def _get_neg_pos_coupled_variables(self, variables):
"""
A private function to get the coupled variables when the domain is 'Negative'
or 'Positive'.
"""
param = self.param
conductivity, sigma_eff = self._get_conductivities(variables)
i_boundary_cc = variables["Current collector current density"]
c_e = variables[self.domain + " electrolyte concentration"]
delta_phi = variables[self.domain +
" electrode surface potential difference"]
T = variables[self.domain + " electrode temperature"]
i_e = conductivity * (
((1 + param.Theta * T) * param.chi(c_e) / c_e) * pybamm.grad(c_e) +
pybamm.grad(delta_phi) + i_boundary_cc / sigma_eff)
variables.update(self._get_domain_current_variables(i_e))
# TODO: Expression can be written in a form which does not require phi_s and
# so avoid this hack.
phi_s = self.nasty_hack_to_get_phi_s(variables)
phi_e = phi_s - delta_phi
variables.update(self._get_domain_potential_variables(phi_e))
variables.update({"test": pybamm.x_average(phi_s)})
return variables
def test_adding_1D_external_variable(self):
model = pybamm.BaseModel()
a = pybamm.Variable("a", domain=["test"])
b = pybamm.Variable("b", domain=["test"])
model.rhs = {a: a * b}
model.boundary_conditions = {
a: {
"left": (0, "Dirichlet"),
"right": (0, "Dirichlet")
}
}
model.initial_conditions = {a: 0}
model.external_variables = [b]
model.variables = {
"a": a,
"b": b,
"c": a * b,
"grad b": pybamm.grad(b),
"div grad b": pybamm.div(pybamm.grad(b)),
}
x = pybamm.SpatialVariable("x", domain="test", coord_sys="cartesian")
geometry = {
"test": {
"primary": {
x: {
"min": pybamm.Scalar(0),
"max": pybamm.Scalar(1)
}
}
}
}
submesh_types = {"test": pybamm.MeshGenerator(pybamm.Uniform1DSubMesh)}
var_pts = {x: 10}
mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
spatial_methods = {"test": pybamm.FiniteVolume()}
disc = pybamm.Discretisation(mesh, spatial_methods)
disc.process_model(model)
self.assertEqual(disc.y_slices[a.id][0], slice(0, 10, None))
self.assertEqual(model.y_slices[a][0], slice(0, 10, None))
b_test = np.ones((10, 1))
np.testing.assert_array_equal(
model.variables["b"].evaluate(inputs={"b": b_test}), b_test)
# check that b is added to the boundary conditions
model.bcs[b.id]["left"]
model.bcs[b.id]["right"]
# check that grad and div(grad ) produce the correct shapes
self.assertEqual(model.variables["b"].shape_for_testing, (10, 1))
self.assertEqual(model.variables["grad b"].shape_for_testing, (11, 1))
self.assertEqual(model.variables["div grad b"].shape_for_testing,
(10, 1))
def get_fundamental_variables(self):
# Electrolyte pressure
p_n = pybamm.Variable(
"Negative electrode pressure",
domain="negative electrode",
auxiliary_domains={"secondary": "current collector"},
)
p_p = pybamm.Variable(
"Positive electrode pressure",
domain="positive electrode",
auxiliary_domains={"secondary": "current collector"},
)
variables = self._get_standard_neg_pos_pressure_variables(p_n, p_p)
# TODO: add permeability and viscosity, and other terms
v_mass_n = -pybamm.grad(p_n)
v_mass_p = -pybamm.grad(p_p)
v_box_n = v_mass_n
v_box_p = v_mass_p
variables.update(
self._get_standard_neg_pos_velocity_variables(v_box_n, v_box_p))
div_v_box_n = pybamm.div(v_box_n)
div_v_box_p = pybamm.div(v_box_p)
variables.update(
self._get_standard_neg_pos_acceleration_variables(
div_v_box_n, div_v_box_p))
return variables
def test_exceptions(self):
c_n = pybamm.Variable("c", domain=["negative electrode"])
N_n = pybamm.grad(c_n)
c_s = pybamm.Variable("c", domain=["separator"])
N_s = pybamm.grad(c_s)
model = pybamm.BaseModel()
model.rhs = {c_n: pybamm.div(N_n), c_s: pybamm.div(N_s)}
model.initial_conditions = {c_n: pybamm.Scalar(3), c_s: pybamm.Scalar(1)}
model.boundary_conditions = {
c_n: {"left": (0, "Neumann"), "right": (0, "Neumann")},
c_s: {"left": (0, "Neumann"), "right": (0, "Neumann")},
}
disc = get_discretisation_for_testing()
# check raises error if different sized key and output var
model.variables = {c_n.name: c_s}
with self.assertRaisesRegex(pybamm.ModelError, "variable and its eqn"):
disc.process_model(model)
# check doesn't raise if concatenation
model.variables = {c_n.name: pybamm.Concatenation(c_n, c_s)}
disc.process_model(model, inplace=False)
# check doesn't raise if broadcast
model.variables = {
c_n.name: pybamm.PrimaryBroadcast(
pybamm.InputParameter("a"), ["negative electrode"]
)
}
disc.process_model(model)
def get_coupled_variables(self, variables):
param = self.param
if self.domain in ["Negative", "Positive"]:
conductivity, sigma_eff = self._get_conductivities(variables)
i_boundary_cc = variables["Current collector current density"]
c_e = variables[self.domain + " electrolyte concentration"]
delta_phi = variables[
self.domain + " electrode surface potential difference"
]
T = variables[self.domain + " electrode temperature"]
i_e = conductivity * (
((1 + param.Theta * T) * param.chi(c_e, T) / c_e) * pybamm.grad(c_e)
+ pybamm.grad(delta_phi)
+ i_boundary_cc / sigma_eff
)
variables.update(self._get_domain_current_variables(i_e))
phi_s = variables[self.domain + " electrode potential"]
phi_e = phi_s - delta_phi
variables.update(self._get_domain_potential_variables(phi_e))
elif self.domain == "Separator":
x_s = pybamm.standard_spatial_vars.x_s
i_boundary_cc = variables["Current collector current density"]
c_e_s = variables["Separator electrolyte concentration"]
phi_e_n = variables["Negative electrolyte potential"]
tor_s = variables["Separator porosity"]
T = variables["Separator temperature"]
chi_e_s = param.chi(c_e_s, T)
kappa_s_eff = param.kappa_e(c_e_s, T) * tor_s
phi_e_s = pybamm.boundary_value(
phi_e_n, "right"
) + pybamm.IndefiniteIntegral(
(1 + param.Theta * T) * chi_e_s / c_e_s * pybamm.grad(c_e_s)
- param.C_e * i_boundary_cc / kappa_s_eff,
x_s,
)
i_e_s = pybamm.PrimaryBroadcast(i_boundary_cc, "separator")
variables.update(self._get_domain_potential_variables(phi_e_s))
variables.update(self._get_domain_current_variables(i_e_s))
# Update boundary conditions (for indefinite integral)
self.boundary_conditions[c_e_s] = {
"left": (pybamm.BoundaryGradient(c_e_s, "left"), "Neumann"),
"right": (pybamm.BoundaryGradient(c_e_s, "right"), "Neumann"),
}
if self.domain == "Positive":
variables.update(self._get_whole_cell_variables(variables))
return variables
def _current_collector_heating(self, variables):
"Compute Ohmic heating in current collectors"
# TODO: implement grad in 0D to return a scalar zero
# TODO: implement grad_squared in other spatial methods so that the if
# statement can be removed
# In the limit of infinitely large current collector conductivity (i.e.
# 0D current collectors), the Ohmic heating in the current collectors is
# zero
if self.cc_dimension == 0:
Q_s_cn = pybamm.Scalar(0)
Q_s_cp = pybamm.Scalar(0)
# Otherwise we compute the Ohmic heating for 1 or 2D current collectors
elif self.cc_dimension in [1, 2]:
phi_s_cn = variables["Negative current collector potential"]
phi_s_cp = variables["Positive current collector potential"]
if self.cc_dimension == 1:
Q_s_cn = self.param.sigma_cn_prime * pybamm.inner(
pybamm.grad(phi_s_cn), pybamm.grad(phi_s_cn))
Q_s_cp = self.param.sigma_cp_prime * pybamm.inner(
pybamm.grad(phi_s_cp), pybamm.grad(phi_s_cp))
elif self.cc_dimension == 2:
# Inner not implemented in 2D -- have to call grad_squared directly
Q_s_cn = self.param.sigma_cn_prime * pybamm.grad_squared(
phi_s_cn)
Q_s_cp = self.param.sigma_cp_prime * pybamm.grad_squared(
phi_s_cp)
return Q_s_cn, Q_s_cp
def get_coupled_variables(self, variables):
c_s = variables[self.domain + " particle concentration"]
T_k = pybamm.PrimaryBroadcast(
variables[self.domain + " electrode temperature"],
[self.domain.lower() + " particle"],
)
if self.domain == "Negative":
N_s = -self.param.D_n(c_s, T_k) * pybamm.grad(c_s)
elif self.domain == "Positive":
N_s = -self.param.D_p(c_s, T_k) * pybamm.grad(c_s)
variables.update(self._get_standard_flux_variables(N_s, N_s))
if self.domain == "Negative":
x = pybamm.standard_spatial_vars.x_n
R = pybamm.FunctionParameter(
"Negative particle distribution in x",
{"Dimensionless through-cell position (x_n)": x},
)
variables.update({"Negative particle distribution in x": R})
elif self.domain == "Positive":
x = pybamm.standard_spatial_vars.x_p
R = pybamm.FunctionParameter(
"Positive particle distribution in x",
{"Dimensionless through-cell position (x_p)": x},
)
variables.update({"Positive particle distribution in x": R})
return variables
def test_grad_div_with_bcs_on_tab(self):
# 2d macroscale
mesh = get_1p1d_mesh_for_testing()
spatial_methods = {
"macroscale": pybamm.FiniteVolume(),
"negative particle": pybamm.FiniteVolume(),
"positive particle": pybamm.FiniteVolume(),
"current collector": pybamm.FiniteVolume(),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
y_test = np.ones(mesh["current collector"][0].npts)
# var
var = pybamm.Variable("var", domain="current collector")
disc.set_variable_slices([var])
# grad
grad_eqn = pybamm.grad(var)
# div
N = pybamm.grad(var)
div_eqn = pybamm.div(N)
# bcs (on each tab)
boundary_conditions = {
var.id: {
"negative tab": (pybamm.Scalar(1), "Dirichlet"),
"positive tab": (pybamm.Scalar(0), "Neumann"),
}
}
disc.bcs = boundary_conditions
grad_eqn_disc = disc.process_symbol(grad_eqn)
grad_eqn_disc.evaluate(None, y_test)
div_eqn_disc = disc.process_symbol(div_eqn)
div_eqn_disc.evaluate(None, y_test)
# bcs (one pos, one not tab)
boundary_conditions = {
var.id: {
"no tab": (pybamm.Scalar(1), "Dirichlet"),
"positive tab": (pybamm.Scalar(0), "Dirichlet"),
}
}
disc.bcs = boundary_conditions
grad_eqn_disc = disc.process_symbol(grad_eqn)
grad_eqn_disc.evaluate(None, y_test)
div_eqn_disc = disc.process_symbol(div_eqn)
div_eqn_disc.evaluate(None, y_test)
# bcs (one neg, one not tab)
boundary_conditions = {
var.id: {
"negative tab": (pybamm.Scalar(1), "Neumann"),
"no tab": (pybamm.Scalar(0), "Neumann"),
}
}
disc.bcs = boundary_conditions
grad_eqn_disc = disc.process_symbol(grad_eqn)
grad_eqn_disc.evaluate(None, y_test)
div_eqn_disc = disc.process_symbol(div_eqn)
div_eqn_disc.evaluate(None, y_test)
def test_processed_variable_ode_pde_solution(self):
# without space
model = pybamm.BaseBatteryModel()
c = pybamm.Variable("conc")
model.rhs = {c: -c}
model.initial_conditions = {c: 1}
model.variables = {"c": c}
modeltest = tests.StandardModelTest(model)
modeltest.test_all()
t_sol, y_sol = modeltest.solution.t, modeltest.solution.y
processed_vars = pybamm.post_process_variables(model.variables, t_sol,
y_sol)
np.testing.assert_array_almost_equal(processed_vars["c"](t_sol),
np.exp(-t_sol))
# with space
# set up and solve model
whole_cell = ["negative electrode", "separator", "positive electrode"]
model = pybamm.BaseBatteryModel()
c = pybamm.Variable("conc", domain=whole_cell)
c_s = pybamm.Variable(
"particle conc",
domain="negative particle",
auxiliary_domains={"secondary": ["negative electrode"]},
)
model.rhs = {c: -c, c_s: 1 - c_s}
model.initial_conditions = {c: 1, c_s: 0.5}
model.boundary_conditions = {
c: {
"left": (0, "Neumann"),
"right": (0, "Neumann")
},
c_s: {
"left": (0, "Neumann"),
"right": (0, "Neumann")
},
}
model.variables = {
"c": c,
"N": pybamm.grad(c),
"c_s": c_s,
"N_s": pybamm.grad(c_s),
}
modeltest = tests.StandardModelTest(model)
modeltest.test_all()
# set up testing
t_sol, y_sol = modeltest.solution.t, modeltest.solution.y
x = pybamm.SpatialVariable("x", domain=whole_cell)
x_sol = modeltest.disc.process_symbol(x).entries[:, 0]
processed_vars = pybamm.post_process_variables(model.variables, t_sol,
y_sol,
modeltest.disc.mesh)
# test
np.testing.assert_array_almost_equal(
processed_vars["c"](t_sol, x_sol),
np.ones_like(x_sol)[:, np.newaxis] * np.exp(-t_sol),
)
def test_grad_div_shapes_mixed_domain(self):
"""
Test grad and div with Dirichlet boundary conditions (applied by grad on var)
"""
# create discretisation
mesh = get_mesh_for_testing()
spatial_methods = {"macroscale": pybamm.SpectralVolume()}
disc = pybamm.Discretisation(mesh, spatial_methods)
# grad
var = pybamm.Variable("var",
domain=["negative electrode", "separator"])
grad_eqn = pybamm.grad(var)
boundary_conditions = {
var.id: {
"left": (pybamm.Scalar(1), "Dirichlet"),
"right": (pybamm.Scalar(1), "Dirichlet"),
}
}
disc.bcs = boundary_conditions
disc.set_variable_slices([var])
grad_eqn_disc = disc.process_symbol(grad_eqn)
combined_submesh = mesh.combine_submeshes("negative electrode",
"separator")
constant_y = np.ones_like(combined_submesh.nodes[:, np.newaxis])
np.testing.assert_array_almost_equal(
grad_eqn_disc.evaluate(None, constant_y),
np.zeros_like(combined_submesh.edges[:, np.newaxis]),
)
# div: test on linear y (should have laplacian zero) so change bcs
linear_y = combined_submesh.nodes
N = pybamm.grad(var)
div_eqn = pybamm.div(N)
boundary_conditions = {
var.id: {
"left": (pybamm.Scalar(0), "Dirichlet"),
"right":
(pybamm.Scalar(combined_submesh.edges[-1]), "Dirichlet"),
}
}
disc.bcs = boundary_conditions
grad_eqn_disc = disc.process_symbol(grad_eqn)
np.testing.assert_array_almost_equal(
grad_eqn_disc.evaluate(None, linear_y),
np.ones_like(combined_submesh.edges[:, np.newaxis]),
)
div_eqn_disc = disc.process_symbol(div_eqn)
np.testing.assert_array_almost_equal(
div_eqn_disc.evaluate(None, linear_y),
np.zeros_like(combined_submesh.nodes[:, np.newaxis]),
)
def _current_collector_heating(self, variables):
"""Returns the heat source terms in the 1D current collector"""
phi_s_cn = variables["Negative current collector potential"]
phi_s_cp = variables["Positive current collector potential"]
Q_s_cn = self.param.sigma_cn_prime * pybamm.inner(
pybamm.grad(phi_s_cn), pybamm.grad(phi_s_cn))
Q_s_cp = self.param.sigma_cp_prime * pybamm.inner(
pybamm.grad(phi_s_cp), pybamm.grad(phi_s_cp))
return Q_s_cn, Q_s_cp
def test_symbol_repr(self):
"""
test that __repr___ returns the string
`__class__(id, name, parent expression)`
"""
a = pybamm.Symbol("a")
b = pybamm.Symbol("b")
c = pybamm.Symbol("c", domain=["test"])
d = pybamm.Symbol("d",
domain=["test"],
auxiliary_domains={"sec": "other test"})
hex_regex = r"\-?0x[0-9,a-f]+"
self.assertRegex(
a.__repr__(),
r"Symbol\(" + hex_regex +
r", a, children\=\[\], domain\=\[\], auxiliary_domains\=\{\}\)",
)
self.assertRegex(
b.__repr__(),
r"Symbol\(" + hex_regex +
r", b, children\=\[\], domain\=\[\], auxiliary_domains\=\{\}\)",
)
self.assertRegex(
c.__repr__(),
r"Symbol\(" + hex_regex +
r", c, children\=\[\], domain\=\['test'\], auxiliary_domains\=\{\}\)",
)
self.assertRegex(
d.__repr__(),
r"Symbol\(" + hex_regex +
r", d, children\=\[\], domain\=\['test'\]" +
r", auxiliary_domains\=\{'sec': \"\['other test'\]\"\}\)",
)
self.assertRegex(
(a + b).__repr__(),
r"Addition\(" + hex_regex +
r", \+, children\=\['a', 'b'\], domain=\[\]",
)
self.assertRegex(
(c * d).__repr__(),
r"Multiplication\(" + hex_regex +
r", \*, children\=\['c', 'd'\], domain=\['test'\]" +
r", auxiliary_domains\=\{'sec': \"\['other test'\]\"\}\)",
)
self.assertRegex(
pybamm.grad(a).__repr__(),
r"Gradient\(" + hex_regex +
r", grad, children\=\['a'\], domain=\[\], auxiliary_domains\=\{\}\)",
)
self.assertRegex(
pybamm.grad(c).__repr__(),
r"Gradient\(" + hex_regex +
r", grad, children\=\['c'\], domain=\['test'\]" +
r", auxiliary_domains\=\{\}\)",
)
def test_symbol_new_copy(self):
a = pybamm.Parameter("a")
b = pybamm.Parameter("b")
v_n = pybamm.Variable("v", "negative electrode")
x_n = pybamm.standard_spatial_vars.x_n
v_s = pybamm.Variable("v", "separator")
vec = pybamm.Vector([1, 2, 3, 4, 5])
mat = pybamm.Matrix([[1, 2], [3, 4]])
mesh = get_mesh_for_testing()
for symbol in [
a + b,
a - b,
a * b,
a / b,
a**b,
-a,
abs(a),
pybamm.Function(np.sin, a),
pybamm.FunctionParameter("function", {"a": a}),
pybamm.grad(v_n),
pybamm.div(pybamm.grad(v_n)),
pybamm.upwind(v_n),
pybamm.IndefiniteIntegral(v_n, x_n),
pybamm.BackwardIndefiniteIntegral(v_n, x_n),
pybamm.BoundaryValue(v_n, "right"),
pybamm.BoundaryGradient(v_n, "right"),
pybamm.PrimaryBroadcast(a, "domain"),
pybamm.SecondaryBroadcast(v_n, "current collector"),
pybamm.FullBroadcast(a, "domain",
{"secondary": "other domain"}),
pybamm.concatenation(v_n, v_s),
pybamm.NumpyConcatenation(a, b, v_s),
pybamm.DomainConcatenation([v_n, v_s], mesh),
pybamm.Parameter("param"),
pybamm.InputParameter("param"),
pybamm.StateVector(slice(0, 56)),
pybamm.Matrix(np.ones((50, 40))),
pybamm.SpatialVariable("x", ["negative electrode"]),
pybamm.t,
pybamm.Index(vec, 1),
pybamm.NotConstant(a),
pybamm.ExternalVariable(
"external variable",
20,
domain="test",
auxiliary_domains={"secondary": "test2"},
),
pybamm.minimum(a, b),
pybamm.maximum(a, b),
pybamm.SparseStack(mat, mat),
]:
self.assertEqual(symbol.id, symbol.new_copy().id)
def test_p2d_spherical_grad_div_shapes_Neumann_bcs(self):
"""
Test grad and div with Dirichlet boundary conditions (applied by grad on var)
in the pseudo 2-dimensional case
"""
mesh = get_p2d_mesh_for_testing()
spatial_methods = {"negative particle": pybamm.SpectralVolume()}
disc = pybamm.Discretisation(mesh, spatial_methods)
n_mesh = mesh["negative particle"]
mesh.add_ghost_meshes()
disc.mesh.add_ghost_meshes()
# test grad
var = pybamm.Variable(
"var",
domain=["negative particle"],
auxiliary_domains={"secondary": "negative electrode"},
)
grad_eqn = pybamm.grad(var)
disc.set_variable_slices([var])
grad_eqn_disc = disc.process_symbol(grad_eqn)
prim_pts = n_mesh.npts
sec_pts = mesh["negative electrode"].npts
constant_y = np.kron(np.ones(sec_pts), np.ones(prim_pts))
grad_eval = grad_eqn_disc.evaluate(None, constant_y)
grad_eval = np.reshape(grad_eval, [sec_pts, prim_pts + 1])
np.testing.assert_array_equal(grad_eval,
np.zeros([sec_pts, prim_pts + 1]))
# div
# div (grad r^2) = 6, N_left = N_right = 0
N = pybamm.grad(var)
div_eqn = pybamm.div(N)
boundary_conditions = {
var.id: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
disc.bcs = boundary_conditions
div_eqn_disc = disc.process_symbol(div_eqn)
const = 6 * np.ones(sec_pts * prim_pts)
div_eval = div_eqn_disc.evaluate(None, const)
div_eval = np.reshape(div_eval, [sec_pts, prim_pts])
np.testing.assert_array_almost_equal(div_eval,
np.zeros([sec_pts, prim_pts]))
def get_coupled_variables(self, variables):
param = self.param
T = variables["Cell temperature"]
eps = variables["Porosity"]
c_e = variables["Electrolyte concentration"]
phi_e = variables["Electrolyte potential"]
i_e = (param.kappa_e(c_e, T) * (eps**param.b) * param.gamma_e /
param.C_e) * (param.chi(c_e) * pybamm.grad(c_e) / c_e -
pybamm.grad(phi_e))
variables.update(self._get_standard_current_variables(i_e))
return variables
def test_check_well_posedness_initial_boundary_conditions(self):
# Well-posed model - Dirichlet
whole_cell = ["negative electrode", "separator", "positive electrode"]
model = pybamm.BaseModel()
c = pybamm.Variable("c", domain=whole_cell)
model.rhs = {c: 5 * pybamm.div(pybamm.grad(c)) - 1}
model.initial_conditions = {c: 1}
model.boundary_conditions = {
c: {
"left": (0, "Dirichlet"),
"right": (0, "Dirichlet")
}
}
model.check_well_posedness()
# Well-posed model - Neumann
model.boundary_conditions = {
c: {
"left": (0, "Neumann"),
"right": (0, "Neumann")
}
}
model.check_well_posedness()
# Model with bad initial conditions (expect assertion error)
d = pybamm.Variable("d", domain=whole_cell)
model.initial_conditions = {d: 3}
with self.assertRaisesRegex(pybamm.ModelError, "initial condition"):
model.check_well_posedness()
# Algebraic well-posed model
whole_cell = ["negative electrode", "separator", "positive electrode"]
model = pybamm.BaseModel()
model.algebraic = {c: 5 * pybamm.div(pybamm.grad(c)) - 1}
model.boundary_conditions = {
c: {
"left": (0, "Dirichlet"),
"right": (0, "Dirichlet")
}
}
model.check_well_posedness()
model.boundary_conditions = {
c: {
"left": (0, "Neumann"),
"right": (0, "Neumann")
}
}
model.check_well_posedness()
def get_coupled_variables(self, variables):
eps = separator_and_positive_only(variables["Porosity"])
c_ox = variables[
"Separator and positive electrode oxygen concentration"]
# TODO: allow charge and convection?
v_box = pybamm.Scalar(0)
param = self.param
b = pybamm.Concatenation(
pybamm.FullBroadcast(param.b_s, ["separator"],
"current collector"),
pybamm.FullBroadcast(param.b_p, ["positive electrode"],
"current collector"),
)
N_ox_diffusion = -(eps**b) * param.curlyD_ox * pybamm.grad(c_ox)
N_ox = N_ox_diffusion + c_ox * v_box
# Flux in the negative electrode is zero
N_ox = pybamm.Concatenation(
pybamm.FullBroadcast(0, "negative electrode", "current collector"),
N_ox)
variables.update(self._get_standard_flux_variables(N_ox))
return variables
def get_coupled_variables(self, variables):
tor_0 = variables["Leading-order electrolyte tortuosity"]
c_e_0_av = variables[
"Leading-order x-averaged electrolyte concentration"]
c_e = variables["Electrolyte concentration"]
# i_e = variables["Electrolyte current density"]
v_box_0 = variables["Leading-order volume-averaged velocity"]
T_0 = variables["Leading-order cell temperature"]
param = self.param
N_e_diffusion = -tor_0 * param.D_e(c_e_0_av, T_0) * pybamm.grad(c_e)
# N_e_migration = (param.C_e * param.t_plus) / param.gamma_e * i_e
# N_e_convection = c_e * v_box_0
# N_e = N_e_diffusion + N_e_migration + N_e_convection
if v_box_0.id == pybamm.Scalar(0).id:
N_e = N_e_diffusion
else:
N_e = N_e_diffusion + v_box_0 * c_e
variables.update(self._get_standard_flux_variables(N_e))
return variables
def test_gradient(self):
# gradient of scalar symbol should fail
a = pybamm.Symbol("a")
with self.assertRaisesRegex(
pybamm.DomainError,
"Cannot take gradient of 'a' since its domain is empty"):
pybamm.Gradient(a)
# gradient of variable evaluating on edges should fail
a = pybamm.PrimaryBroadcastToEdges(pybamm.Scalar(1), "test")
with self.assertRaisesRegex(TypeError, "evaluates on edges"):
pybamm.Gradient(a)
# gradient of broadcast should return broadcasted zero
a = pybamm.PrimaryBroadcast(pybamm.Variable("a"), "test domain")
grad = pybamm.grad(a)
self.assertIsInstance(grad, pybamm.PrimaryBroadcastToEdges)
self.assertIsInstance(grad.child, pybamm.PrimaryBroadcast)
self.assertIsInstance(grad.child.child, pybamm.Scalar)
self.assertEqual(grad.child.child.value, 0)
# otherwise gradient should work
a = pybamm.Symbol("a", domain="test domain")
grad = pybamm.Gradient(a)
self.assertEqual(grad.children[0].name, a.name)
self.assertEqual(grad.domain, a.domain)
def get_coupled_variables(self, variables):
eps_0 = variables["Leading-order porosity"]
c_e_0_av = variables[
"Leading-order x-averaged electrolyte concentration"]
c_e = variables["Electrolyte concentration"]
# i_e = variables["Electrolyte current density"]
v_box_0 = variables["Leading-order volume-averaged velocity"]
T_0 = variables["Leading-order cell temperature"]
param = self.param
whole_cell = ["negative electrode", "separator", "positive electrode"]
N_e_diffusion = (-(eps_0**param.b) * pybamm.PrimaryBroadcast(
param.D_e(c_e_0_av, T_0), whole_cell) * pybamm.grad(c_e))
# N_e_migration = (param.C_e * param.t_plus) / param.gamma_e * i_e
# N_e_convection = c_e * v_box_0
# N_e = N_e_diffusion + N_e_migration + N_e_convection
if v_box_0.id == pybamm.Scalar(0).id:
N_e = N_e_diffusion
else:
N_e = N_e_diffusion + pybamm.outer(v_box_0, c_e)
variables.update(self._get_standard_flux_variables(N_e))
return variables
def get_coupled_variables(self, variables):
phi_s = variables[self.domain + " electrode potential"]
eps = variables[self.domain + " electrode porosity"]
if self.domain == "Negative":
sigma = self.param.sigma_n
b = self.param.b_n
elif self.domain == "Positive":
sigma = self.param.sigma_p
b = self.param.b_p
sigma_eff = sigma * (1 - eps)**b
i_s = -sigma_eff * pybamm.grad(phi_s)
variables.update(
{self.domain + " electrode effective conductivity": sigma_eff})
variables.update(self._get_standard_current_variables(i_s))
if self.domain == "Positive":
variables.update(
self._get_standard_whole_cell_variables(variables))
return variables
def test_new_copy(self):
model = pybamm.BaseModel(name="a model")
whole_cell = ["negative electrode", "separator", "positive electrode"]
c = pybamm.Variable("c", domain=whole_cell)
d = pybamm.Variable("d", domain=whole_cell)
model.rhs = {c: 5 * pybamm.div(pybamm.grad(d)) - 1, d: -c}
model.initial_conditions = {c: 1, d: 2}
model.boundary_conditions = {
c: {
"left": (0, "Dirichlet"),
"right": (0, "Dirichlet")
},
d: {
"left": (0, "Dirichlet"),
"right": (0, "Dirichlet")
},
}
model.use_jacobian = False
model.use_simplify = False
model.convert_to_format = "python"
new_model = model.new_copy()
self.assertEqual(new_model.name, model.name)
self.assertEqual(new_model.use_jacobian, model.use_jacobian)
self.assertEqual(new_model.use_simplify, model.use_simplify)
self.assertEqual(new_model.convert_to_format, model.convert_to_format)
self.assertEqual(new_model.timescale, model.timescale)
def test_process_parameters_and_discretise(self):
model = pybamm.lithium_ion.SPM()
# Set up geometry and parameters
geometry = model.default_geometry
parameter_values = model.default_parameter_values
parameter_values.process_geometry(geometry)
# Set up discretisation
mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
# Process expression
c = pybamm.Parameter("Negative electrode thickness [m]") * pybamm.Variable(
"X-averaged negative particle concentration",
domain="negative particle",
auxiliary_domains={"secondary": "current collector"},
)
processed_c = model.process_parameters_and_discretise(c, parameter_values, disc)
self.assertIsInstance(processed_c, pybamm.Multiplication)
self.assertIsInstance(processed_c.left, pybamm.Scalar)
self.assertIsInstance(processed_c.right, pybamm.StateVector)
# Process flux manually and check result against flux computed in particle
# submodel
c_n = model.variables["X-averaged negative particle concentration"]
T = pybamm.PrimaryBroadcast(
model.variables["X-averaged negative electrode temperature"],
["negative particle"],
)
D = model.param.D_n(c_n, T)
N = -D * pybamm.grad(c_n)
flux_1 = model.process_parameters_and_discretise(N, parameter_values, disc)
flux_2 = model.variables["X-averaged negative particle flux"]
param_flux_2 = parameter_values.process_symbol(flux_2)
disc_flux_2 = disc.process_symbol(param_flux_2)
self.assertEqual(flux_1.id, disc_flux_2.id)
def test_gradient(self):
mesh = get_unit_2p1D_mesh_for_testing(ypts=32, zpts=32, include_particles=False)
spatial_methods = {
"macroscale": pybamm.FiniteVolume(),
"current collector": pybamm.ScikitFiniteElement(),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
# test gradient of 5*y + 6*z
var = pybamm.Variable("var", domain="current collector")
disc.set_variable_slices([var])
y = mesh["current collector"].coordinates[0, :]
z = mesh["current collector"].coordinates[1, :]
gradient = pybamm.grad(var)
grad_disc = disc.process_symbol(gradient)
grad_disc_y, grad_disc_z = grad_disc.children
np.testing.assert_array_almost_equal(
grad_disc_y.evaluate(None, 5 * y + 6 * z),
5 * np.ones_like(y)[:, np.newaxis],
)
np.testing.assert_array_almost_equal(
grad_disc_z.evaluate(None, 5 * y + 6 * z),
6 * np.ones_like(z)[:, np.newaxis],
)
# check grad_squared positive
eqn = pybamm.grad_squared(var)
eqn_disc = disc.process_symbol(eqn)
ans = eqn_disc.evaluate(None, 3 * y ** 2)
np.testing.assert_array_less(0, ans)
def get_coupled_variables(self, variables):
param = self.param
T = variables["Cell temperature"]
tor = variables["Electrolyte tortuosity"]
c_e = variables["Electrolyte concentration"]
phi_e = variables["Electrolyte potential"]
i_e = (param.kappa_e(c_e, T) * tor * param.gamma_e /
param.C_e) * (param.chi(c_e, T) *
(1 + param.Theta * T) * pybamm.grad(c_e) / c_e -
pybamm.grad(phi_e))
variables.update(self._get_standard_current_variables(i_e))
variables.update(self._get_electrolyte_overpotentials(variables))
return variables
def test_grad_div_broadcast(self):
# create mesh and discretisation
spatial_methods = {"macroscale": pybamm.FiniteVolume()}
mesh = get_mesh_for_testing()
disc = pybamm.Discretisation(mesh, spatial_methods)
a = pybamm.PrimaryBroadcast(1, "negative electrode")
grad_a = disc.process_symbol(pybamm.grad(a))
np.testing.assert_array_equal(grad_a.evaluate(), 0)
a_edge = pybamm.PrimaryBroadcastToEdges(1, "negative electrode")
div_a = disc.process_symbol(pybamm.div(a_edge))
np.testing.assert_array_equal(div_a.evaluate(), 0)
div_grad_a = disc.process_symbol(pybamm.div(pybamm.grad(a)))
np.testing.assert_array_equal(div_grad_a.evaluate(), 0)
def set_rhs(self, variables):
T = variables["Cell temperature"]
T_n = variables["Negative electrode temperature"]
T_s = variables["Separator temperature"]
T_p = variables["Positive electrode temperature"]
Q = variables["Total heating"]
# Define volumetric heat capacity
rho_k = pybamm.concatenation(
self.param.rho_n(T_n),
self.param.rho_s(T_s),
self.param.rho_p(T_p),
)
# Devine thermal conductivity
lambda_k = pybamm.concatenation(
self.param.lambda_n(T_n),
self.param.lambda_s(T_s),
self.param.lambda_p(T_p),
)
# Fourier's law for heat flux
q = -lambda_k * pybamm.grad(T)
# N.B only y-z surface cooling is implemented for this model
self.rhs = {
T: (-pybamm.div(q) / self.param.delta**2 + self.param.B * Q) /
(self.param.C_th * rho_k)
}
def test_process_model_not_inplace(self):
# concatenation of variables as the key
c = pybamm.Variable("c", domain=["negative electrode"])
N = pybamm.grad(c)
model = pybamm.BaseModel()
model.rhs = {c: pybamm.div(N)}
model.initial_conditions = {c: pybamm.Scalar(3)}
model.boundary_conditions = {
c: {"left": (0, "Neumann"), "right": (0, "Neumann")}
}
model.check_well_posedness()
# create discretisation
disc = get_discretisation_for_testing()
mesh = disc.mesh
submesh = mesh["negative electrode"]
discretised_model = disc.process_model(model, inplace=False)
y0 = discretised_model.concatenated_initial_conditions.evaluate()
np.testing.assert_array_equal(
y0, 3 * np.ones_like(submesh.nodes[:, np.newaxis])
)
# grad and div are identity operators here
np.testing.assert_array_equal(
y0, discretised_model.concatenated_rhs.evaluate(None, y0)
)
discretised_model.check_well_posedness()
def test_inner(self):
model = pybamm.lithium_ion.BaseModel()
phi_s = pybamm.standard_variables.phi_s_n
i = pybamm.grad(phi_s)
model.rhs = {phi_s: pybamm.inner(i, i)}
model.boundary_conditions = {
phi_s: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
model.initial_conditions = {phi_s: pybamm.Scalar(0)}
model.variables = {"inner": pybamm.inner(i, i)}
# load parameter values and process model and geometry
param = model.default_parameter_values
geometry = model.default_geometry
param.process_model(model)
param.process_geometry(geometry)
# set mesh
mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts)
# discretise model
disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
disc.process_model(model)
# check doesn't evaluate on edges anymore
self.assertEqual(model.variables["inner"].evaluates_on_edges("primary"), False)
def get_coupled_variables(self, variables):
c_s = variables[self.domain + " particle concentration"]
T = pybamm.PrimaryBroadcast(
variables[self.domain + " electrode temperature"],
[self.domain.lower() + " particle"],
)
if self.domain == "Negative":
N_s = -self.param.D_n(c_s, T) * pybamm.grad(c_s)
elif self.domain == "Positive":
N_s = -self.param.D_p(c_s, T) * pybamm.grad(c_s)
variables.update(self._get_standard_flux_variables(N_s, N_s))
return variables
