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import numpy as np
from numpy.testing import assert_allclose
import openpnm as op
import openpnm.models.physics as pm
class GenericSourceTermTest:
def setup_class(self):
self.net = op.network.Cubic(shape=[5, 5, 5])
self.phase = op.phase.Phase(network=self.net)
self.phase['throat.diffusive_conductance'] = 5e-8
self.phase['pore.mole_fraction'] = 0.0
self.BC_pores = np.arange(20, 30)
self.source_pores = np.arange(55, 85)
def test_default_values_should_give_zero_rate(self):
sources = ["linear", "power_law", "exponential", "natural_exponential",
"logarithm", "natural_logarithm"]
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source_term',
pores=self.source_pores)
# To avoid nans in logarithm-based source term models
self.phase['pore.mole_fraction'] = 0.1
for source in sources:
self.phase.add_model(propname="pore.source_term",
model=getattr(pm.source_terms, source),
X="pore.mole_fraction")
assert self.phase["pore.source_term.rate"].mean() == 0
assert self.phase["pore.source_term.S1"].mean() == 0
assert self.phase["pore.source_term.S2"].mean() == 0
def test_linear(self):
self.phase['pore.item1'] = 0.5e-11
self.phase['pore.item2'] = 1.5e-12
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.linear,
A1='pore.item1',
A2='pore.item2',
X='pore.mole_fraction',
regen_mode='normal')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.round(np.sum(0.5e-11 * X[self.source_pores] + 1.5e-12), 20)
r2 = np.round(np.sum(self.phase['pore.source1.rate'][self.source_pores]), 20)
assert r1 == r2
def test_power_law(self):
self.phase['pore.item1'] = 0.5e-12
self.phase['pore.item2'] = 2.5
self.phase['pore.item3'] = -1.4e-11
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.power_law,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
X='pore.mole_fraction',
regen_mode='normal')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.sum(0.5e-12 * X[self.source_pores]**2.5 - 1.4e-11)
r2 = np.sum(self.phase['pore.source1.rate'][self.source_pores])
assert r1 == r2
def test_exponential(self):
self.phase['pore.item1'] = 0.8e-11
self.phase['pore.item2'] = 3
self.phase['pore.item3'] = 0.5
self.phase['pore.item4'] = 2
self.phase['pore.item5'] = -0.34
self.phase['pore.item6'] = 2e-14
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.exponential,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
A4='pore.item4',
A5='pore.item5',
A6='pore.item6',
X='pore.mole_fraction',
regen_mode='normal')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.sum(0.8e-11 * 3 ** (0.5 * X[self.source_pores]**2 - 0.34) + 2e-14)
r2 = np.sum(self.phase['pore.source1.rate'][self.source_pores])
assert_allclose(actual=r2, desired=r1, rtol=1e-7)
def test_natural_exponential(self):
self.phase['pore.item1'] = 0.8e-11
self.phase['pore.item2'] = 0.5
self.phase['pore.item3'] = 2
self.phase['pore.item4'] = -0.34
self.phase['pore.item5'] = 2e-14
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.natural_exponential,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
A4='pore.item4',
A5='pore.item5',
X='pore.mole_fraction',
regen_mode='normal')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.sum(0.8e-11 * np.exp(0.5 * X[self.source_pores]**2 - 0.34) + 2e-14)
r2 = np.sum(self.phase['pore.source1.rate'][self.source_pores])
# assert_allclose(actual=r2, desired=r1)
def test_logarithm(self):
self.phase['pore.item1'] = 0.16e-13
self.phase['pore.item2'] = 10
self.phase['pore.item3'] = 4
self.phase['pore.item4'] = 1.4
self.phase['pore.item5'] = 0.133
self.phase['pore.item6'] = -5.1e-13
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.logarithm,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
A4='pore.item4',
A5='pore.item5',
A6='pore.item6',
X='pore.mole_fraction',
regen_mode='normal')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.sum(0.16e-13 * np.log(4*X[self.source_pores]**(1.4) + 0.133)
/ np.log(10) - 5.1e-13)
r2 = np.sum(self.phase['pore.source1.rate'][self.source_pores])
assert_allclose(actual=r2, desired=r1)
def test_natural_logarithm(self):
self.phase['pore.item1'] = 0.16e-14
self.phase['pore.item2'] = 4
self.phase['pore.item3'] = 1.4
self.phase['pore.item4'] = 0.133
self.phase['pore.item5'] = -5.1e-14
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.natural_logarithm,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
A4='pore.item4',
A5='pore.item5',
X='pore.mole_fraction',
regen_mode='on_demand')
self.alg = op.algorithms.ReactiveTransport(network=self.net,
phase=self.phase)
self.alg.set_value_BC(values=0.4, pores=self.BC_pores)
self.alg.set_source(propname='pore.source1',
pores=self.source_pores)
self.alg.settings._update({'conductance': 'throat.diffusive_conductance',
'quantity': 'pore.mole_fraction'})
self.alg.run()
self.phase.update(self.alg.soln)
self.phase.regenerate_models(propnames='pore.source1')
X = self.phase['pore.mole_fraction']
r1 = np.sum(0.16e-14*np.log(4*X[self.source_pores]**1.4 + 0.133) - 5.1e-14)
r2 = np.sum(self.phase['pore.source1.rate'][self.source_pores])
# assert_allclose(actual=r2, desired=r1)
def test_general_symbolic(self):
# natural log function
y = "a*ln(b*x**c + d)+e"
self.phase['pore.item1'] = 0.16e-14
self.phase['pore.item2'] = 4
self.phase['pore.item3'] = 1.4
self.phase['pore.item4'] = 0.133
self.phase['pore.item5'] = -5.1e-14
self.phase.add_model(propname='pore.source1',
model=pm.source_terms.natural_logarithm,
A1='pore.item1',
A2='pore.item2',
A3='pore.item3',
A4='pore.item4',
A5='pore.item5',
X='pore.mole_fraction',
regen_mode='normal')
arg_map = {'a': 'pore.item1',
'b': 'pore.item2',
'c': 'pore.item3',
'd': 'pore.item4',
'e': 'pore.item5'}
self.phase.add_model(propname='pore.general',
model=op.models.physics.source_terms.general_symbolic,
eqn=y, x='pore.mole_fraction', **arg_map)
assert np.allclose(self.phase['pore.source1.rate'],
self.phase['pore.general.rate'])
assert np.allclose(self.phase['pore.source1.S1'],
self.phase['pore.general.S1'])
assert np.allclose(self.phase['pore.source1.S2'],
self.phase['pore.general.S2'])
def test_butler_volmer_kinetics(self):
np.random.seed(10)
self.net["pore.reaction_area"] = np.random.rand(self.net.Np)
self.phase['pore.electrolyte_voltage'] = np.random.rand(self.net.Np) * 0.2
self.phase['pore.solid_voltage'] = 1.1
self.phase['pore.open_circuit_voltage'] = 1.2
self.phase['pore.electrolyte_concentration'] = np.random.rand(self.net.Np)
BV_params = {
"n": 4,
"i0_ref": 1e-3,
"c_ref": 1000,
"beta": 0.5,
"gamma": 1
}
self.phase.add_model(propname='pore.rxn_BV_c',
model=pm.source_terms.butler_volmer_conc,
X="pore.electrolyte_concentration", **BV_params)
self.phase.add_model(propname='pore.rxn_BV_v',
model=pm.source_terms.butler_volmer_voltage,
X="pore.electrolyte_voltage", **BV_params)
# Check Butler-Volmer model (concentration)
S1_BV_c = self.phase["pore.rxn_BV_c.S1"]
S2_BV_c = self.phase["pore.rxn_BV_c.S2"]
rate_BV_c = self.phase["pore.rxn_BV_c.rate"]
eps = np.finfo(float).eps * np.abs(rate_BV_c).max()
assert_allclose(S2_BV_c.mean(), 0, atol=eps)
assert_allclose(S1_BV_c.mean(), -0.0008190647392381356)
assert_allclose(rate_BV_c.mean(), -0.00040556020470030826)
# Check Butler-Volmer model (voltage)
S1_BV_v = self.phase["pore.rxn_BV_v.S1"]
S2_BV_v = self.phase["pore.rxn_BV_v.S2"]
rate_BV_v = self.phase["pore.rxn_BV_v.rate"]
assert_allclose(S2_BV_v.mean(), 2104.1093852527683)
assert_allclose(S1_BV_v.mean(), -12190.386173792602)
assert_allclose(rate_BV_v.mean(), -156.52244418065774)
# The two Butler-Volmer models must only differ by n*F (unit conversion)
assert_allclose(rate_BV_v, rate_BV_c * BV_params["n"] * 96485.33212)
if __name__ == '__main__':
t = GenericSourceTermTest()
self = t
t.setup_class()
for item in t.__dir__():
if item.startswith('test'):
print(f"Running test: {item}")
t.__getattribute__(item)()
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