{CH_4/O_2}$',
'pressure': r'$\mathbf{P}$',
'tau': r'$\mathbf{\tau}$',
'R_omega': r'$\mathbf{R_\Omega}$',
'R_omega_wc': r'$\mathbf{R_{\Omega, wc}}$',
'particle_density': r'$\mathbf{\rho}$'
}
#### Identifying the variable with multiple inputs
count = 0
multiple = False
multiple_variable = 'inlet_ratio'
for key, val in fixed_dict.items():
try:
len(val)
except TypeError:
continue
else:
multiple = True
multiple_variable = key
break
if multiple:
values = fixed_dict[multiple_variable]
else:
values = []
values.append(fixed_dict[multiple_variable])
for val in values:
count += 1
fixed_dict[multiple_variable] = val
if multiple_variable == 'R_omega':
fixed_dict[
'R_omega_wc'] = fixed_dict['R_omega'] / 0.25e-03 * 100e-06
style_index = count % no_of_lines
#### Retreiving the data from .npz file
npzfile = _load_file(react_system, system, catalyst, fixed_dict)
T_bd_arr, F_in_arr, species_ID_arr = npzfile.files
T_bd = npzfile[T_bd_arr]
F_in = npzfile[F_in_arr]
species_ID = npzfile[species_ID_arr].item()
n, m = T_bd.shape
if multiple_variable == 'R_omega':
label = (symbol[multiple_variable] + ' = ' + str(val * 1e03) +
units[multiple_variable])
else:
label = (symbol[multiple_variable] + ' = ' + str(val) +
units[multiple_variable])
if b_options['basic_plot']:
#### Retrieving the species_specific data from T_bd
hc_index = species_ID[inlet_species]
conv_hc = 1 - T_bd[hc_index, :] / F_in[hc_index]
O2_index = species_ID['O2']
conv_O2 = 1 - T_bd[O2_index, :] / F_in[O2_index]
#### Plotting Area
plot_dict = b_options['plots']
if bif_par_var == 'tau':
xplottype = 'log'
x_axis = 'Residence Time (s)'
else:
xplottype = 'normal'
x_axis = ('Inlet Fluid Temperature, ' +
r'$\mathbf{T_{f,in}}$' + ' (K)')
#### Exit Fluid Temperature vs Inlet Fluid Temperature
if plot_dict['fluid_temp']:
if count == 1:
fig, ax1 = plt.subplots()
fig.subplots_adjust(left=0.145, bottom=0.11)
ax1.set_xlabel(x_axis)
ax1.set_ylabel(('Exit Fluid Temperature, ' +
r'$\mathbf{T_f}$' + ' (K)'))
axis_limits = (b_options['xaxis_lim'] +
b_options['yaxis_lim'])
ax1.axis(axis_limits)
if xplottype == 'log':
ax1.semilogx(T_bd[-1, :],
T_bd[-2, :],
linestyle=lines[style_index],
label=label)
else:
ax1.plot(T_bd[-1, :],
T_bd[-2, :],
linestyle=lines[style_index],
label=label)
ax1.legend(loc='lower right')
plt.show()
#### Solid Temperature vs Inlet Fluid Temperature
if plot_dict['solid_temp']:
if count == 1:
fig, ax2 = plt.subplots()
ax2.set_xlabel(x_axis, fontsize=14, fontweight='bold')
ax2.set_ylabel(('Catalyst Surface Temperature ' +
r'$\mathbf{T_s}$' + ' (K)'))
axis_limits = (b_options['xaxis_lim'] +
b_options['yaxis_lim'])
ax2.axis(axis_limits)
if xplottype == 'log':
ax2.semilogx(T_bd[-1, :],
T_bd[-3, :],
linestyle=lines[style_index],
label=label)
else:
ax2.plot(T_bd[-1, :],
T_bd[-3, :],
linestyle=lines[style_index],
label=label)
ax2.legend(loc='best')
#### Conversion of Hydrocarbon and O2
if plot_dict['conversion']:
if count == 1:
fig31, ax31 = plt.subplots()
fig32, ax32 = plt.subplots()
axis_limits_CH4 = b_options['xaxis_lim'] + [0, 0.5]
ax31.set_xlabel(x_axis)
ax31.set_ylabel(('Conversion of ' + r'$\mathbf{CH_4}$'))
ax31.axis(axis_limits_CH4)
axis_limits = b_options['xaxis_lim'] + [0, 1]
ax32.set_xlabel(x_axis)
ax32.set_ylabel(('Conversion of ' + r'$\mathbf{O_2}$'))
ax32.axis(axis_limits)
if xplottype == 'log':
ax31.semilogx(T_bd[-1, :],
conv_hc,
linestyle=lines[style_index],
label=label)
ax32.semilogx(T_bd[-1, :],
conv_O2,
linestyle=lines[style_index],
label=label)
else:
ax31.plot(T_bd[-1, :],
conv_hc,
linestyle=lines[style_index],
label=label)
ax32.plot(T_bd[-1, :],
conv_O2,
linestyle=lines[style_index],
label=label)
ax31.legend(loc='best')
ax32.legend(loc='best')
#### Combined Selectivities and Yields
if (plot_dict['select_comb'] or plot_dict['yield_comb']):
elem_comb = ['CO', 'CO2', 'C2H6', 'C2H4', 'C2H2']
selectivity = [np.zeros(m)] * 5
select_dict = dict(zip(elem_comb, selectivity))
yield_dict = dict(zip(elem_comb, selectivity))
for elem in elem_comb:
elem_index = species_ID.get(elem, None)
carbon_no = species_identifier(elem)
if (elem_index != None) and (carbon_no != 0):
yield_elem = (T_bd[elem_index, :] - F_in[elem_index]) \
* carbon_no/F_in[hc_index]
selectivity_elem = (T_bd[elem_index, :]
- F_in[elem_index]) \
* carbon_no \
/ (F_in[hc_index] - T_bd[hc_index, :])
select_dict[elem] = selectivity_elem
yield_dict[elem] = yield_elem
selectivity_COx = select_dict['CO'] + select_dict['CO2']
selectivity_C2 = select_dict['C2H6'] + select_dict['C2H4'] \
+ select_dict['C2H2']
#selectivity_C2 = np.where(np.isnan(selectivity_C2), 0.0, selectivity_C2)
#selectivity_C2 = np.where(np.isinf(selectivity_C2), 0.0, selectivity_C2)
#selectivity_C2 = np.where(selectivity_C2 < 0.0, 0.0, selectivity_C2)
#selectivity_C2 = np.where(selectivity_C2 > 1.0, 0.0, selectivity_C2)
if fixed_dict['inlet_ratio'] == 4:
selectivity_C2[:
1500] = 0.0 # Didn't find any better method,
selectivity_COx[:1500] = 1.0
else:
selectivity_C2[:
1000] = 0.0 # Didn't find any better method,
selectivity_COx[:1000] = 1.0
if plot_dict['select_comb']:
if (count == 1):
fig61, ax6_sc1 = plt.subplots()
fig62, ax6_sc2 = plt.subplots()
axis_limits = b_options['xaxis_lim'] + [0, 1]
ax6_sc1.set_xlabel(x_axis)
ax6_sc1.set_ylabel(('Selectivity of (' +
r'$\mathbf{CO + CO_{2}}$' + ')'))
ax6_sc1.axis(axis_limits)
ax6_sc2.set_xlabel(x_axis)
ax6_sc2.set_ylabel(
('Selectivity of (' +
r'$\mathbf{C_{2}H_{6} + C_{2}H_{4}}$' + ')'))
ax6_sc2.axis(axis_limits)
if xplottype == 'log':
ax6_sc1.semilogx(T_bd[-1, :],
selectivity_COx,
linestyle=lines[style_index],
label=label)
ax6_sc2.semilogx(T_bd[-1, :],
selectivity_C2,
linestyle=lines[style_index],
label=label)
else:
ax6_sc1.plot(T_bd[-1, :],
selectivity_COx,
linestyle=lines[style_index],
label=label)
ax6_sc2.plot(T_bd[-1, :],
selectivity_C2,
linestyle=lines[style_index],
label=label)
ax6_sc1.legend(loc='best')
ax6_sc2.legend(loc='best')
if plot_dict['yield_comb']:
yield_COx = yield_dict['CO'] + yield_dict['CO2']
yield_C2 = yield_dict['C2H6'] + yield_dict['C2H4']\
+ yield_dict['C2H2']
if (count == 1):
fig71, ax7_yc1 = plt.subplots()
fig71.subplots_adjust(left=0.145, bottom=0.11)
fig72, ax7_yc2 = plt.subplots()
fig72.subplots_adjust(left=0.145, bottom=0.11)
axis_limits = b_options['xaxis_lim'] + [0, 0.3]
ax7_yc1.set_xlabel(x_axis)
ax7_yc1.set_ylabel(
('Yields of (' + r'$\mathbf{CO + CO_{2}}$' + ')'))
ax7_yc1.axis(axis_limits)
ax7_yc2.set_xlabel(x_axis)
ax7_yc2.set_ylabel(
('Yield of (' +
r'$\mathbf{C_{2}H_{6} + C_{2}H_{4}}$' + ')'))
ax7_yc2.axis(axis_limits)
if xplottype == 'log':
ax7_yc1.semilogx(T_bd[-1, :],
yield_COx,
linestyle=lines[style_index],
label=label)
ax7_yc2.semilogx(T_bd[-1, :],
yield_C2,
linestyle=lines[style_index],
label=label)
else:
ax7_yc1.plot(T_bd[-1, :],
yield_COx,
linestyle=lines[style_index],
label=label)
ax7_yc2.plot(T_bd[-1, :],
yield_C2,
linestyle=lines[style_index],
label=label)
ax7_yc1.legend(loc='best')
ax7_yc2.legend(loc='best')
#### Yields of all 'products', (Identifying the compound and then
#### identifying limiting reactant is
#### important, we will do it later)
if plot_dict['yield_all']:
products = b_options['products']
if products:
fig4, ax4_y = plt.subplots()
label = []
not_calc_elem = []
for elem in products:
elem_index = species_ID.get(elem, None)
carbon_no = species_identifier(elem)
if (elem_index != None) and (carbon_no != 0):
yield_elem = T_bd[elem_index, :] \
* carbon_no/F_in[hc_index]
if xplottype == 'log':
ax4_y.semilogx(T_bd[-1, :], yield_elem)
else:
ax4_y.plot(T_bd[-1, :], yield_elem)
label.append(elem)
else:
not_calc_elem.append(elem)
ax4_y.set_xlabel(x_axis)
ax4_y.set_ylabel(('Yield of Products'))
ax4_y.legend(tuple(label), loc='best')
axis_limits = b_options['xaxis_lim'] + [0, 0.5]
ax4_y.axis(axis_limits)
title = ('Inlet ratio = ' + str(fixed_dict['inlet_ratio']) +
', tau = ' + str(fixed_dict['tau']) + 's, Radius = ' +
str(fixed_dict['R_omega'] * 1e03) + 'mm.')
ax4_y.set_title(title)
if not_calc_elem:
print('The yields of {} are not calculated, '
'the program thinks they are '
'unimportant'.format(not_calc_elem))
#### Selectivity of all products
if plot_dict['select_all']:
products = b_options['products']
if products:
fig5, ax5_s = plt.subplots()
label = []
not_calc_elem = []
for elem in products:
elem_index = species_ID.get(elem, None)
carbon_no = species_identifier(elem)
if (elem_index != None) and (carbon_no != 0):
yield_elem = (T_bd[elem_index, :] - F_in[elem_index]) \
* carbon_no/F_in[hc_index]
selectivity_elem = (T_bd[elem_index, :]
- F_in[elem_index]) \
* carbon_no \
/ (F_in[hc_index] - T_bd[hc_index, :])
if xplottype == 'log':
ax5_s.semilogx(T_bd[-1, :],
selectivity_elem,
linewidth=2.0)
else:
ax5_s.plot(T_bd[-1, :],
selectivity_elem,
linewidth=2.0)
label.append(elem)
else:
not_calc_elem.append(elem)
ax5_s.set_xlabel(x_axis)
ax5_s.set_ylabel(('Selectivity of Products'))
ax5_s.legend(tuple(label), loc='best')
axis_limits = b_options['xaxis_lim'] + [0, 1]
ax5_s.axis(axis_limits)
title = ('Inlet ratio = ' + str(fixed_dict['inlet_ratio']) +
', tau = ' + str(fixed_dict['tau']) + 's, Radius = ' +
str(fixed_dict['R_omega'] * 1e03) + 'mm.')
ax5_s.set_title(title)
if not_calc_elem:
print('The yields of {} are not calculated, '
'the program thinks they are '
'unimportant'.format(not_calc_elem))
if a_options['analysis_plot']:
#### Retrieving the species_specific data from T_bd
const, thermo_object = constants.fixed_parameters(react_system)
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
#### The local species ID is useful in Thiele Modulus calculations
local_species = const['species']
no_of_local_species = len(local_species)
local_ID = np.arange(no_of_local_species)
local_species_ID = dict(zip(local_species, local_ID))
hc_local_index = local_species_ID[inlet_species]
O2_local_index = local_species_ID['O2']
#### This is the species_ID of the actual calculation coming from
#### the calling function
no_of_species = len(species_ID)
species = list(species_ID.keys())
hc_index = species_ID[inlet_species]
O2_index = species_ID['O2']
#### User-requested plots
plot_dict = a_options['plots']
if bif_par_var == 'tau':
xplottype = 'log'
x_axis = 'Residence Time (s)'
else:
xplottype = 'normal'
x_axis = ('Inlet Fluid Temperature ' + r'$\mathbf{T_{f,in}}$' +
' (K)')
#### Calculation of reaction rates
#### Unpacking the matrix T_bd
F_j = T_bd[:no_of_species, :]
C_s = T_bd[no_of_species:2 * no_of_species, :]
T_s = T_bd[2 * no_of_species, :]
T_f = T_bd[2 * no_of_species + 1, :]
bif_par = T_bd[-1, :]
no_of_iter = T_bd.shape[1]
T_f_in = T_bd[-1, :]
#### Unpacking the constants
P = fixed_dict['pressure']
R_omega = fixed_dict['R_omega']
R_omega_wc = fixed_dict['R_omega_wc']
R_omega_w = const['R_omega_w']
nu = const['nu']
nu_cat = nu.T[cat_index]
nu_cat_inlet_species = nu_cat[:, hc_local_index]
nu_cat_O2 = nu_cat[:, O2_local_index]
eps_f = 4 * R_omega**2 / (2 * R_omega + R_omega_wc + R_omega_w)**2
#### Defining mole fractions and concentrations
C_total = 101325 * P / (8.314 * T_f)
Y_j = F_j / np.sum(F_j, axis=0)
C_f = Y_j * C_total
C_in = 101325 * P / (8.314 * T_f_in)
#### Calculation of reaction rates [Fig. 1]
if plot_dict['reaction_rates'] or plot_dict['thiele_modulus']:
homo_basis, cat_basis = a_options['rate_basis']
homo_units, cat_units = a_options['rate_units']
all_homo_rate = np.zeros([len(homo_index), no_of_iter],
dtype=float)
all_cat_rate = np.zeros([len(cat_index), no_of_iter],
dtype=float)
for i in range(no_of_iter):
if (system == 'cat'):
homo_rate = np.zeros(len(homo_index))
cat_rate = cat.act_rate(C_s[:, i], species, cat_basis,
T_s[i], P)
elif (system == 'h**o'):
cat_rate = np.zeros(len(cat_index))
homo_rate = h**o.act_rate(C_f[:, i], species,
homo_basis, T_f[i], P)
else:
homo_rate = h**o.act_rate(C_f[:, i], species,
homo_basis, T_f[i], P)
cat_rate = cat.act_rate(C_s[:, i], species, cat_basis,
T_s[i], P)
all_homo_rate[:, i] = homo_rate[:]
all_cat_rate[:, i] = cat_rate[:]
#### This is not required for the time being
# We got to check the units and perform subsequent calculations
if homo_basis == 'mole_fraction':
if homo_units != 'second':
raise Exception('There is a discrepancy '
'in the homogeneous reaction rate')
if (cat_units == 'kg_sec'):
all_cat_rate *= fixed_dict['particle_density']
elif (cat_units == 'gm_sec'):
all_cat_rate *= fixed_dict['particle_density'] * 1000
# Here we make two separate variables for catalytic reactions
#homo_rate_C0 = all_homo_rate/C_in
cat_rate_C0 = all_cat_rate.copy()
cat_rate_C0_surf = all_cat_rate.copy()
all_cat_rate *= R_omega_wc * eps_f / R_omega
#return all_cat_rate - cat_rate_C0
#### Plotting the reaction rates
if plot_dict['reaction_rates'] and not multiple:
#### Catalytic reaction rates
fig, ax1 = plt.subplots()
label_cat = ()
for i in range(len(cat_index)):
if xplottype == 'log':
ax1.loglog(bif_par,
R_omega_wc * all_cat_rate[i, :])
else:
ax1.plot(bif_par, R_omega_wc * all_cat_rate[i, :])
label = 'Reaction No.= ' + str(cat_index[i] + 1)
label_cat += (label, )
ax1.set_xlabel(x_axis)
ax1.set_ylabel(('Catalytic reaction rates (' +
r'$\mathbf{mol/m^2 s}$' + ')'))
ax1.axes.set_xlim(10, 1200) # Hardcoded
ax1.legend(label_cat, loc='best')
#### Homogeneous reaction rates
fig, ax11 = plt.subplots()
label_homo = ()
for i in range(len(homo_index)):
if xplottype == 'log':
ax11.loglog(bif_par, all_homo_rate[i, :])
else:
ax11.plot(bif_par, all_homo_rate[i, :])
label = 'Reaction No.= ' + str(homo_index[i] + 1)
label_homo += (label, )
ax11.set_xlabel(x_axis)
ax11.set_ylabel(('Homogeneous reaction rates (' +
r'$\mathbf{mol/m^3 s}$' + ')'))
ax11.axes.set_xlim(10, 1200) # Hardcoded
ax11.legend(label_homo, loc='best')
#### Plotting Thiele Modulus
if plot_dict['thiele_modulus']:
#### Normal Thiele Modulus based on vol. cat. reac. rate
inlet_species_cat_rate = np.dot(-nu_cat_inlet_species,
cat_rate_C0)
D_f = 9.8e-10 * T_f**1.75
D_e = 0.01 * D_f
thiele_mod_inlet_species = (inlet_species_cat_rate /
C_s[hc_index, :] *
R_omega_wc**2 / D_e)
#### Surface Thiele Modulus based on surf. cat. reac. rate
cat_rate_C0_surf *= R_omega_wc
inlet_species_surf_cat_rate = np.dot(
-nu_cat_O2, cat_rate_C0_surf)
surf_thiele_modulus = (inlet_species_surf_cat_rate /
C_s[O2_index, :] * R_omega / D_f)
#print(fixed_dict['tau'])
#damkohler_second_type = 1/ (inlet_species_surf_cat_rate
# / C_s[hc_index, :])
if multiple_variable == 'R_omega':
label1 = (symbol['R_omega_wc'] + ' = ' +
str(int(100 * val / 0.25e-03)) +
units['R_omega_wc'])
label2 = (symbol['R_omega'] + ' = ' + str(val * 1e03) +
units['R_omega'])
else:
label1 = (symbol[multiple_variable] + ' = ' +
str(val) + units[multiple_variable])
label2 = label1
if count == 1:
fig, ax111 = plt.subplots()
ax111.set_xlabel(x_axis)
ax111.set_ylabel(('Thiele Modulus (' +
r'$\mathbf{\phi_{wc}^2}$' + ')'))
ax111.axes.set_xlim(10, 1200) # Hardcoded
fig, ax112 = plt.subplots()
ax112.set_xlabel(x_axis)
ax112.set_ylabel(('External Damkohler No. (' +
r'$\mathbf{Da_{ext}}$' + ')'))
ax112.axes.set_xlim(10, 1200) # Hardcoded
ax112.axes.set_ylim(1e-06, 1e06)
ax111.plot(bif_par,
thiele_mod_inlet_species,
linestyle=lines[style_index],
label=label1)
ax112.semilogy(bif_par,
surf_thiele_modulus,
linestyle=lines[style_index],
label=label2)
ax111.legend(loc='best')
ax112.legend(loc='best')
#ax113.legend(loc='best')
return T_bd
def _analysis(fixed_dict, bif_par_var, react_system, system, catalyst,
inlet_species, T_bd, F_in, species_ID, multiple, count,
a_options):
#### LineStyles
lines = ['-', '--', '-.', ':']
no_of_lines = len(lines)
style_index = count % no_of_lines
#linecycler = cycle(lines)
#### Retrieving the species_specific data from T_bd
const, thermo_object = constants.fixed_parameters(react_system)
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
#### The local species ID is useful in Thiele Modulus calculations
local_species = const['species']
no_of_local_species = len(local_species)
local_ID = np.arange(no_of_local_species)
local_species_ID = dict(zip(local_species, local_ID))
hc_local_index = local_species_ID[inlet_species]
O2_local_index = local_species_ID['O2']
#### This is the species_ID of the actual calculation coming from
#### the calling function
no_of_species = len(species_ID)
species = list(species_ID.keys())
hc_index = species_ID[inlet_species]
O2_index = species_ID['O2']
#### User-requested plots
plot_dict = a_options['plots']
if bif_par_var == 'tau':
xplottype = 'log'
x_axis = 'Residence Time (s)'
else:
xplottype = 'normal'
x_axis = ('Inlet Fluid Temperature ' + r'$\mathbf{T_{f,in}}$' + ' (K)')
#### Calculation of reaction rates
#### Unpacking the matrix T_bd
F_j = T_bd[:no_of_species, :]
C_s = T_bd[no_of_species:2 * no_of_species, :]
T_s = T_bd[2 * no_of_species, :]
T_f = T_bd[2 * no_of_species + 1, :]
bif_par = T_bd[-1, :]
no_of_iter = T_bd.shape[1]
T_f_in = T_bd[-1, :]
#### Unpacking the constants
R_omega = fixed_dict['R_omega']
R_omega_wc = fixed_dict['R_omega_wc']
R_omega_w = const['R_omega_w']
nu = const['nu']
nu_cat = nu.T[cat_index]
nu_cat_inlet_species = nu_cat[:, hc_local_index]
nu_cat_O2 = nu_cat[:, O2_local_index]
eps_f = 4 * R_omega**2 / (2 * R_omega + R_omega_wc + R_omega_w)**2
#### Defining mole fractions and concentrations
C_total = 101325 / (8.314 * T_f)
Y_j = F_j / np.sum(F_j, axis=0)
C_f = Y_j * C_total
C_in = 101325 / (8.314 * T_f_in)
#### Calculation of reaction rates [Fig. 1]
if plot_dict['reaction_rates'] or plot_dict['thiele_modulus']:
homo_basis, cat_basis = a_options['rate_basis']
homo_units, cat_units = a_options['rate_units']
all_homo_rate = np.zeros([len(homo_index), no_of_iter], dtype=float)
all_cat_rate = np.zeros([len(cat_index), no_of_iter], dtype=float)
for i in range(no_of_iter):
if (system == 'cat'):
homo_rate = np.zeros(len(homo_index))
cat_rate = cat.act_rate(species, cat_basis, C_s[:, i], T_s[i])
elif (system == 'h**o'):
cat_rate = np.zeros(len(cat_index))
homo_rate = h**o.act_rate(species, homo_basis, C_f[:, i],
T_f[i])
else:
homo_rate = h**o.act_rate(species, homo_basis, C_f[:, i],
T_f[i])
cat_rate = cat.act_rate(species, cat_basis, C_s[:, i], T_s[i])
all_homo_rate[:, i] = homo_rate[:]
all_cat_rate[:, i] = cat_rate[:]
#### This is not required for the time being
# We got to check the units and perform subsequent calculations
if homo_basis == 'mole_fraction':
if homo_units != 'second':
raise Exception('There is a discrepancy '
'in the homogeneous reaction rate')
if (cat_units == 'kg_sec'):
all_cat_rate *= fixed_dict['particle_density']
elif (cat_units == 'gm_sec'):
all_cat_rate *= fixed_dict['particle_density'] * 1000
# Here we make two separate variables for catalytic reactions
#homo_rate_C0 = all_homo_rate/C_in
cat_rate_C0 = all_cat_rate.copy()
all_cat_rate *= R_omega_wc * eps_f / R_omega
#return all_cat_rate - cat_rate_C0
#### Plotting the reaction rates
if plot_dict['reaction_rates'] and not multiple:
#### Catalytic reaction rates
fig, ax1 = plt.subplots()
label_cat = ()
for i in range(len(cat_index)):
if xplottype == 'log':
ax1.loglog(bif_par,
all_cat_rate[i, :],
linestyle=next(linecycler))
else:
ax1.plot(bif_par,
all_cat_rate[i, :],
linestyle=next(linecycler))
label = 'Reaction No.= ' + str(cat_index[i] + 1)
label_cat += (label, )
ax1.set_xlabel(x_axis)
ax1.set_ylabel(
('Catalytic reaction rates (' + r'$\mathbf{mol/m^3 s}$' + ')'))
ax1.axes.set_xlim(10, 1200) # Hardcoded
ax1.legend(label_cat, loc='best')
#### Homogeneous reaction rates
fig, ax11 = plt.subplots()
label_homo = ()
for i in range(len(homo_index)):
if xplottype == 'log':
ax11.loglog(bif_par,
all_homo_rate[i, :],
linestyle=next(linecycler))
else:
ax11.plot(bif_par,
all_homo_rate[i, :],
linestyle=next(linecycler))
label = 'Reaction No.= ' + str(homo_index[i] + 1)
label_homo += (label, )
ax11.set_xlabel(x_axis)
ax11.set_ylabel(('Homogeneous reaction rates (' +
r'$\mathbf{mol/m^3 s}$' + ')'))
ax11.axes.set_xlim(10, 1200) # Hardcoded
ax11.legend(label_homo, loc='best')
#### Plotting Thiele Modulus based on volumetric cat. reaction rates
if plot_dict['thiele_modulus']:
inlet_species_cat_rate = np.dot(-nu_cat_inlet_species, cat_rate_C0)
thiele_mod_inlet_species = (inlet_species_cat_rate /
C_s[hc_index, :] * R_omega_wc**2 /
1e-06)
inlet_species_surf_cat_rate = np.dot(-nu_cat_inlet_species,
all_cat_rate)
surf_thiele_modulus = (inlet_species_surf_cat_rate /
C_s[hc_index, :] * R_omega**2 / 1e-08)
if count == 1:
fig, ax111 = plt.subplots()
ax111.set_xlabel(x_axis)
ax111.set_ylabel(
('Thiele Modulus (' + r'$\mathbf{\phi^2}$' + ')'))
ax111.axes.set_xlim(10, 1200) # Hardcoded
fig, ax112 = plt.subplots()
ax112.set_xlabel(x_axis)
ax112.set_ylabel(('Surface Thiele Modulus (' +
r'$\mathbf{\phi_{s}^2}$' + ')'))
ax112.axes.set_xlim(10, 1200) # Hardcoded
ax111.plot(bif_par,
thiele_mod_inlet_species,
linestyle=lines[style_index])
ax112.plot(bif_par,
surf_thiele_modulus,
linestyle=lines[style_index])
#### Calculation of ratio at the surface [Fig. 2]
if plot_dict['ratio_surface'] or plot_dict['surface_to_bulk']:
conc_surf_hc = C_s[hc_index, :]
conc_surf_O2 = C_s[O2_index, :]
surface_ratio = conc_surf_hc / conc_surf_O2
if plot_dict['ratio_surface']:
fig, ax2 = plt.subplots()
if xplottype == 'log':
ax2.loglog(bif_par, surface_ratio)
else:
ax2.semilogy(bif_par, surface_ratio)
ax2.set_xlabel(x_axis)
ax2.set_ylabel(('HC to O2 ratio at surface'))
#axis_limits = b_options['xaxis_lim'] + b_options['yaxis_lim']
#ax1.axis(axis_limits)
#### Calculation of ratio in bulk [Fig. 3]
if plot_dict['ratio_bulk'] or plot_dict['surface_to_bulk']:
molar_rate_hc = F_j[hc_index, :]
molar_rate_O2 = F_j[O2_index, :]
bulk_ratio = molar_rate_hc / molar_rate_O2
if plot_dict['ratio_bulk']:
fig, ax3 = plt.subplots()
if xplottype == 'log':
ax3.loglog(bif_par, bulk_ratio)
else:
ax3.semilogy(bif_par, bulk_ratio)
ax3.set_xlabel(x_axis)
ax3.set_ylabel('HC to O2 ratio at bulk')
#axis_limits = b_options['xaxis_lim'] + b_options['yaxis_lim']
#ax1.axis(axis_limits)
#### Calculation of ratio of HC to O2 in surface to that in bulk [Fig. 4]
if plot_dict['surface_to_bulk']:
surface_to_bulk = surface_ratio / bulk_ratio
fig, ax4 = plt.subplots()
if xplottype == 'log':
ax4.loglog(bif_par, surface_to_bulk, color='b', linewidth=2.0)
else:
ax4.semilogy(bif_par, surface_to_bulk, color='b', linewidth=2.0)
ax4.set_xlabel(x_axis)
ax4.set_ylabel('HC to O2 ratio at surface to that in bulk')
#axis_limits = b_options['xaxis_lim'] + b_options['yaxis_lim']
#ax1.axis(axis_limits)
if plot_dict['C2H4_C2H6_ratio']:
ethylene_index = species_ID['C2H4']
ethane_index = species_ID['C2H6']
ethylene_ethane_ratio = F_j[ethylene_index, :] / F_j[ethane_index, :]
fig, ax5 = plt.subplots()
if xplottype == 'log':
ax5.loglog(bif_par, ethylene_ethane_ratio)
else:
ax5.semilogy(bif_par, ethylene_ethane_ratio)
ax5.set_xlabel(x_axis)
ax5.set_ylabel('Ethylene to Ethane ratio in bulk')
def low_D_non_iso_model_solver(fixed_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, n,
options):
'''
This function solves the low_D_Sh_phi function to generate the
conversion vs Temperature curve.
fixed_dict : Dictionary of fixed variables and values.
react_system : Name of the reaction system
system : Whether the system is 'cat'-alytic, 'coup'-led, or
'h**o'-geneous
catalyst : Name of the catalyst used
rate_basis : Basis of the rate expressions, whether pressure,
concentration, mole fraction
rate_units : Units of the rate expressions
inlet_species : Name of the fuel, e.g. 'CH4'
n : No of discretized points in x
options : Some options, like whether to save the result, or just
to test the model.
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
inlet_ratio = fixed_dict['inlet_ratio']
y_A_in = inlet_ratio / (inlet_ratio + 1)
y_B_in = 1 / (inlet_ratio + 1)
T_f_in = 300
#### Function names
func = low_D_non_iso_model
tspan = np.linspace(0, 8000, 1000000)
#### Inlet values
c_A_in = y_A_in * 101325 * fixed_dict['pressure'] / (8.314 * T_f_in)
c_B_in = y_B_in * 101325 * fixed_dict['pressure'] / (8.314 * T_f_in)
C_f_in = 1e-03 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
C_f_in[A_index] = c_A_in
C_f_in[B_index] = c_B_in
C_f_0 = np.tile(C_f_in, n)
C_w_0 = np.tile(C_f_in, n)
T_f_0 = np.tile(T_f_in, n)
# T_f_0 = np.linspace(300, 600, n)
T_s_0 = np.tile(T_f_in, n)
C_0 = np.r_[C_f_0, C_w_0, T_f_0, T_s_0]
#### Testing
if options['Testing']:
Y = func(C_0, 4200, inlet_species, fixed_dict, const, thermo_object,
react_const, system, rate_basis, rate_units, options['model'])
return Y
else:
#### Integration
Y = odeint(func,
C_0,
tspan,
args=(inlet_species, fixed_dict, const, thermo_object,
react_const, system, rate_basis, rate_units,
options['model']))
### Conversion calculations
T_f_in = np.array(list(map(temp_ramp_func, tspan)))
C_in = 101325 * fixed_dict['pressure'] / (8.314 * T_f_in)
CH4_in = y_A_in * C_in
O2_in = y_B_in * C_in
print(no_of_species * (n - 1))
print(no_of_species * n)
C_f_exit = Y[:, no_of_species * (n - 1):no_of_species * n]
CH4_exit = C_f_exit[:, A_index]
O2_exit = C_f_exit[:, B_index]
conv_A = (1 - CH4_exit / CH4_in)
conv_B = (1 - O2_exit / O2_in)
print(n * (no_of_species * 2 + 1) - 1)
T_f_exit = Y[:, n * (no_of_species * 2 + 1) - 1]
#### Plotting
fig, ax = plt.subplots()
ax.plot(T_f_in, conv_B)
ax.set_xlabel('Inlet Fluid Temperature (K)')
ax.set_ylabel(r'Conversion of $\mathbf{O_2}$')
ax.set_xlim([0, 1])
fig, ax1 = plt.subplots()
ax1.plot(T_f_in, T_f_exit)
ax1.set_xlabel('Inlet fluid Temperature (K)')
ax1.set_ylabel('Exit fluid Temperature (K)')
plt.show()
return Y
def hys_locus_solver(fixed_dict, bif_par_dict, react_system, system,
catalyst, rate_basis, rate_units, inlet_species,
break_dict, step_change, max_val, options):
'''
This function solves for the hysteresis locus.
(Region of multiplicities)
We will write other details later.
Just to point out one important difference between hys_locus_solver
and bif_dia_solver is that, here we take in the entire break_dict
It is similar to bif_set_solver in that respect.
'''
#### Generating the constant parameters
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print('This is the new species ID',species_ID)
#### Manipulation of input variables
all_dict = dict(bif_par_dict, **fixed_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
break_val = break_dict[bif_par_var]
#y_A_in_max_val = max_dict['y_A_in']
# Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(react_system,
const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
##### First dataset
if options['occurence']:
filename = 'bif_set_{0}_{1}'.format(options['limit_point'],
options['occurence'])
else:
filename = 'bif_set_{0}'.fomat(options['limit_point'])
npzfile = _load_file(react_system, system, catalyst, all_dict,
filename=filename)
T_bs_arr, species_ID_arr = npzfile.files
T_bs = npzfile[T_bs_arr]
species_ID_old = npzfile[species_ID_arr]
index = ig_ext_point_calculator(T_bs, system, 'inlet_ratio',
break_dict, fulloutput= False,
tol=1e-02)
limit_index = 0
#data_vals = [value for (key, value) in sorted(fixed_dict.items())]
#n = len(data_vals)
#
#filename = 'hysteresis_locus_left_{}'.format(options['occurence'])
#for i in range(n):
# filename += '_{}'.format(data_vals[i])
#filename += '.npz'
#print(filename)
#
#react_filename, ext = os.path.splitext(react_system)
#
#Target_folder = os.path.join(os.getcwd(), react_filename,
# catalyst.lower(), 'Data', system)
#FullfileName = os.path.join(Target_folder, filename)
#
#print(FullfileName)
#npzfile = np.load(FullfileName)
#T_hl_arr = npzfile.files
#T_hl = npzfile[T_hl_arr[0]]
#x = T_hl[4*(no_of_species+1):-1, -1]
#bif_par_val = T_hl[-1, -1]
#### Function name
func = hys_locus_func
jac = jacobian
#### Initial guess of the left and right eigen vectors
no_of_func = 2*(no_of_species + 1)
y0 = np.zeros(no_of_func, dtype=float)
v0 = np.zeros_like(y0)
y0[1] = 1
v0[1] = 1
col_index = index[limit_index]
if col_index == -1 or col_index == 0:
raise Exception('Cusp point does not exist at this configuration')
x_old = T_bs[:, col_index]
#### Rearranging the species data (Because of different species IDs)
fluid_f_old = x_old[:no_of_species]
wc_f_old = x_old[no_of_species : 2*no_of_species]
invariant = x_old[2*no_of_species : ]
fluid_f_new = _rearrange_species(species_ID_old, species_ID, fluid_f_old)
wc_f_new = _rearrange_species(species_ID_old, species_ID, wc_f_old)
x = np.r_[fluid_f_new, wc_f_new, invariant]
x_init = np.r_[y0, v0, x, bif_par_val]
J, d2f = func(x_init, inlet_species, fixed_dict, const, thermo_object,
react_const, system, rate_basis, rate_units, options,
get_jacobian=True, get_second_deriv=True)
Y_init = np.r_[y0, v0]
Y0 = root(left_right_eigen_vector, Y_init, args=(J, d2f), method='lm')
state_var_val = np.r_[Y0.x, x, bif_par_val]
if options['Testing']:
print('\nThe eigen vectors are calculated under the following status')
print('Status: {0}, msg: {1}'.format(Y0.status, Y0.message))
print('\nAll the varibles going into the function')
print(state_var_val)
F = func(state_var_val, inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis, rate_units,
options)
return F
else:
#### Solving for hysteresis locus
no_of_var = len(state_var_val)
pref = np.ones(no_of_var)
no_of_func = 2*(no_of_species + 1)
weights = np.ones(no_of_var)
weights[:2*no_of_func] = 1e-04
weights[-5:-2] = 1e-03
weights[-2] = 1e-01
jac_eps = options['jac_eps']
T_hl = der.derpar(func, jac, state_var_val, pref, max_val,
break_val, weights, jac_eps, initial=False,
hh=step_change, maxout=5000, hhmax=10*step_change,
ncorr=5, args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units, options), kwargs=())
##### Plotting
if bif_par_var == 'T_f_in' or bif_par_var == 'inlet_species':
plot_flag = 'norm'
else:
plot_flag = 'log'
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_hl[-1, :], 1/T_hl[-2, :])
else:
plt.plot(T_hl[-1, :], 1/T_hl[-2, :])
plt.show()
#### Storing data
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
if options['occurence']:
filename = 'hysteresis_locus_left_{0}'.format(options['occurence'])
else:
filename = 'hysteresis_locus_left'
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
react_filename, ext = os.path.splitext(react_system)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_hl[2*no_of_func:, :], species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_hl
def bif_set_solver(fixed_dict, bif_par_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, break_dict,
step_change, max_val, options):
'''
This function solves for the bifurcation set
(ignition-extinction locus).
we will write other details later
Just to point out one important difference between bif_set_solver
and bif_dia_solver is that, here we take in the entire break_dict
'''
# Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print('This is the new species ID',species_ID)
# Manipulation of input variables
all_dict = dict(fixed_dict, **bif_par_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
break_val = break_dict[bif_par_var]
temp_break_val = break_dict['T_f_in']
# Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(react_system,
const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
# First Dataset
npzfile = _load_file(react_system, system, catalyst, all_dict)
T_bd_arr, Y_in_arr, species_ID_arr = npzfile.files
T_bd = npzfile[T_bd_arr]
species_ID_old = npzfile[species_ID_arr]
T_bd_wo_noise = T_bd[:, 1000:]
index = ig_ext_point_calculator(T_bd_wo_noise, system,
'T_f_in', break_dict,
fulloutput=True)
#### Fixing the starting point (ignition/extinction, first/second)
if options['limit_point'] == 'ignition':
if options['occurence'] == 'first':
limit_index = 0
elif options['occurence'] == 'second':
limit_index = 2
else:
raise Exception('Wrong value of occurence')
elif options['limit_point'] == 'extinction':
if options['occurence'] == 'first':
limit_index = 1
elif options['occurence'] == 'second':
limit_index = 3
else:
raise Exception('Wrong value of occurence')
else:
raise Exception('Wrong value of limit_point')
#### Function names
func = bif_set_func
jac = jacobian
#### Initial guess
no_of_func = 2*(no_of_species + 1)
y0 = np.zeros(no_of_func)
y0[1] = 1
col_index = index[limit_index]
if col_index == -1 or col_index == 0:
raise Exception('Limit point does not exist at this configuration')
x_old = T_bd_wo_noise[:, col_index]
#### Rearranging the species data (Because of different species IDs)
fluid_f_old = x_old[:no_of_species]
wc_f_old = x_old[no_of_species : 2*no_of_species]
invariant = x_old[2*no_of_species : ]
fluid_f_new = _rearrange_species(species_ID_old, species_ID, fluid_f_old)
wc_f_new = _rearrange_species(species_ID_old, species_ID, wc_f_old)
x = np.r_[fluid_f_new, wc_f_new, invariant]
x_init = np.r_[y0, x, bif_par_val]
J = func(x_init, inlet_species, fixed_dict, const, thermo_object,
react_const, system, rate_basis, rate_units, options,
get_jacobian=True)
y0_sol = root(null_space, y0, args= J, method= 'lm')
state_var_val = np.r_[y0_sol.x, x, bif_par_val]
if options['Testing']:
print('\nThe eigen vectors are calculated under the following status')
print('Status: {0}, msg: {1}'.format(y0_sol.status, y0_sol.message))
print('\nAll the varibles going into the function')
print(state_var_val)
print('\nAnd the fixed variables are:')
print(fixed_dict)
Ysol = func(state_var_val, inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units, options)
return Ysol
else:
#### Solving for ignition-extinction locus
no_of_var = len(state_var_val)
pref = np.ones(no_of_var)
weights = np.ones(no_of_var)
# weights[:2*(no_of_species + 1)] = 1e-03
# weights[-4:-1] = 1e-03
# weights[-1] = 1e-01
jac_eps = options['jac_eps']
T_bs = der.derpar(func, jac, state_var_val, pref, max_val,
break_val, weights, jac_eps, initial=False,
hh=step_change, maxout=100000,
hhmax= 10*step_change, ncorr=5,
args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units, options), kwargs=(temp_break_val))
#### Plotting the figure
if bif_par_var == 'T_f_in' or bif_par_var == 'inlet_ratio':
plot_flag = 'norm'
else:
plot_flag = 'log'
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_bs[-1, :], T_bs[-2, :])
else:
plt.plot(T_bs[-1, :], T_bs[-2, :])
plt.show()
#### Storing data
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
filename = 'bif_set_{}_{}'.format(options['limit_point'],
options['occurence'])
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
react_filename, ext = os.path.splitext(react_system)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_bs[no_of_func:, :], species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_bs
def bif_dia_solver(fixed_dict, bif_par_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, break_val,
step_change, max_val, options):
'''
This function solves for the bifurcation diagram.
fixed_dict= Dictionary of fixed variables and values.
bif_par_dict= Dictionary of Bifurcation variable and value.
react_system= Describes the reaction system chosen,
e.g: 'OCM_three_reaction.txt'.
system= Whether the system is catalytic only, homogeneous only, or
homogeneous-heterogeneous coupled.
inlet_species= The hydrocarbon species, as of now just hydrocarbon and
O2 is taken as inlet.
break_val= break value of the bifurcation parameter.
step_change= step_change value of the bifurcation parameter, necessary in
Pseudo-Arc Length Continuation.
max_val= maximum value of the bifurcation parameter.
options= Solver Options.
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Manipulation of input variables
all_dict = dict(fixed_dict, **bif_par_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
#### Initial guess
inlet_ratio = all_dict['inlet_ratio']
N2 = 0 # For the time being, we use pure O2
F_A_in = inlet_ratio/(N2 + inlet_ratio + 1)
F_B_in = 1/(N2 + inlet_ratio + 1)
F_in = 1e-08 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
F_in[A_index] = F_A_in
F_in[B_index] = F_B_in
state_var_val = np.r_[F_in, F_in, all_dict['T_f_in'],
all_dict['T_f_in'], bif_par_val]
#### Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(react_system,
const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
#### Function name
func = bif_dia_func
jac = jacobian
plot_flag = 'norm'
if options['Testing']:
#### Just testing the function(s)
print('\nAll the varibles going into the function')
print(state_var_val)
print('\nAnd the fixed valriables going into the function')
print(fixed_dict)
Ysol = func(state_var_val, inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis, rate_units)
return Ysol
elif options['solver'] == 'python':
#### Solving the set of equations using Arc-Length method in Python
no_of_var = len(state_var_val)
pref = np.ones(no_of_var)
weights = np.ones(no_of_var)
weights[-2:] = 1e-03
jac_eps = options['jac_eps']
T_bd = der.derpar(func, jac, state_var_val, pref, max_val, break_val,
weights, jac_eps, initial= False, hh = step_change,
maxout = 200000, hhmax = 10*step_change,
args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis, rate_units))
else:
#### Solving the set of equations using Arc-Length method in Fortran
eps = 1e-08
w = np.ones(no_of_var, dtype=float, order='F')
initial = 0
itin = 50
hh = 0.05
hmax = 0.1 * np.ones(no_of_var, dtype=float, order='F')
ndir = np.ones(no_of_var, dtype=np.int64, order='F')
e = 1e-06
mxadms = 4
ncorr = 4
ncrad = 0
maxout = 4
nout, out = arc_length.derpar(func, jac, state_var_val,
break_val, max_val, eps, w, initial, itin, hh,
hmax, pref, ndir, e, mxadms, ncorr, ncrad, maxout,
func_extra_args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units),
jac_extra_args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units))
out_act = out[:nout,:-1]
T_bd = out_act.T
#### Plotting the figure
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_bd[-1, :], T_bd[-2, :])
else:
plt.plot(T_bd[-1, :], T_bd[-2, :])
plt.show()
#### Storing data
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
filename = 'bif_dia'
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
react_filename, ext = os.path.splitext(react_system)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_bd, F_in, species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = react_filename + '/' + catalyst.lower() + '/' \
+ '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_bd
def bif_dia_solver(fixed_dict, bif_par_dict, react_system, system, catalyst,
rate_basis, rate_units, inlet_species, break_val,
step_change, max_val, options):
'''
We will write this thing later
'''
#### Constant parameters involved with the system
const, thermo_object = constants.fixed_parameters(react_system)
species = const['species']
no_of_species = len(species)
ID = np.arange(no_of_species)
species_ID = dict(zip(species, ID))
print(species_ID)
#### Manipulation of input variables
all_dict = dict(fixed_dict, **bif_par_dict)
bif_par_var = list(bif_par_dict.keys())[0]
bif_par_val = bif_par_dict.get(bif_par_var)
#### Initial guess
T_f_in = all_dict['T_f_in']
inlet_ratio = all_dict['inlet_ratio']
N2 = 0 # For the time being, we use pure O2
F_A_in = inlet_ratio / (N2 + inlet_ratio + 1)
F_B_in = 1 / (N2 + inlet_ratio + 1)
F_in = 1e-08 * np.ones(no_of_species)
A_index = species_ID[inlet_species]
B_index = species_ID['O2']
F_in[A_index] = F_A_in
F_in[B_index] = F_B_in
state_var_val = np.r_[F_in, F_in, T_f_in, T_f_in, bif_par_val]
#### Generating the reaction objects
h**o, cat, homo_index, cat_index = react.instantiate(
react_system, const, catalyst)
react_const = [h**o, cat, homo_index, cat_index]
#### Function name
func = bif_dia_func
jac = jacobian
plot_flag = 'norm'
if options['Testing']:
#### Just testing the function(s)
print('\nAll the varibles going into the function')
print(state_var_val)
print('\nAnd the fixed valriables going into the function')
print(fixed_dict)
Ysol = func(state_var_val, inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis, rate_units,
options)
return Ysol
else:
#### Solving the set of equations using Arc-Length method in Python
no_of_var = len(state_var_val)
pref = np.ones(no_of_var)
weights = np.ones(no_of_var)
weights[-2:] = 1e-03
jac_eps = options['jac_eps']
max_iter = options['max_iter']
data_vals = [value for (key, value) in sorted(fixed_dict.items())]
n = len(data_vals)
react_filename, ext = os.path.splitext(react_system)
if options['model'] is 'sh_phi' and options['write_eig_val']:
#### Filename
file = react_filename + '_' + catalyst.lower() + '_' + system
for i in range(n):
file += '_{}'.format(data_vals[i])
file += '.txt'
#### Directory
folder = os.path.join(os.getcwd(), 'EigenValues')
if not os.path.isdir(folder):
os.mkdir('EigenValues')
fullfilename = os.path.join(folder, file)
print(fullfilename)
#### Creating the file
with open(fullfilename, "w") as fh:
fh.write(react_system)
fh.write('\n')
options.update({'eig_val_filename': fullfilename})
T_bd = der.derpar(func,
jac,
state_var_val,
pref,
max_val,
break_val,
weights,
jac_eps,
eps=1e-04,
initial=False,
maxIter=50,
hh=step_change,
maxout=max_iter,
hhmax=10 * step_change,
ncorr=6,
args=(inlet_species, fixed_dict, const,
thermo_object, react_const, system, rate_basis,
rate_units, options))
#### Plotting the figure
fig = plt.figure()
if plot_flag == 'log':
plt.semilogx(T_bd[-1, :], T_bd[-2, :])
else:
plt.plot(T_bd[-1, :], T_bd[-2, :])
plt.show()
#### Storing data
filename = 'bif_dia_{}'.format(options['model'].lower())
for i in range(n):
filename += '_{}'.format(data_vals[i])
print(filename)
if options['save']:
#### Saving Data
Target_folder = os.path.join(os.getcwd(), 'Washcoat', react_filename,
catalyst.lower(), 'Data', system)
if os.path.isdir(Target_folder) == False:
New_Folder = 'Washcoat/' + react_filename + '/' + catalyst.lower() + '/' \
+ '/Data/' + system
os.makedirs(New_Folder)
FullfileName = os.path.join(Target_folder, filename)
np.savez(FullfileName, T_bd, F_in, species_ID)
#### Saving Diagrams (Why I don't know)
Target_folder = os.path.join(os.getcwd(), 'Washcoat', react_filename,
catalyst.lower(), 'Diagram', system)
if os.path.isdir(Target_folder) == False:
New_Folder = 'Washcoat/' + react_filename + '/' + catalyst.lower() + '/' \
+ '/Diagram/' + system
os.makedirs(New_Folder)
dia_filename = filename + '.png'
FullfileName = os.path.join(Target_folder, dia_filename)
fig.savefig(FullfileName)
#### Returning the final result
return T_bd