def div(symbol):
"""
convenience function for creating a :class:`Divergence`
Parameters
----------
symbol : :class:`Symbol`
the divergence will be performed on this sub-symbol
Returns
-------
:class:`Divergence`
the divergence of ``symbol``
"""
# Divergence of a broadcast is zero
if isinstance(symbol, pybamm.PrimaryBroadcastToEdges):
new_child = pybamm.PrimaryBroadcast(0, symbol.child.domain)
return pybamm.PrimaryBroadcast(new_child, symbol.domain)
# Divergence commutes with Negate operator
if isinstance(symbol, pybamm.Negate):
return -div(symbol.orphans[0])
else:
return Divergence(symbol)
def _get_coupled_variables_from_potential(self, variables, phi_e_av):
i_boundary_cc = variables["Current collector current density"]
param = self.param
l_n = param.l_n
l_p = param.l_p
x_n = pybamm.standard_spatial_vars.x_n
x_p = pybamm.standard_spatial_vars.x_p
phi_e_n = pybamm.PrimaryBroadcast(phi_e_av, ["negative electrode"])
phi_e_s = pybamm.PrimaryBroadcast(phi_e_av, ["separator"])
phi_e_p = pybamm.PrimaryBroadcast(phi_e_av, ["positive electrode"])
phi_e = pybamm.Concatenation(phi_e_n, phi_e_s, phi_e_p)
i_e_n = pybamm.outer(i_boundary_cc, x_n / l_n)
i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc, ["separator"])
i_e_p = pybamm.outer(i_boundary_cc, (1 - x_p) / l_p)
i_e = pybamm.Concatenation(i_e_n, i_e_s, i_e_p)
variables.update(self._get_standard_potential_variables(phi_e, phi_e_av))
variables.update(self._get_standard_current_variables(i_e))
eta_c_av = pybamm.Scalar(0) # concentration overpotential
delta_phi_e_av = pybamm.Scalar(0) # ohmic losses
variables.update(self._get_split_overpotential(eta_c_av, delta_phi_e_av))
return variables
def test_concatenation_simplify(self):
# Primary broadcast
var = pybamm.Variable("var", "current collector")
a = pybamm.PrimaryBroadcast(var, "negative electrode")
b = pybamm.PrimaryBroadcast(var, "separator")
c = pybamm.PrimaryBroadcast(var, "positive electrode")
concat = pybamm.concatenation(a, b, c)
self.assertIsInstance(concat, pybamm.PrimaryBroadcast)
self.assertEqual(concat.orphans[0], var)
self.assertEqual(
concat.domain,
["negative electrode", "separator", "positive electrode"])
# Full broadcast
a = pybamm.FullBroadcast(0, "negative electrode", "current collector")
b = pybamm.FullBroadcast(0, "separator", "current collector")
c = pybamm.FullBroadcast(0, "positive electrode", "current collector")
concat = pybamm.concatenation(a, b, c)
self.assertIsInstance(concat, pybamm.FullBroadcast)
self.assertEqual(concat.orphans[0].id, pybamm.Scalar(0).id)
self.assertEqual(
concat.domain,
["negative electrode", "separator", "positive electrode"])
self.assertEqual(concat.auxiliary_domains,
{"secondary": ["current collector"]})
def nasty_hack_to_get_phi_s(self, variables):
"This restates what is already in the electrode submodel which we should not do"
param = self.param
x_n = pybamm.standard_spatial_vars.x_n
x_p = pybamm.standard_spatial_vars.x_p
eps = variables[self.domain + " electrode porosity"]
i_boundary_cc = variables["Current collector current density"]
i_e = variables[self.domain + " electrolyte current density"]
i_s = pybamm.PrimaryBroadcast(i_boundary_cc, self.domain_for_broadcast) - i_e
if self.domain == "Negative":
conductivity = param.sigma_n * (1 - eps) ** param.b_n
phi_s = -pybamm.IndefiniteIntegral(i_s / conductivity, x_n)
elif self.domain == "Positive":
phi_e_s = variables["Separator electrolyte potential"]
delta_phi_p = variables["Positive electrode surface potential difference"]
conductivity = param.sigma_p * (1 - eps) ** param.b_p
phi_s = -pybamm.IndefiniteIntegral(
i_s / conductivity, x_p
) + pybamm.PrimaryBroadcast(
pybamm.boundary_value(phi_e_s, "right")
+ pybamm.boundary_value(delta_phi_p, "left"),
"positive electrode",
)
return phi_s
def format(self, left, right):
"Format children left and right into compatible form"
# Turn numbers into scalars
if isinstance(left, numbers.Number):
left = pybamm.Scalar(left)
if isinstance(right, numbers.Number):
right = pybamm.Scalar(right)
# Check both left and right are pybamm Symbols
if not (isinstance(left, pybamm.Symbol) and isinstance(right, pybamm.Symbol)):
raise NotImplementedError(
"""'{}' not implemented for symbols of type {} and {}""".format(
self.__class__.__name__, type(left), type(right)
)
)
# Do some broadcasting in special cases, to avoid having to do this manually
if left.domain != [] and right.domain != []:
if (
left.domain != right.domain
and "secondary" in right.auxiliary_domains
and left.domain == right.auxiliary_domains["secondary"]
):
left = pybamm.PrimaryBroadcast(left, right.domain)
if (
right.domain != left.domain
and "secondary" in left.auxiliary_domains
and right.domain == left.auxiliary_domains["secondary"]
):
right = pybamm.PrimaryBroadcast(right, left.domain)
return left, right
def set_rhs(self, variables):
"Composite reaction-diffusion with source terms from leading order"
param = self.param
eps_0 = variables["Leading-order porosity"]
deps_0_dt = variables["Leading-order porosity change"]
c_e = variables["Electrolyte concentration"]
N_e = variables["Electrolyte flux"]
if self.extended is False:
sum_s_j = variables[
"Leading-order sum of electrolyte reaction source terms"
]
elif self.extended == "distributed":
sum_s_j = variables["Sum of electrolyte reaction source terms"]
elif self.extended == "average":
sum_s_j_n_av = variables[
"Sum of x-averaged negative electrode electrolyte reaction source terms"
]
sum_s_j_p_av = variables[
"Sum of x-averaged positive electrode electrolyte reaction source terms"
]
sum_s_j = pybamm.Concatenation(
pybamm.PrimaryBroadcast(sum_s_j_n_av, "negative electrode"),
pybamm.FullBroadcast(0, "separator", "current collector"),
pybamm.PrimaryBroadcast(sum_s_j_p_av, "positive electrode"),
)
source_terms = sum_s_j / self.param.gamma_e
self.rhs = {
c_e: (1 / eps_0)
* (-pybamm.div(N_e) / param.C_e + source_terms - c_e * deps_0_dt)
}
def test_public_functions(self):
param = pybamm.standard_parameters_lead_acid
a = pybamm.Scalar(0)
a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
variables = {
"Current collector current density": a,
"Negative electrode potential": a_n,
"Negative electrolyte potential": a_n,
"Negative electrode open circuit potential": a_n,
"Negative electrolyte concentration": a_n,
"Negative electrode temperature": a_n,
}
submodel = pybamm.interface.ButlerVolmer(param, "Negative",
"lead-acid main")
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
variables = {
"Current collector current density": a,
"Positive electrode potential": a_p,
"Positive electrolyte potential": a_p,
"Positive electrode open circuit potential": a_p,
"Positive electrolyte concentration": a_p,
"Positive electrode temperature": a_p,
"Negative electrode interfacial current density": a_n,
"Negative electrode exchange current density": a_n,
}
submodel = pybamm.interface.ButlerVolmer(param, "Positive",
"lead-acid main")
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
def get_fundamental_variables(self):
c_e_av = pybamm.standard_variables.c_e_av
c_e_n = pybamm.PrimaryBroadcast(c_e_av, ["negative electrode"])
c_e_s = pybamm.PrimaryBroadcast(c_e_av, ["separator"])
c_e_p = pybamm.PrimaryBroadcast(c_e_av, ["positive electrode"])
return self._get_standard_concentration_variables(c_e_n, c_e_s, c_e_p)
def test_public_functions(self):
param = pybamm.standard_parameters_lead_acid
a = pybamm.Scalar(0)
variables = {
"Current collector current density":
a,
"Negative electrolyte current density":
pybamm.PrimaryBroadcast(a, ["negative electrode"]),
"Positive electrolyte current density":
pybamm.PrimaryBroadcast(a, ["positive electrode"]),
"Negative electrode porosity":
a,
"Positive electrode porosity":
a,
"Separator electrolyte potential":
a,
"Positive electrode surface potential difference":
a,
}
submodel = pybamm.electrode.ohm.SurfaceForm(param, "Negative")
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
submodel = pybamm.electrode.ohm.SurfaceForm(param, "Positive")
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
def get_coupled_variables(self, variables):
j_n = variables[
"X-averaged negative electrode interfacial current density"]
j_p = variables[
"X-averaged positive electrode interfacial current density"]
j_sei_n = variables[
"X-averaged negative electrode sei interfacial current density"]
beta_sei_n = self.param.beta_sei_n
deps_n_dt = pybamm.PrimaryBroadcast(
-self.param.beta_surf_n * j_n + beta_sei_n * j_sei_n,
["negative electrode"])
deps_s_dt = pybamm.FullBroadcast(
0,
"separator",
auxiliary_domains={"secondary": "current collector"})
deps_p_dt = pybamm.PrimaryBroadcast(-self.param.beta_surf_p * j_p,
["positive electrode"])
variables.update(
self._get_standard_porosity_change_variables(
deps_n_dt, deps_s_dt, deps_p_dt))
return variables
def test_shape_and_size_for_testing(self):
scal = pybamm.Scalar(1)
self.assertEqual(scal.shape_for_testing, scal.shape)
self.assertEqual(scal.size_for_testing, scal.size)
state = pybamm.StateVector(slice(10, 25), domain="test")
state2 = pybamm.StateVector(slice(10, 25), domain="test 2")
self.assertEqual(state.shape_for_testing, state.shape)
param = pybamm.Parameter("a")
self.assertEqual(param.shape_for_testing, ())
func = pybamm.FunctionParameter("func", {"state": state})
self.assertEqual(func.shape_for_testing, state.shape_for_testing)
concat = pybamm.concatenation(state, state2)
self.assertEqual(concat.shape_for_testing, (30, 1))
self.assertEqual(concat.size_for_testing, 30)
var = pybamm.Variable("var", domain="negative electrode")
broadcast = pybamm.PrimaryBroadcast(0, "negative electrode")
self.assertEqual(var.shape_for_testing, broadcast.shape_for_testing)
self.assertEqual((var + broadcast).shape_for_testing,
broadcast.shape_for_testing)
var = pybamm.Variable("var", domain=["random domain", "other domain"])
broadcast = pybamm.PrimaryBroadcast(0,
["random domain", "other domain"])
self.assertEqual(var.shape_for_testing, broadcast.shape_for_testing)
self.assertEqual((var + broadcast).shape_for_testing,
broadcast.shape_for_testing)
sym = pybamm.Symbol("sym")
with self.assertRaises(NotImplementedError):
sym.shape_for_testing
def test_jac_of_domain_concatenation(self):
# create mesh
disc = get_1p1d_discretisation_for_testing()
mesh = disc.mesh
y = pybamm.StateVector(slice(0, 1500))
# Jacobian of a DomainConcatenation of constants is a zero matrix of the
# appropriate size
a_dom = ["negative electrode"]
b_dom = ["separator"]
c_dom = ["positive electrode"]
cc_npts = mesh["current collector"][0].npts
curr_coll_vector = pybamm.Vector(np.ones(cc_npts), domain="current collector")
a = 2 * pybamm.PrimaryBroadcast(curr_coll_vector, a_dom)
b = pybamm.PrimaryBroadcast(curr_coll_vector, b_dom)
c = 3 * pybamm.PrimaryBroadcast(curr_coll_vector, c_dom)
conc = pybamm.Concatenation(a, b, c)
disc.set_variable_slices([conc])
conc_disc = disc.process_symbol(conc)
jac = conc_disc.jac(y).evaluate().toarray()
np.testing.assert_array_equal(jac, np.zeros((1500, 1500)))
# Jacobian of a DomainConcatenation of StateVectors
a = pybamm.Variable("a", domain=a_dom)
b = pybamm.Variable("b", domain=b_dom)
c = pybamm.Variable("c", domain=c_dom)
conc = pybamm.Concatenation(a, b, c)
disc.set_variable_slices([conc])
conc_disc = disc.process_symbol(conc)
y0 = np.ones(1500)
jac = conc_disc.jac(y).evaluate(y=y0).toarray()
np.testing.assert_array_equal(jac, np.eye(1500))
def preprocess_binary(left, right):
if isinstance(left, numbers.Number):
left = pybamm.Scalar(left)
if isinstance(right, numbers.Number):
right = pybamm.Scalar(right)
# Check both left and right are pybamm Symbols
if not (isinstance(left, pybamm.Symbol)
and isinstance(right, pybamm.Symbol)):
raise NotImplementedError(
"""BinaryOperator not implemented for symbols of type {} and {}""".
format(type(left), type(right)))
# Do some broadcasting in special cases, to avoid having to do this manually
if left.domain != [] and right.domain != []:
if (left.domain != right.domain
and "secondary" in right.auxiliary_domains
and left.domain == right.auxiliary_domains["secondary"]):
left = pybamm.PrimaryBroadcast(left, right.domain)
if (right.domain != left.domain
and "secondary" in left.auxiliary_domains
and right.domain == left.auxiliary_domains["secondary"]):
right = pybamm.PrimaryBroadcast(right, left.domain)
return left, right
def test_public_function(self):
param = pybamm.standard_parameters_lead_acid
a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
a_s = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["separator"])
a = pybamm.Scalar(0)
variables = {
"Current collector current density": a,
"Negative electrode potential": a_n,
"Negative electrolyte potential": a_n,
"Negative electrolyte concentration": a_n,
"Negative electrode temperature": a_n,
"X-averaged positive electrode oxygen interfacial current density": a,
"Separator tortuosity": a_s,
"Separator oxygen concentration": a_s,
"Leading-order negative electrode oxygen interfacial current density": a,
}
submodel = pybamm.interface.DiffusionLimited(
param, "Negative", "lead-acid oxygen", "leading"
)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
submodel = pybamm.interface.DiffusionLimited(
param, "Negative", "lead-acid oxygen", "composite"
)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
submodel = pybamm.interface.DiffusionLimited(
param, "Negative", "lead-acid oxygen", "full"
)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
def test_yz_average(self):
a = pybamm.Scalar(1)
z_average_a = pybamm.z_average(a)
yz_average_a = pybamm.yz_average(a)
self.assertEqual(z_average_a.id, a.id)
self.assertEqual(yz_average_a.id, a.id)
z_average_broad_a = pybamm.z_average(
pybamm.PrimaryBroadcast(a, ["current collector"]))
yz_average_broad_a = pybamm.yz_average(
pybamm.PrimaryBroadcast(a, ["current collector"]))
self.assertEqual(z_average_broad_a.evaluate(), np.array([1]))
self.assertEqual(yz_average_broad_a.evaluate(), np.array([1]))
a = pybamm.Variable("a", domain=["current collector"])
y = pybamm.SpatialVariable("y", ["current collector"])
z = pybamm.SpatialVariable("z", ["current collector"])
z_av_a = pybamm.z_average(a)
yz_av_a = pybamm.yz_average(a)
self.assertIsInstance(yz_av_a, pybamm.Division)
self.assertIsInstance(z_av_a, pybamm.Division)
self.assertIsInstance(z_av_a.children[0], pybamm.Integral)
self.assertIsInstance(yz_av_a.children[0], pybamm.Integral)
self.assertEqual(z_av_a.children[0].integration_variable[0].domain,
z.domain)
self.assertEqual(yz_av_a.children[0].integration_variable[0].domain,
y.domain)
self.assertEqual(yz_av_a.children[0].integration_variable[1].domain,
z.domain)
self.assertIsInstance(z_av_a.children[1], pybamm.Integral)
self.assertIsInstance(yz_av_a.children[1], pybamm.Integral)
self.assertEqual(z_av_a.children[1].integration_variable[0].domain,
a.domain)
self.assertEqual(z_av_a.children[1].children[0].id,
pybamm.ones_like(a).id)
self.assertEqual(yz_av_a.children[1].integration_variable[0].domain,
y.domain)
self.assertEqual(yz_av_a.children[1].integration_variable[0].domain,
z.domain)
self.assertEqual(yz_av_a.children[1].children[0].id,
pybamm.ones_like(a).id)
self.assertEqual(z_av_a.domain, [])
self.assertEqual(yz_av_a.domain, [])
a = pybamm.Symbol("a", domain="bad domain")
with self.assertRaises(pybamm.DomainError):
pybamm.z_average(a)
with self.assertRaises(pybamm.DomainError):
pybamm.yz_average(a)
# average of symbol that evaluates on edges raises error
symbol_on_edges = pybamm.PrimaryBroadcastToEdges(1, "domain")
with self.assertRaisesRegex(
ValueError,
"Can't take the z-average of a symbol that evaluates on edges"
):
pybamm.z_average(symbol_on_edges)
def get_fundamental_variables(self):
c_ox_av = pybamm.Variable(
"X-averaged oxygen concentration", domain="current collector"
)
c_ox_n = pybamm.PrimaryBroadcast(c_ox_av, "negative electrode")
c_ox_s = pybamm.PrimaryBroadcast(c_ox_av, "separator")
c_ox_p = pybamm.PrimaryBroadcast(c_ox_av, "positive electrode")
return self._get_standard_concentration_variables(c_ox_n, c_ox_s, c_ox_p)
def get_coupled_variables(self, variables):
# Set up
param = self.param
l_n = param.l_n
x_n = pybamm.standard_spatial_vars.x_n
x_s = pybamm.standard_spatial_vars.x_s
x_p = pybamm.standard_spatial_vars.x_p
p_s = variables["X-averaged separator pressure"]
j_n_av = variables["X-averaged negative electrode interfacial current density"]
j_p_av = variables["X-averaged positive electrode interfacial current density"]
p_n = param.beta_n * j_n_av * (-(x_n ** 2) + param.l_n ** 2) / 2 + p_s
p_p = param.beta_n * j_n_av * ((x_p - 1) ** 2 - param.l_p ** 2) / 2 + p_s
variables.update(self._get_standard_neg_pos_pressure_variables(p_n, p_p))
# Volume-averaged velocity
v_box_n = param.beta_n * j_n_av * x_n
v_box_p = param.beta_p * j_p_av * (x_p - 1)
variables.update(
self._get_standard_neg_pos_velocity_variables(v_box_n, v_box_p)
)
div_v_box_n = pybamm.PrimaryBroadcast(
param.beta_n * j_n_av, "negative electrode"
)
div_v_box_p = pybamm.PrimaryBroadcast(
param.beta_p * j_p_av, "positive electrode"
)
variables.update(
self._get_standard_neg_pos_acceleration_variables(div_v_box_n, div_v_box_p)
)
# Transverse velocity in the separator determines through-cell velocity
div_Vbox_s = variables[
"X-averaged separator transverse volume-averaged acceleration"
]
i_boundary_cc = variables["Current collector current density"]
v_box_n_right = param.beta_n * i_boundary_cc
div_v_box_s_av = -div_Vbox_s
div_v_box_s = pybamm.PrimaryBroadcast(div_v_box_s_av, "separator")
# Simple formula for velocity in the separator
v_box_s = div_v_box_s_av * (x_s - l_n) + v_box_n_right
variables.update(
self._get_standard_sep_velocity_variables(v_box_s, div_v_box_s)
)
variables.update(self._get_standard_whole_cell_velocity_variables(variables))
variables.update(
self._get_standard_whole_cell_acceleration_variables(variables)
)
variables.update(self._get_standard_whole_cell_pressure_variables(variables))
return variables
def test_public_functions(self):
param = pybamm.standard_parameters_lead_acid
a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
variables = {
"Negative electrode interfacial current density": a_n,
"Positive electrode interfacial current density": a_p,
}
submodel = pybamm.porosity.Full(param)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
def test_public_functions(self):
param = pybamm.standard_parameters_lithium_ion
a = pybamm.Scalar(0)
a_n = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["negative electrode"])
a_s = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["separator"])
a_p = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["positive electrode"])
variables = {
"Current collector current density": a,
"Negative electrode porosity": a_n,
"Negative electrolyte concentration": a_n,
"Negative electrode interfacial current density": a_n,
"X-averaged negative electrode interfacial current density": a,
"X-averaged negative electrode total interfacial current density":
a,
}
icd = " interfacial current density"
reactions = {
"main": {
"Negative": {
"s": 1,
"aj": "Negative electrode" + icd
},
"Positive": {
"s": 1,
"aj": "Positive electrode" + icd
},
}
}
spf = pybamm.electrolyte.stefan_maxwell.conductivity.surface_potential_form
submodel = spf.LeadingOrderAlgebraic(param, "Negative", reactions)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
submodel = spf.LeadingOrderDifferential(param, "Negative", reactions)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
variables = {
"Current collector current density": a,
"Negative electrolyte potential": a_n,
"Negative electrolyte current density": a_n,
"Separator electrolyte potential": a_s,
"Separator electrolyte current density": a_s,
"Positive electrode porosity": a_p,
"Positive electrolyte concentration": a_p,
"X-averaged positive electrode interfacial current density": a,
"X-averaged positive electrode total interfacial current density":
a,
}
submodel = spf.LeadingOrderAlgebraic(param, "Positive", reactions)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
submodel = spf.LeadingOrderDifferential(param, "Positive", reactions)
std_tests = tests.StandardSubModelTests(submodel, variables)
std_tests.test_all()
def get_fundamental_variables(self):
eps_n_pc = pybamm.standard_variables.eps_n_pc
eps_s_pc = pybamm.standard_variables.eps_s_pc
eps_p_pc = pybamm.standard_variables.eps_p_pc
eps_n = pybamm.PrimaryBroadcast(eps_n_pc, "negative electrode")
eps_s = pybamm.PrimaryBroadcast(eps_s_pc, "separator")
eps_p = pybamm.PrimaryBroadcast(eps_p_pc, "positive electrode")
variables = self._get_standard_porosity_variables(eps_n, eps_s, eps_p)
return variables
def get_fundamental_variables(self):
if self.domain == "Negative":
c_s_xav = pybamm.standard_variables.c_s_n_xav
c_s = pybamm.PrimaryBroadcast(c_s_xav, ["negative electrode"])
elif self.domain == "Positive":
c_s_xav = pybamm.standard_variables.c_s_p_xav
c_s = pybamm.PrimaryBroadcast(c_s_xav, ["positive electrode"])
variables = self._get_standard_concentration_variables(c_s, c_s_xav)
return variables
def test_x_average(self):
a = pybamm.Scalar(1)
average_a = pybamm.x_average(a)
self.assertEqual(average_a.id, a.id)
average_broad_a = pybamm.x_average(
pybamm.PrimaryBroadcast(a, ["negative electrode"]))
self.assertEqual(average_broad_a.evaluate(), np.array([1]))
conc_broad = pybamm.Concatenation(
pybamm.PrimaryBroadcast(1, ["negative electrode"]),
pybamm.PrimaryBroadcast(2, ["separator"]),
pybamm.PrimaryBroadcast(3, ["positive electrode"]),
)
average_conc_broad = pybamm.x_average(conc_broad)
self.assertIsInstance(average_conc_broad, pybamm.Division)
for domain in [
["negative electrode"],
["separator"],
["positive electrode"],
["negative electrode", "separator", "positive electrode"],
]:
a = pybamm.Symbol("a", domain=domain)
x = pybamm.SpatialVariable("x", domain)
av_a = pybamm.x_average(a)
self.assertIsInstance(av_a, pybamm.Division)
self.assertIsInstance(av_a.children[0], pybamm.Integral)
self.assertEqual(av_a.children[0].integration_variable[0].domain,
x.domain)
self.assertEqual(av_a.domain, [])
a = pybamm.Symbol("a", domain="new domain")
av_a = pybamm.x_average(a)
self.assertEqual(av_a.domain, [])
self.assertIsInstance(av_a, pybamm.Division)
self.assertIsInstance(av_a.children[0], pybamm.Integral)
self.assertEqual(av_a.children[0].integration_variable[0].domain,
a.domain)
self.assertIsInstance(av_a.children[1], pybamm.Integral)
self.assertEqual(av_a.children[1].integration_variable[0].domain,
a.domain)
self.assertEqual(av_a.children[1].children[0].id,
pybamm.ones_like(a).id)
# x-average of symbol that evaluates on edges raises error
symbol_on_edges = pybamm.PrimaryBroadcastToEdges(1, "domain")
with self.assertRaisesRegex(
ValueError,
"Can't take the x-average of a symbol that evaluates on edges"
):
pybamm.x_average(symbol_on_edges)
def get_fundamental_variables(self):
T_x_av = pybamm.standard_variables.T_av
T_n = pybamm.PrimaryBroadcast(T_x_av, "negative electrode")
T_s = pybamm.PrimaryBroadcast(T_x_av, "separator")
T_p = pybamm.PrimaryBroadcast(T_x_av, "positive electrode")
T = pybamm.Concatenation(T_n, T_s, T_p)
T_cn = T_x_av
T_cp = T_x_av
variables = self._get_standard_fundamental_variables(T, T_cn, T_cp)
return variables
def get_fundamental_variables(self):
T_amb = self.param.T_amb(pybamm.t * self.param.timescale)
T_x_av = pybamm.PrimaryBroadcast(T_amb, "current collector")
T_vol_av = pybamm.PrimaryBroadcast(T_amb, "current collector")
T_cn = T_x_av
T_n = pybamm.PrimaryBroadcast(T_x_av, "negative electrode")
T_s = pybamm.PrimaryBroadcast(T_x_av, "separator")
T_p = pybamm.PrimaryBroadcast(T_x_av, "positive electrode")
T_cp = T_x_av
variables = self._get_standard_fundamental_variables(
T_cn, T_n, T_s, T_p, T_cp, T_x_av, T_vol_av)
return variables
def set_initial_conditions(self, variables):
c_s = variables[self.domain + " particle concentration"]
if self.domain == "Negative":
x_n = pybamm.PrimaryBroadcast(pybamm.standard_spatial_vars.x_n,
"negative particle")
c_init = self.param.c_n_init(x_n)
elif self.domain == "Positive":
x_p = pybamm.PrimaryBroadcast(pybamm.standard_spatial_vars.x_p,
"positive particle")
c_init = self.param.c_p_init(x_p)
self.initial_conditions = {c_s: c_init}
def get_coupled_variables(self, variables):
param = self.param
l_n = param.l_n
l_s = param.l_s
l_p = param.l_p
x_s = pybamm.standard_spatial_vars.x_s
x_p = pybamm.standard_spatial_vars.x_p
# Unpack
eps_s_0_av = variables["Leading-order x-averaged separator porosity"]
eps_p_0_av = variables[
"Leading-order x-averaged positive electrode porosity"]
# Diffusivities
D_ox_s = (eps_s_0_av**param.b_s) * param.curlyD_ox
D_ox_p = (eps_p_0_av**param.b_p) * param.curlyD_ox
# Reactions
sj_ox_p = sum(reaction["Positive"]["s_ox"] *
variables["Leading-order x-averaged " +
reaction["Positive"]["aj"].lower()]
for reaction in self.reactions.values())
# Fluxes
N_ox_n_1 = pybamm.FullBroadcast(0, "negative electrode",
"current collector")
N_ox_s_1 = -pybamm.PrimaryBroadcast(sj_ox_p * l_p, "separator")
N_ox_p_1 = pybamm.outer(sj_ox_p, x_p - 1)
# Concentrations
c_ox_n_1 = pybamm.FullBroadcast(0, "negative electrode",
"current collector")
c_ox_s_1 = pybamm.outer(sj_ox_p * l_p / D_ox_s, x_s - l_n)
c_ox_p_1 = pybamm.outer(
-sj_ox_p / (2 * D_ox_p),
(x_p - 1)**2 - l_p**2) + pybamm.PrimaryBroadcast(
sj_ox_p * l_p * l_s / D_ox_s, "positive electrode")
# Update variables
c_ox = pybamm.Concatenation(param.C_e * c_ox_n_1, param.C_e * c_ox_s_1,
param.C_e * c_ox_p_1)
variables.update(self._get_standard_concentration_variables(c_ox))
N_ox = pybamm.Concatenation(param.C_e * N_ox_n_1, param.C_e * N_ox_s_1,
param.C_e * N_ox_p_1)
variables.update(self._get_standard_flux_variables(N_ox))
return variables
def get_fundamental_variables(self):
T_x_av = pybamm.standard_variables.T_av
T_vol_av = self._yz_average(T_x_av)
T_cn = T_x_av
T_n = pybamm.PrimaryBroadcast(T_x_av, "negative electrode")
T_s = pybamm.PrimaryBroadcast(T_x_av, "separator")
T_p = pybamm.PrimaryBroadcast(T_x_av, "positive electrode")
T_cp = T_x_av
variables = self._get_standard_fundamental_variables(
T_cn, T_n, T_s, T_p, T_cp, T_x_av, T_vol_av)
return variables
def test_public_functions(self):
param = pybamm.standard_parameters_lithium_ion
phi_s_cn = pybamm.PrimaryBroadcast(pybamm.Scalar(0), ["current collector"])
phi_s_cp = pybamm.PrimaryBroadcast(pybamm.Scalar(3), ["current collector"])
coupled_variables.update(
{
"Negative current collector potential": phi_s_cn,
"Positive current collector potential": phi_s_cp,
}
)
submodel = pybamm.thermal.x_lumped.SetTemperature1D(param)
std_tests = tests.StandardSubModelTests(submodel, coupled_variables)
std_tests.test_all()
def get_coupled_variables(self, variables):
# Unpack
c_e_0 = variables["Leading-order " + self.domain.lower() +
" electrolyte concentration"]
c_e = variables[self.domain + " electrolyte concentration"]
c_e_1 = (c_e - c_e_0) / self.param.C_e
dj_dc_0 = self._get_dj_dc(variables)
dj_ddeltaphi_0 = self._get_dj_ddeltaphi(variables)
# Update delta_phi with new phi_e and phi_s
variables = self._get_delta_phi(variables)
delta_phi_0 = variables["Leading-order " + self.domain.lower() +
" electrode surface potential difference"]
delta_phi = variables[self.domain +
" electrode surface potential difference"]
delta_phi_1 = (delta_phi - delta_phi_0) / self.param.C_e
j_0 = variables["Leading-order " + self.domain.lower() + " electrode" +
self.reaction_name + " interfacial current density"]
j_1 = (pybamm.PrimaryBroadcast(dj_dc_0, self.domain_for_broadcast) *
c_e_1 + pybamm.PrimaryBroadcast(
dj_ddeltaphi_0, self.domain_for_broadcast) * delta_phi_1)
j = j_0 + self.param.C_e * j_1
# Get exchange-current density
j0 = self._get_exchange_current_density(variables)
# Get open-circuit potential variables and reaction overpotential
ocp, dUdT = self._get_open_circuit_potential(variables)
eta_r = delta_phi - ocp
variables.update(self._get_standard_interfacial_current_variables(j))
variables.update(self._get_standard_exchange_current_variables(j0))
variables.update(self._get_standard_overpotential_variables(eta_r))
variables.update(self._get_standard_ocp_variables(ocp, dUdT))
if ("Negative electrode" + self.reaction_name +
" interfacial current density" in variables
and "Positive electrode" + self.reaction_name +
" interfacial current density" in variables):
variables.update(
self._get_standard_whole_cell_interfacial_current_variables(
variables))
variables.update(
self._get_standard_whole_cell_exchange_current_variables(
variables))
return variables
def _separator_velocity(self, variables):
"""
A private method to calculate x- and z-components of velocity in the separator
Parameters
----------
variables : dict
Dictionary of variables in the whole model.
Returns
-------
v_box_s : :class:`pybamm.Symbol`
The x-component of velocity in the separator
dVbox_dz : :class:`pybamm.Symbol`
The z-component of velocity in the separator
"""
# Set up
param = self.param
l_n = pybamm.geometric_parameters.l_n
l_s = pybamm.geometric_parameters.l_s
x_s = pybamm.standard_spatial_vars.x_s
# Difference in negative and positive electrode velocities determines the
# velocity in the separator
i_boundary_cc = variables["Current collector current density"]
v_box_n_right = param.beta_n * i_boundary_cc
v_box_p_left = param.beta_p * i_boundary_cc
d_vbox_s__dx = (v_box_p_left - v_box_n_right) / l_s
# Simple formula for velocity in the separator
dVbox_dz = pybamm.Concatenation(
pybamm.FullBroadcast(
0,
"negative electrode",
auxiliary_domains={"secondary": "current collector"},
),
pybamm.PrimaryBroadcast(-d_vbox_s__dx, "separator"),
pybamm.FullBroadcast(
0,
"positive electrode",
auxiliary_domains={"secondary": "current collector"},
),
)
v_box_s = pybamm.outer(d_vbox_s__dx,
(x_s - l_n)) + pybamm.PrimaryBroadcast(
v_box_n_right, "separator")
return v_box_s, dVbox_dz