def setUp(self):
self.phase = ct.DustyGas('h2o2.xml')
self.phase.TPX = 500.0, ct.one_atm, "O2:2.0, H2:1.0, H2O:1.0"
self.phase.porosity = 0.2
self.phase.tortuosity = 0.3
self.phase.mean_pore_radius = 1e-4
self.phase.mean_particle_diameter = 5e-4
self.Dref = self.phase.multi_diff_coeffs
def DGM (Por, Tau, RPo, Dia, PTR, PTL, TL, TR, PL, PR, YL, YR): # solve the dusty gas model # set the transport manager to the dusty gas model dg = ct.DustyGas(FileCanteraGas) dg.porosity = Por dg.tortuosity = Tau dg.mean_pore_radius = RPo dg.mean_particle_diameter = Dia DGMFlux = np.zeros((Kgas,)) Dist = PTR - PTL # setup properties on the left side gas.TPY = (TL, PL, YL) XMOLL = gas.X DENSL = gas.density CONCL = gas.density * XMOLL ENTHL = gas.standard_enthalpies_RT * ct.gas_constant * TL # setup properties on the right side gas.TPY = (TR, PR, YR) XMOLR = gas.X DENSR = gas.density CONCR = gas.density * XMOLR ENTHR = gas.standard_enthalpies_RT * ct.gas_constant * TR DGMFlux = dg.molar_fluxes(TL, TR, DENSL, DENSR, YL, YR, Dist) DGMHeat = 0.0 for k in range (1,Kgas - 1): if DGMFlux[k] >= 0: DGMHeat = DGMHeat + ENTHL[k] * DGMFlux[k] else: DGMHeat = DGMHeat + ENTHR[k] * DGMFlux[k] # convert molar fluxes from DGM to mass fluxes for k in range (1,Kgas - 1): DGMFlux [k] = W[k] * DGMFlux[k] return(DGMFlux, DGMHeat)
t = datetime.now()
dtStr = t.strftime('%m_%d_%Y_%H%M')
save_string = (membrane_params['name']+'_'+str(int(row_temp['Feed Temp [C]']))+'_'+str(int(row_temp['Perm Temp [C]']))+'_'+dtStr)
print(save_string)
i_tau=-1
for tau_g in tau_g_vec:
i_tau += 1
print('Tortusity factor = ',tau_g)
tic = timeit.default_timer()
if DGM:
# Transport flag: 0 for DGM, 1 for Fickean
trans_flag = 0
gas = ct.DustyGas(ctifile)
gas.porosity = membrane_params['eps_g']
gas.tortuosity = tau_g
gas.mean_pore_radius = 2.*membrane_params['r_p']
gas.mean_particle_diameter = membrane_params['d_p']
liq = ct.Solution(ctifile,liqphase)
liq_int = ct.Interface(ctifile,intphase,[gas,liq])
params['sdot_gas_ptr'] = np.arange(int_ord_gas,int_ord_gas+gas.n_species)
from oneD_membrane_sim import load_model, run_model
SV_0, params = load_model(gas, membrane_params, params, temp_data.loc[i_temp,:], n_points)
"""
Dusty Gas transport model.
The Dusty Gas model is a multicomponent transport model for gas transport
through the pores of a stationary porous medium. This example shows how to
create a transport manager that implements the Dusty Gas model and use it to
compute the multicomponent diffusion coefficients.
"""
import cantera as ct
# create a gas-phase object to represent the gas in the pores, with a
# dusty gas transport manager
g = ct.DustyGas('h2o2.cti')
# set the gas state
g.TPX = 500.0, ct.one_atm, "OH:1, H:2, O2:3, O:1.0E-8, H2:1.0E-8, H2O:1.0E-8, H2O2:1.0E-8, HO2:1.0E-8, AR:1.0E-8"
# set its parameters
g.porosity = 0.2
g.tortuosity = 4.0
g.mean_pore_radius = 1.5e-7
g.mean_particle_diameter = 1.5e-6 # lengths in meters
# print the multicomponent diffusion coefficients
print(g.multi_diff_coeffs)
# compute molar species fluxes
T1, rho1, Y1 = g.TDY
g.TP = g.T, 1.2 * ct.one_atm
cwd = os.getcwd()
gas_path = ct.__path__[0]
copy2(os.path.basename(__file__), folder_name)
if os.path.exists(gas_file):
copy2(gas_file, folder_name)
else:
copy2(gas_path + '/data/' + gas_file, folder_name)
# Tortuosity calculation via Bruggeman correlation:
tau_g = phi_g**(-0.5)
# Call cantera for gas-phase:
if Diff_Mod == 1:
gas = ct.Solution(gas_file)
elif Diff_Mod == 2:
gas = ct.DustyGas(gas_file)
gas.porosity = phi_g
gas.tortuosity = tau_g
gas.mean_pore_radius = r_p
gas.mean_particle_diameter = d_p
# Initialize solution vector:
Nspecies = gas.n_species
SV_0 = np.zeros(Nx * Ny * Nspecies)
# Given constants:
F = ct.faraday # Faraday Constant [s-A/kmol]
R = ct.gas_constant # Universal gas constant [J/kmol-K]
Temp = Temp + 273.15 # convert temperature from [C] -> [K]
gas.TP = Temp, Press # set gas state via temperature and pressure
Calculate transport properties in a porous medium using the dusty gas transport model.
The Dusty Gas model is a multicomponent transport model for gas transport
through the pores of a stationary porous medium. This example shows how to
create a transport manager that implements the Dusty Gas model and use it to
compute the multicomponent diffusion coefficients and thermal conductivity.
Requires: cantera >= 2.6.0
Keywords: transport, multicomponent transport
"""
import cantera as ct
# create a gas-phase object to represent the gas in the pores, with a
# dusty gas transport manager
g = ct.DustyGas('h2o2.yaml')
# set the gas state
composition = {
"OH": 1,
"H": 2,
"O2": 3,
"O": 1.0E-8,
"H2": 1.0E-8,
"H2O": 1.0E-8,
"H2O2": 1.0E-8,
"HO2": 1.0E-8,
"AR": 1.0E-8
}
g.TPX = 500.0, ct.one_atm, composition
def initialize(membrane, tau_g, transport, feed_temp, permeate_temp):
"""
Initialize variables, parameters, and Cantera objects for the simulation.
- Reads user inputs from membrane_distillation_inputs.yaml
- Creates necessary Cantera objects.
- Reads and stores necessary parameters.
- Initializes the solution vector
Returns:
- Initial solution vector SV_0
- Dictionary of cantera objects obj
- Dictionary of paramters params
"""
import numpy as np
from ruamel.yaml import YAML
from pathlib import Path
import cantera as ct
#===========================================================================
# READ IN INPUTS
#===========================================================================
input_file = 'membrane_distillation_inputs.yaml'
path = Path(input_file)
yaml = YAML(typ='safe')
inputs = yaml.load(path)
#===========================================================================
# READ OUT MEMBRANE PROPERTIES
#===========================================================================
# Read out the selected membrane. Specification via command-line overrides # all other input. If no command-line input is provided, look in the
# input file. Otherwise, raise an exception:
if membrane is None:
if 'membrane' in inputs['simulation-params']:
membrane = inputs['simulation-params']['membrane']
else:
raise ValueError(
'Please specify a membrane for your simulation'
'either via command line (--membrane=[name string]) or as a'
' field in membrane_distillation_inputs.yaml:simulation-params'
)
# print(membrane)
# This tracks whether or not the named membrane was found in the list of
# parameters in the input file. Initialize to "not found":
found = 0
for mem in inputs['membrane-data']:
if mem['name'] == str(membrane):
if found:
# More than one set of membrane params has that name.
# Throw an error.
raise ValueError('There were two or more membranes with the'
' name ' + membrane +
' in your input file. Pleease edit.')
membrane_params = mem
found = 1
if not found:
raise ValueError('Specified membrane ' + membrane + ' not found in' +
' membrane_distillation_inputs.yaml. Pleease edit.')
#===========================================================================
# LOAD PARAMETERS INTO 'param' DICT
#===========================================================================
# Create dictionary of parameters, 'params'
params = {}
params['n_y'] = inputs['simulation-params']['n_y']
params['dyInv'] = params['n_y'] / membrane_params['thickness']
params['dy'] = 1. / params['dyInv']
params['eps_g'] = membrane_params['porosity']
params['r_pore'] = 0.5 * membrane_params['pore-diameter']
params['d_part'] = membrane_params['particle-diameter']
params['kappa_membrane'] = membrane_params['thermal-conductivity']
params['c_v_membrane'] = membrane_params['const-vol-specific-heat']
params['rho_membrane'] = membrane_params['solid-phase-density']
params['h_fg_feed'] = inputs['temp-data']['h_fg_feed']
params['h_fg_permeate'] = inputs['temp-data']['h_fg_permeate']
#===========================================================================
# LOAD PARAMETERS THAT CAN COME FROM EITHER INPUT FILE OR COMMAND LINE:
#===========================================================================
# Load the transport model:
if transport:
params['transport'] = transport
elif 'transport-model' in inputs['simulation-params']:
params['transport'] = inputs['simulation-params']['transport-model']
else:
raise ValueError('Please select a transport model: DGM or Fick.')
# Load the tortuosity:
if tau_g:
params['tau_g'] = float(tau_g)
elif 'tau_g' in membrane_params:
params['tau_g'] = membrane_params['tau_g']
else:
raise ValueError('Please specify a tortuosity tau_g, either via the '
'command line (--tau_g=[value]) or as a field in '
'membrane_distillation_inputs.yaml:membrane-params')
# For the Fick model, we manually store the membrane permeability:
if params['transport'] == 'Fick':
params['K_g'] = (4. * params['d_part']**2 * params['eps_g']**3 /
(72. * params['tau_g']**2 *
(1. - params['eps_g'])**2))
Press = 101325.0 # Initial gas pressure [Pa]
# Read out the feed flow temperature and convert to K:
if feed_temp is not None:
params['T_feed'] = 273.15 + feed_temp
elif 'T_feed' in inputs['temp-data']:
params['T_feed'] = 273.15 + inputs['temp-data']['T_feed']
else:
raise ValueError('Please specify a feed temperature, either via the'
' command line (--feed_temp=[temp]) or as a field in '
'membrane_distillation_inputs.yaml:temp-data')
# Read out the permeate flow temperature and convert to K:
if permeate_temp is not None:
params['T_permeate'] = 273.15 + permeate_temp
elif 'T_permeate' in inputs['temp-data']:
params['T_permeate'] = 273.15 + inputs['temp-data']['T_permeate']
else:
raise ValueError(
'Please specify a permeate temperature, either via '
'the command line (--permeate_temp=[temp]) or as a field in '
'membrane_distillation_inputs.yaml:temp-data')
#===========================================================================
# CHECK FOR AND LOAD INTEGRATOR PARAMETERS:
#===========================================================================
# If tolerances are not specified, load in the defaults:
if 'rtol' not in params:
params['rtol'] = 1e-3
if 'atol' not in params:
params['atol'] = 1e-6
# If a final simulation time is not specified, load the default:
if 't final' not in params:
params['t final'] = 1000 # seconds
# If an integrator method is not specified, load BDF:
if 'method' not in params:
params['method'] = 'BDF'
#===========================================================================
# Create Cantera objects and store in 'obj' dict:
#===========================================================================
if params['transport'] == 'Fick':
gas = ct.Solution(input_file, inputs['phase-names']['gas'])
elif params['transport'] == 'DGM':
gas = ct.DustyGas(input_file, inputs['phase-names']['gas'])
gas.tortuosity = params['tau_g']
gas.porosity = params['eps_g']
gas.mean_pore_radius = params['r_pore']
gas.mean_particle_diameter = params['d_part']
liquid = ct.Solution(input_file, inputs['phase-names']['liquid'])
interface = ct.Interface(input_file, inputs['phase-names']['interface'],
[gas, liquid])
obj = {'gas': gas, 'liquid': liquid, 'interface': interface}
#===========================================================================
# POINTERS TO SOLUTION VECTOR LOCATIONS
#===========================================================================
# Each volume has n_species + 1 variables (the extra being temperature)
params['n_vars'] = gas.n_species + 1
size_SV = params['n_y'] * params['n_vars']
SV_0 = np.zeros(size_SV)
params['ptr_temp'] = range(0, size_SV, params['n_vars'])
params['ptr_rho_k'] = np.ndarray(shape=(params['n_y'], gas.n_species),
dtype='int')
for j in range(params['n_y']):
params['ptr_rho_k'][j, :] = range(
1 + j * params['n_vars'], 1 + j * params['n_vars'] + gas.n_species)
#===========================================================================
# INITIALIZE THE SOLUTION VECTOR
#===========================================================================
# Calculate the mole fraction of water vapor at the feed side, assuming
# saturation.
# Generic string for gas-phase mole fractions:
comp_string = 'N2:{},H2O:{}'
X_h2o = inputs['temp-data']['P_H2O_feed'] / Press
X_k_feed = comp_string.format(1. - X_h2o, X_h2o)
# Calculate the mole fraction of water vapor at the permeate side,
# assuming saturation:
X_h2o = inputs['temp-data']['P_H2O_permeate'] / Press
X_k_permeate = comp_string.format(1. - X_h2o, X_h2o)
# Use cantera gas object to calcualte species mass densities:
# Feed side:
gas.TPX = params['T_feed'], Press, X_k_feed
rho_k_feed = gas.density * gas.Y
# Permeate side:
gas.TPX = params['T_permeate'], Press, X_k_permeate
rho_k_permeate = gas.density * gas.Y
# Starting guess assumes linear gradients:
SV_0[params['ptr_temp']] = np.linspace(params['T_feed'],
params['T_permeate'], params['n_y'])
for k in np.arange(gas.n_species):
SV_0[params['ptr_rho_k'][:, k]] = np.linspace(rho_k_feed[k],
rho_k_permeate[k],
params['n_y'])
return SV_0, obj, params
