def test_to_equation(self):
a = pybamm.Symbol("a", domain="negative particle")
b = pybamm.Symbol("b", domain="current collector")
c = pybamm.Symbol("c", domain="test")
# Test print_name
pybamm.Floor.print_name = "test"
self.assertEqual(pybamm.Floor(-2.5).to_equation(), sympy.symbols("test"))
# Test Negate
self.assertEqual(pybamm.Negate(4).to_equation(), -4.0)
# Test AbsoluteValue
self.assertEqual(pybamm.AbsoluteValue(-4).to_equation(), 4.0)
# Test Gradient
self.assertEqual(pybamm.Gradient(a).to_equation(), sympy_Gradient("a"))
# Test Divergence
self.assertEqual(
pybamm.Divergence(pybamm.Gradient(a)).to_equation(),
sympy_Divergence(sympy_Gradient(a)),
)
# Test BoundaryValue
self.assertEqual(
pybamm.BoundaryValue(a, "right").to_equation(), sympy.symbols("a^{surf}")
)
self.assertEqual(
pybamm.BoundaryValue(b, "positive tab").to_equation(), sympy.symbols(str(b))
)
self.assertEqual(
pybamm.BoundaryValue(c, "left").to_equation(), sympy.symbols("c^{left}")
)
def set_boundary_conditions(self, variables):
T_av = variables["X-averaged cell temperature"]
T_amb = variables["Ambient temperature"]
# Subtract the edge cooling from the tab portion so as to not double count
# Note: tab cooling is also only applied on the current collector hence
# the (l_cn / l) and (l_cp / l) prefactors.
# We also still have edge cooling on the region: x in (0, 1)
h_tab_n_corrected = ((self.param.l_cn / self.param.l) *
(self.param.h_tab_n - self.param.h_edge) /
self.param.delta)
h_tab_p_corrected = ((self.param.l_cp / self.param.l) *
(self.param.h_tab_p - self.param.h_edge) /
self.param.delta)
T_av_n = pybamm.BoundaryValue(T_av, "negative tab")
T_av_p = pybamm.BoundaryValue(T_av, "positive tab")
self.boundary_conditions = {
T_av: {
"negative tab":
(-h_tab_n_corrected * (T_av_n - T_amb), "Neumann"),
"positive tab":
(-h_tab_p_corrected * (T_av_p - T_amb), "Neumann"),
}
}
def get_fundamental_variables(self):
T = pybamm.standard_variables.T
T_cn = pybamm.BoundaryValue(T, "left")
T_cp = pybamm.BoundaryValue(T, "right")
variables = self._get_standard_fundamental_variables(T, T_cn, T_cp)
return variables
def errors(pts, function, method_options, bcs=None):
domain = "test"
x = pybamm.SpatialVariable("x", domain=domain)
geometry = {
domain: {"primary": {x: {"min": pybamm.Scalar(0), "max": pybamm.Scalar(1)}}}
}
submesh_types = {domain: pybamm.MeshGenerator(pybamm.Uniform1DSubMesh)}
var_pts = {x: pts}
mesh = pybamm.Mesh(geometry, submesh_types, var_pts)
spatial_methods = {"test": pybamm.FiniteVolume(method_options)}
disc = pybamm.Discretisation(mesh, spatial_methods)
var = pybamm.Variable("var", domain="test")
left_extrap = pybamm.BoundaryValue(var, "left")
right_extrap = pybamm.BoundaryValue(var, "right")
if bcs:
model = pybamm.BaseBatteryModel()
bc_dict = {var: bcs}
model.boundary_conditions = bc_dict
disc.bcs = disc.process_boundary_conditions(model)
submesh = mesh["test"]
y, l_true, r_true = function(submesh[0].nodes)
disc.set_variable_slices([var])
left_extrap_processed = disc.process_symbol(left_extrap)
right_extrap_processed = disc.process_symbol(right_extrap)
l_error = np.abs(l_true - left_extrap_processed.evaluate(None, y))
r_error = np.abs(r_true - right_extrap_processed.evaluate(None, y))
return l_error, r_error
def get_fundamental_variables(self):
T_n = pybamm.standard_variables.T_n
T_s = pybamm.standard_variables.T_s
T_p = pybamm.standard_variables.T_p
T_cn = pybamm.BoundaryValue(T_n, "left")
T_cp = pybamm.BoundaryValue(T_p, "right")
T = pybamm.Concatenation(T_n, T_s, T_p)
T_x_av = self._x_average(T, T_cn, T_cp)
T_vol_av = self._yz_average(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_boundary_conditions(self, variables):
if self.domain == "Separator":
return None
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"]
if self.domain == "Negative":
c_e_flux = pybamm.BoundaryGradient(c_e, "right")
flux_left = -i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "left")
flux_right = (
(i_boundary_cc / pybamm.BoundaryValue(conductivity, "right"))
- pybamm.BoundaryValue(param.chi(c_e) / c_e, "right") * c_e_flux
- i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "right")
)
lbc = (flux_left, "Neumann")
rbc = (flux_right, "Neumann")
lbc_c_e = (pybamm.Scalar(0), "Neumann")
rbc_c_e = (c_e_flux, "Neumann")
elif self.domain == "Positive":
c_e_flux = pybamm.BoundaryGradient(c_e, "left")
flux_left = (
(i_boundary_cc / pybamm.BoundaryValue(conductivity, "left"))
- pybamm.BoundaryValue(param.chi(c_e) / c_e, "left") * c_e_flux
- i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "left")
)
flux_right = -i_boundary_cc * pybamm.BoundaryValue(1 / sigma_eff, "right")
lbc = (flux_left, "Neumann")
rbc = (flux_right, "Neumann")
lbc_c_e = (c_e_flux, "Neumann")
rbc_c_e = (pybamm.Scalar(0), "Neumann")
# TODO: check if we still need the boundary conditions for c_e, once we have
# internal boundary conditions
self.boundary_conditions = {
delta_phi: {"left": lbc, "right": rbc},
c_e: {"left": lbc_c_e, "right": rbc_c_e},
}
if self.domain == "Negative":
phi_e = variables["Electrolyte potential"]
self.boundary_conditions.update(
{
phi_e: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
)
def test_neg_pos(self):
mesh = get_2p1d_mesh_for_testing()
spatial_methods = {
"macroscale": pybamm.FiniteVolume(),
"current collector": pybamm.ScikitFiniteElement(),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
var = pybamm.Variable("var", domain="current collector")
disc.set_variable_slices([var])
extrap_neg = pybamm.BoundaryValue(var, "negative tab")
extrap_pos = pybamm.BoundaryValue(var, "positive tab")
extrap_neg_disc = disc.process_symbol(extrap_neg)
extrap_pos_disc = disc.process_symbol(extrap_pos)
# check constant returns constant at tab
constant_y = np.ones(mesh["current collector"][0].npts)[:, np.newaxis]
np.testing.assert_array_almost_equal(
extrap_neg_disc.evaluate(None, constant_y), 1)
np.testing.assert_array_almost_equal(
extrap_pos_disc.evaluate(None, constant_y), 1)
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_symbol_new_copy(self):
a = pybamm.Scalar(0)
b = pybamm.Scalar(1)
v_n = pybamm.Variable("v", "negative electrode")
x_n = pybamm.standard_spatial_vars.x_n
v_s = pybamm.Variable("v", "separator")
vec = pybamm.Vector(np.array([1, 2, 3, 4, 5]))
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.Integral(a, pybamm.t),
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),
]:
self.assertEqual(symbol.id, symbol.new_copy().id)
def __init__(self, n=100, max_x=10, param=None):
# Set fixed parameters here
if param is None:
param = pybamm.ParameterValues({
"Far-field concentration of A [mol cm-3]":
1e-6,
"Diffusion Constant [cm2 s-1]":
7.2e-6,
"Faraday Constant [C mol-1]":
96485.3328959,
"Gas constant [J K-1 mol-1]":
8.314459848,
"Electrode Area [cm2]":
0.07,
"Temperature [K]":
297.0,
"Voltage frequency [rad s-1]":
9.0152,
"Voltage start [V]":
0.5,
"Voltage reverse [V]":
-0.1,
"Voltage amplitude [V]":
0.08,
"Scan Rate [V s-1]":
0.08941,
})
# Create dimensional fixed parameters
c_inf = pybamm.Parameter("Far-field concentration of A [mol cm-3]")
D = pybamm.Parameter("Diffusion Constant [cm2 s-1]")
F = pybamm.Parameter("Faraday Constant [C mol-1]")
R = pybamm.Parameter("Gas constant [J K-1 mol-1]")
S = pybamm.Parameter("Electrode Area [cm2]")
T = pybamm.Parameter("Temperature [K]")
E_start_d = pybamm.Parameter("Voltage start [V]")
E_reverse_d = pybamm.Parameter("Voltage reverse [V]")
deltaE_d = pybamm.Parameter("Voltage amplitude [V]")
v = pybamm.Parameter("Scan Rate [V s-1]")
# Create dimensional input parameters
E0 = pybamm.InputParameter("Reversible Potential [non-dim]")
k0 = pybamm.InputParameter("Reaction Rate [non-dim]")
alpha = pybamm.InputParameter("Symmetry factor [non-dim]")
Cdl = pybamm.InputParameter("Capacitance [non-dim]")
Ru = pybamm.InputParameter("Uncompensated Resistance [non-dim]")
omega_d = pybamm.InputParameter("Voltage frequency [rad s-1]")
E0_d = pybamm.InputParameter("Reversible Potential [V]")
k0_d = pybamm.InputParameter("Reaction Rate [s-1]")
alpha = pybamm.InputParameter("Symmetry factor [non-dim]")
Cdl_d = pybamm.InputParameter("Capacitance [F]")
Ru_d = pybamm.InputParameter("Uncompensated Resistance [Ohm]")
# Create scaling factors for non-dimensionalisation
E_0 = R * T / F
T_0 = E_0 / v
L_0 = pybamm.sqrt(D * T_0)
I_0 = D * F * S * c_inf / L_0
# Non-dimensionalise parameters
E0 = E0_d / E_0
k0 = k0_d * L_0 / D
Cdl = Cdl_d * S * E_0 / (I_0 * T_0)
Ru = Ru_d * I_0 / E_0
omega = 2 * np.pi * omega_d * T_0
E_start = E_start_d / E_0
E_reverse = E_reverse_d / E_0
t_reverse = E_start - E_reverse
deltaE = deltaE_d / E_0
# Input voltage protocol
Edc_forward = -pybamm.t
Edc_backwards = pybamm.t - 2 * t_reverse
Eapp = E_start + \
(pybamm.t <= t_reverse) * Edc_forward + \
(pybamm.t > t_reverse) * Edc_backwards + \
deltaE * pybamm.sin(omega * pybamm.t)
# create PyBaMM model object
model = pybamm.BaseModel()
# Create state variables for model
theta = pybamm.Variable("ratio_A", domain="solution")
i = pybamm.Variable("Current")
# Effective potential
Eeff = Eapp - i * Ru
# Faradaic current
i_f = pybamm.BoundaryGradient(theta, "left")
# ODE equations
model.rhs = {
theta: pybamm.div(pybamm.grad(theta)),
i: 1 / (Cdl * Ru) * (-i_f + Cdl * Eapp.diff(pybamm.t) - i),
}
# algebraic equations (none)
model.algebraic = {}
# Butler-volmer boundary condition at electrode
theta_at_electrode = pybamm.BoundaryValue(theta, "left")
butler_volmer = k0 * (theta_at_electrode * pybamm.exp(-alpha *
(Eeff - E0)) -
(1 - theta_at_electrode) * pybamm.exp(
(1 - alpha) * (Eeff - E0)))
# Boundary and initial conditions
model.boundary_conditions = {
theta: {
"right": (pybamm.Scalar(1), "Dirichlet"),
"left": (butler_volmer, "Neumann"),
}
}
model.initial_conditions = {
theta: pybamm.Scalar(1),
i: Cdl * (-1.0 + deltaE * omega),
}
# set spatial variables and solution domain geometry
x = pybamm.SpatialVariable('x', domain="solution")
default_geometry = pybamm.Geometry({
"solution": {
x: {
"min": pybamm.Scalar(0),
"max": pybamm.Scalar(max_x)
}
}
})
default_var_pts = {x: n}
# Using Finite Volume discretisation on an expanding 1D grid for solution
default_submesh_types = {
"solution":
pybamm.MeshGenerator(pybamm.Exponential1DSubMesh, {'side': 'left'})
}
default_spatial_methods = {"solution": pybamm.FiniteVolume()}
# model variables
model.variables = {
"Current [non-dim]": i,
}
#--------------------------------
# Set model parameters
param.process_model(model)
geometry = default_geometry
param.process_geometry(geometry)
# Create mesh and discretise model
mesh = pybamm.Mesh(geometry, default_submesh_types, default_var_pts)
disc = pybamm.Discretisation(mesh, default_spatial_methods)
disc.process_model(model)
# Create solver
solver = pybamm.CasadiSolver(
mode="fast",
rtol=1e-9,
atol=1e-9,
extra_options_setup={'print_stats': False})
#model.convert_to_format = 'jax'
#solver = pybamm.JaxSolver(method='BDF')
#model.convert_to_format = 'python'
#solver = pybamm.ScipySolver(method='BDF')
# Store discretised model and solver
self._model = model
self._solver = solver
self._fast_solver = None
self._omega_d = param["Voltage frequency [rad s-1]"]
self._I_0 = param.process_symbol(I_0).evaluate()
self._T_0 = param.process_symbol(T_0).evaluate()
self._E_0 = param.process_symbol(E_0).evaluate()
self._L_0 = param.process_symbol(L_0).evaluate()
self._S = param.process_symbol(S).evaluate()
self._D = param.process_symbol(D).evaluate()
self._default_var_points = default_var_pts
def test_process_symbol_base(self):
# create discretisation
mesh = get_mesh_for_testing()
spatial_methods = {
"macroscale": pybamm.SpatialMethod(),
"negative particle": pybamm.SpatialMethod(),
"positive particle": pybamm.SpatialMethod(),
"current collector": pybamm.SpatialMethod(),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
# variable
var = pybamm.Variable("var")
var_vec = pybamm.Variable("var vec", domain=["negative electrode"])
disc.y_slices = {var.id: [slice(53)], var_vec.id: [slice(53, 93)]}
var_disc = disc.process_symbol(var)
self.assertIsInstance(var_disc, pybamm.StateVector)
self.assertEqual(var_disc.y_slices[0], disc.y_slices[var.id][0])
# variable dot
var_dot = pybamm.VariableDot("var'")
var_dot_disc = disc.process_symbol(var_dot)
self.assertIsInstance(var_dot_disc, pybamm.StateVectorDot)
self.assertEqual(var_dot_disc.y_slices[0], disc.y_slices[var.id][0])
# scalar
scal = pybamm.Scalar(5)
scal_disc = disc.process_symbol(scal)
self.assertIsInstance(scal_disc, pybamm.Scalar)
self.assertEqual(scal_disc.value, scal.value)
# vector
vec = pybamm.Vector(np.array([1, 2, 3, 4]))
vec_disc = disc.process_symbol(vec)
self.assertIsInstance(vec_disc, pybamm.Vector)
np.testing.assert_array_equal(vec_disc.entries, vec.entries)
# matrix
mat = pybamm.Matrix(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]))
mat_disc = disc.process_symbol(mat)
self.assertIsInstance(mat_disc, pybamm.Matrix)
np.testing.assert_array_equal(mat_disc.entries, mat.entries)
# binary operator
bin = var + scal
bin_disc = disc.process_symbol(bin)
self.assertIsInstance(bin_disc, pybamm.Addition)
self.assertIsInstance(bin_disc.children[0], pybamm.StateVector)
self.assertIsInstance(bin_disc.children[1], pybamm.Scalar)
bin2 = scal + var
bin2_disc = disc.process_symbol(bin2)
self.assertIsInstance(bin2_disc, pybamm.Addition)
self.assertIsInstance(bin2_disc.children[0], pybamm.Scalar)
self.assertIsInstance(bin2_disc.children[1], pybamm.StateVector)
# non-spatial unary operator
un1 = -var
un1_disc = disc.process_symbol(un1)
self.assertIsInstance(un1_disc, pybamm.Negate)
self.assertIsInstance(un1_disc.children[0], pybamm.StateVector)
un2 = abs(var)
un2_disc = disc.process_symbol(un2)
self.assertIsInstance(un2_disc, pybamm.AbsoluteValue)
self.assertIsInstance(un2_disc.children[0], pybamm.StateVector)
# function of one variable
def myfun(x):
return np.exp(x)
func = pybamm.Function(myfun, var)
func_disc = disc.process_symbol(func)
self.assertIsInstance(func_disc, pybamm.Function)
self.assertIsInstance(func_disc.children[0], pybamm.StateVector)
func = pybamm.Function(myfun, scal)
func_disc = disc.process_symbol(func)
self.assertIsInstance(func_disc, pybamm.Function)
self.assertIsInstance(func_disc.children[0], pybamm.Scalar)
# function of multiple variables
def myfun(x, y):
return np.exp(x) * y
func = pybamm.Function(myfun, var, scal)
func_disc = disc.process_symbol(func)
self.assertIsInstance(func_disc, pybamm.Function)
self.assertIsInstance(func_disc.children[0], pybamm.StateVector)
self.assertIsInstance(func_disc.children[1], pybamm.Scalar)
# boundary value
bv_left = pybamm.BoundaryValue(var_vec, "left")
bv_left_disc = disc.process_symbol(bv_left)
self.assertIsInstance(bv_left_disc, pybamm.MatrixMultiplication)
self.assertIsInstance(bv_left_disc.left, pybamm.Matrix)
self.assertIsInstance(bv_left_disc.right, pybamm.StateVector)
bv_right = pybamm.BoundaryValue(var_vec, "left")
bv_right_disc = disc.process_symbol(bv_right)
self.assertIsInstance(bv_right_disc, pybamm.MatrixMultiplication)
self.assertIsInstance(bv_right_disc.left, pybamm.Matrix)
self.assertIsInstance(bv_right_disc.right, pybamm.StateVector)
# not implemented
sym = pybamm.Symbol("sym")
with self.assertRaises(NotImplementedError):
disc.process_symbol(sym)
def test_extrapolate_2d_models(self):
# create discretisation
mesh = get_p2d_mesh_for_testing()
method_options = {
"extrapolation": {
"order": "linear",
"use bcs": False
}
}
spatial_methods = {
"macroscale": pybamm.FiniteVolume(method_options),
"negative particle": pybamm.FiniteVolume(method_options),
"positive particle": pybamm.FiniteVolume(method_options),
"current collector": pybamm.FiniteVolume(method_options),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
# Microscale
var = pybamm.Variable("var", domain="negative particle")
extrap_right = pybamm.BoundaryValue(var, "right")
disc.set_variable_slices([var])
extrap_right_disc = disc.process_symbol(extrap_right)
self.assertEqual(extrap_right_disc.domain, [])
# domain for boundary values must now be explicitly set
extrap_right.domain = ["negative electrode"]
disc.set_variable_slices([var])
extrap_right_disc = disc.process_symbol(extrap_right)
self.assertEqual(extrap_right_disc.domain, ["negative electrode"])
# evaluate
y_macro = mesh["negative electrode"][0].nodes
y_micro = mesh["negative particle"][0].nodes
y = np.outer(y_macro, y_micro).reshape(-1, 1)
# extrapolate to r=1 --> should evaluate to y_macro
np.testing.assert_array_almost_equal(
extrap_right_disc.evaluate(y=y)[:, 0], y_macro)
var = pybamm.Variable("var", domain="positive particle")
extrap_right = pybamm.BoundaryValue(var, "right")
disc.set_variable_slices([var])
extrap_right_disc = disc.process_symbol(extrap_right)
self.assertEqual(extrap_right_disc.domain, [])
# domain for boundary values must now be explicitly set
extrap_right.domain = ["positive electrode"]
disc.set_variable_slices([var])
extrap_right_disc = disc.process_symbol(extrap_right)
self.assertEqual(extrap_right_disc.domain, ["positive electrode"])
# 2d macroscale
mesh = get_1p1d_mesh_for_testing()
disc = pybamm.Discretisation(mesh, spatial_methods)
var = pybamm.Variable("var", domain="negative electrode")
extrap_right = pybamm.BoundaryValue(var, "right")
disc.set_variable_slices([var])
extrap_right_disc = disc.process_symbol(extrap_right)
self.assertEqual(extrap_right_disc.domain, [])
# test extrapolate to "negative tab" gives same as "left" and
# "positive tab" gives same "right" (see get_mesh_for_testing)
var = pybamm.Variable("var", domain="current collector")
disc.set_variable_slices([var])
submesh = mesh["current collector"]
constant_y = np.ones_like(submesh[0].nodes[:, np.newaxis])
extrap_neg = pybamm.BoundaryValue(var, "negative tab")
extrap_neg_disc = disc.process_symbol(extrap_neg)
extrap_left = pybamm.BoundaryValue(var, "left")
extrap_left_disc = disc.process_symbol(extrap_left)
np.testing.assert_array_equal(
extrap_neg_disc.evaluate(None, constant_y),
extrap_left_disc.evaluate(None, constant_y),
)
extrap_pos = pybamm.BoundaryValue(var, "positive tab")
extrap_pos_disc = disc.process_symbol(extrap_pos)
extrap_right = pybamm.BoundaryValue(var, "right")
extrap_right_disc = disc.process_symbol(extrap_right)
np.testing.assert_array_equal(
extrap_pos_disc.evaluate(None, constant_y),
extrap_right_disc.evaluate(None, constant_y),
)
def test_quadratic_extrapolate_left_right(self):
# create discretisation
mesh = get_mesh_for_testing()
method_options = {
"extrapolation": {
"order": "quadratic",
"use bcs": False
}
}
spatial_methods = {
"macroscale": pybamm.FiniteVolume(method_options),
"negative particle": pybamm.FiniteVolume(method_options),
"current collector": pybamm.ZeroDimensionalMethod(method_options),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
whole_cell = ["negative electrode", "separator", "positive electrode"]
macro_submesh = mesh.combine_submeshes(*whole_cell)
micro_submesh = mesh["negative particle"]
# Macroscale
# create variable
var = pybamm.Variable("var", domain=whole_cell)
# boundary value should work with something more complicated than a variable
extrap_left = pybamm.BoundaryValue(2 * var, "left")
extrap_right = pybamm.BoundaryValue(4 - var, "right")
disc.set_variable_slices([var])
extrap_left_disc = disc.process_symbol(extrap_left)
extrap_right_disc = disc.process_symbol(extrap_right)
# check constant extrapolates to constant
constant_y = np.ones_like(macro_submesh[0].nodes[:, np.newaxis])
np.testing.assert_array_almost_equal(
extrap_left_disc.evaluate(None, constant_y), 2.0)
np.testing.assert_array_almost_equal(
extrap_right_disc.evaluate(None, constant_y), 3.0)
# check linear variable extrapolates correctly
linear_y = macro_submesh[0].nodes
np.testing.assert_array_almost_equal(
extrap_left_disc.evaluate(None, linear_y), 0)
np.testing.assert_array_almost_equal(
extrap_right_disc.evaluate(None, linear_y), 3)
# Fluxes
extrap_flux_left = pybamm.BoundaryGradient(2 * var, "left")
extrap_flux_right = pybamm.BoundaryGradient(1 - var, "right")
extrap_flux_left_disc = disc.process_symbol(extrap_flux_left)
extrap_flux_right_disc = disc.process_symbol(extrap_flux_right)
# check constant extrapolates to constant
np.testing.assert_array_almost_equal(
extrap_flux_left_disc.evaluate(None, constant_y), 0)
self.assertEqual(extrap_flux_right_disc.evaluate(None, constant_y), 0)
# check linear variable extrapolates correctly
np.testing.assert_array_almost_equal(
extrap_flux_left_disc.evaluate(None, linear_y), 2)
np.testing.assert_array_almost_equal(
extrap_flux_right_disc.evaluate(None, linear_y), -1)
# Microscale
# create variable
var = pybamm.Variable("var", domain="negative particle")
surf_eqn = pybamm.surf(var)
disc.set_variable_slices([var])
surf_eqn_disc = disc.process_symbol(surf_eqn)
# check constant extrapolates to constant
constant_y = np.ones_like(micro_submesh[0].nodes[:, np.newaxis])
np.testing.assert_array_almost_equal(
surf_eqn_disc.evaluate(None, constant_y), 1)
# check linear variable extrapolates correctly
linear_y = micro_submesh[0].nodes
y_surf = micro_submesh[0].edges[-1]
np.testing.assert_array_almost_equal(
surf_eqn_disc.evaluate(None, linear_y), y_surf)
k = k_dim * L_0_dim / D_dim(c_inf_dim)
V_hat = V_hat_dim * c_inf_dim
def D(cc):
c_dim = c_inf_dim * cc
return D_dim(c_dim) / D_dim(c_inf_dim)
# variables
x = pybamm.SpatialVariable("x", domain="SEI layer", coord_sys="cartesian")
c = pybamm.Variable("Solvent concentration", domain="SEI layer")
L = pybamm.Variable("SEI thickness")
# 3. State governing equations ---------------------------------------------------------
R = k * pybamm.BoundaryValue(c, "left") # SEI reaction flux
N = -(1 / L) * D(c) * pybamm.grad(c) # solvent flux
dcdt = (V_hat * R) * pybamm.inner(x / L, pybamm.grad(c)) - (
1 / L) * pybamm.div(N) # solvent concentration governing equation
dLdt = V_hat * R # SEI thickness governing equation
model.rhs = {c: dcdt, L: dLdt} # add to model
# 4. State boundary conditions ---------------------------------------------------------
D_left = pybamm.BoundaryValue(
D(c),
"left") # pybamm requires BoundaryValue(D(c)) and not D(BoundaryValue(c))
grad_c_left = L * R / D_left # left bc
c_right = pybamm.Scalar(1) # right bc
# add to model
def __init__(self, param=None):
# Set fixed parameters here
if param is None:
param = pybamm.ParameterValues({
"Far-field concentration of S(soln) [mol cm-3]":
1e-6,
"Diffusion Constant [cm2 s-1]":
7.2e-6,
"Faraday Constant [C mol-1]":
96485.3328959,
"Gas constant [J K-1 mol-1]":
8.314459848,
"Electrode Area [cm2]":
0.07,
"Temperature [K]":
273.0,
"Voltage frequency [rad s-1]":
9.0152,
"Voltage start [V]":
0.4,
"Voltage reverse [V]":
-0.4,
"Voltage amplitude [V]":
0.0, # 0.05,
"Scan Rate [V s-1]":
0.05,
"Electrode Coverage [mol cm2]":
6.5e-12,
})
# Create dimensional fixed parameters
D = pybamm.Parameter("Diffusion Constant [cm2 s-1]")
F = pybamm.Parameter("Faraday Constant [C mol-1]")
R = pybamm.Parameter("Gas constant [J K-1 mol-1]")
a = pybamm.Parameter("Electrode Area [cm2]")
T = pybamm.Parameter("Temperature [K]")
omega_d = pybamm.Parameter("Voltage frequency [rad s-1]")
E_start_d = pybamm.Parameter("Voltage start [V]")
E_reverse_d = pybamm.Parameter("Voltage reverse [V]")
deltaE_d = pybamm.Parameter("Voltage amplitude [V]")
v = pybamm.Parameter("Scan Rate [V s-1]")
Gamma = pybamm.Parameter("Electrode Coverage [mol cm2]")
# Create dimensional input parameters
E0_d = pybamm.InputParameter("Reversible Potential [V]")
k0_d = pybamm.InputParameter("Redox Rate [s-1]")
kcat_d = pybamm.InputParameter("Catalytic Rate [cm3 mol-l s-1]")
alpha = pybamm.InputParameter("Symmetry factor [non-dim]")
Cdl_d = pybamm.InputParameter("Capacitance [F]")
Ru_d = pybamm.InputParameter("Uncompensated Resistance [Ohm]")
# Create scaling factors for non-dimensionalisation
E_0 = R * T / F
T_0 = E_0 / v
I_0 = F * a * Gamma / T
L_0 = pybamm.sqrt(D * T_0)
# Non-dimensionalise parameters
E0 = E0_d / E_0
k0 = k0_d * T_0
kcat = kcat_d * Gamma * L_0 / D
Cdl = Cdl_d * a * E_0 / (I_0 * T_0)
Ru = Ru_d * I_0 / E_0
omega = 2 * np.pi * omega_d * T_0
E_start = E_start_d / E_0
E_reverse = E_reverse_d / E_0
t_reverse = E_start - E_reverse
deltaE = deltaE_d / E_0
# Input voltage protocol
Edc_forward = -pybamm.t
Edc_backwards = pybamm.t - 2 * t_reverse
Eapp = E_start + \
(pybamm.t <= t_reverse) * Edc_forward + \
(pybamm.t > t_reverse) * Edc_backwards + \
deltaE * pybamm.sin(omega * pybamm.t)
# create PyBaMM model object
model = pybamm.BaseModel()
# Create state variables for model
theta = pybamm.Variable("O(surf) [non-dim]")
c = pybamm.Variable("S(soln) [non-dim]", domain="solution")
i = pybamm.Variable("Current [non-dim]")
# Effective potential
Eeff = Eapp - i * Ru
# Faridaic current (Butler Volmer)
i_f = k0 * ((1 - theta) * pybamm.exp(
(1 - alpha) * (Eeff - E0)) - theta * pybamm.exp(-alpha *
(Eeff - E0)))
c_at_electrode = pybamm.BoundaryValue(c, "left")
# Catalytic current
i_cat = kcat * c_at_electrode * (1 - theta)
# ODE equations
model.rhs = {
theta: i_f + i_cat,
i: 1 / (Cdl * Ru) * (i_f + Cdl * Eapp.diff(pybamm.t) - i - i_cat),
c: pybamm.div(pybamm.grad(c)),
}
# algebraic equations (none)
model.algebraic = {}
# Boundary and initial conditions
model.boundary_conditions = {
c: {
"right": (pybamm.Scalar(1), "Dirichlet"),
"left": (i_cat, "Neumann"),
}
}
model.initial_conditions = {
theta: pybamm.Scalar(1),
i: Cdl * Eapp.diff(pybamm.t),
c: pybamm.Scalar(1),
}
# set spatial variables and solution domain geometry
x = pybamm.SpatialVariable('x', domain="solution")
model.geometry = pybamm.Geometry({
"solution": {
x: {
"min": pybamm.Scalar(0),
"max": pybamm.Scalar(20)
}
}
})
model.var_pts = {x: 100}
# Using Finite Volume discretisation on an expanding 1D grid
model.submesh_types = {
"solution":
pybamm.MeshGenerator(pybamm.Exponential1DSubMesh, {'side': 'left'})
}
model.spatial_methods = {"solution": pybamm.FiniteVolume()}
# model variables
model.variables = {
"Current [non-dim]": i,
"O(surf) [non-dim]": theta,
"S(soln) at electrode [non-dim]": c_at_electrode,
"Applied Voltage [non-dim]": Eapp,
}
# --------------------------------
# Set model parameters
param.process_model(model)
geometry = model.geometry
param.process_geometry(geometry)
# Create mesh and discretise model
mesh = pybamm.Mesh(geometry, model.submesh_types, model.var_pts)
disc = pybamm.Discretisation(mesh, model.spatial_methods)
disc.process_model(model)
# Create solver
model.convert_to_format = 'python'
solver = pybamm.ScipySolver(method='BDF', rtol=1e-6, atol=1e-6)
#solver = pybamm.CasadiSolver(mode='fast', rtol=1e-8, atol=1e-10)
# Store discretised model and solver
self._model = model
self._param = param
self._solver = solver
self._omega_d = param.process_symbol(omega_d).evaluate()
self._I_0 = param.process_symbol(I_0).evaluate()
self._T_0 = param.process_symbol(T_0).evaluate()
def __init__(self):
super().__init__()
self.name = "Effective resistance in current collector model"
self.param = pybamm.standard_parameters_lithium_ion
# Get useful parameters
param = self.param
l_cn = param.l_cn
l_cp = param.l_cp
l_y = param.l_y
sigma_cn_dbl_prime = param.sigma_cn_dbl_prime
sigma_cp_dbl_prime = param.sigma_cp_dbl_prime
alpha_prime = param.alpha_prime
# Set model variables
var = pybamm.standard_spatial_vars
psi = pybamm.Variable("Current collector potential weighted sum",
["current collector"])
W = pybamm.Variable(
"Perturbation to current collector potential difference",
["current collector"],
)
c_psi = pybamm.Variable("Lagrange multiplier for variable `psi`")
c_W = pybamm.Variable("Lagrange multiplier for variable `W`")
self.variables = {
"Current collector potential weighted sum": psi,
"Perturbation to current collector potential difference": W,
"Lagrange multiplier for variable `psi`": c_psi,
"Lagrange multiplier for variable `W`": c_W,
}
# Algebraic equations (enforce zero mean constraint through Lagrange multiplier)
# 0*LagrangeMultiplier hack otherwise gives KeyError
self.algebraic = {
psi:
pybamm.laplacian(psi) +
c_psi * pybamm.DefiniteIntegralVector(psi, vector_type="column"),
W:
pybamm.laplacian(W) - pybamm.source(1, W) +
c_W * pybamm.DefiniteIntegralVector(W, vector_type="column"),
c_psi:
pybamm.Integral(psi, [var.y, var.z]) + 0 * c_psi,
c_W:
pybamm.Integral(W, [var.y, var.z]) + 0 * c_W,
}
# Boundary conditons
psi_neg_tab_bc = l_cn
psi_pos_tab_bc = -l_cp
W_neg_tab_bc = l_y / (alpha_prime * sigma_cn_dbl_prime)
W_pos_tab_bc = l_y / (alpha_prime * sigma_cp_dbl_prime)
self.boundary_conditions = {
psi: {
"negative tab": (psi_neg_tab_bc, "Neumann"),
"positive tab": (psi_pos_tab_bc, "Neumann"),
},
W: {
"negative tab": (W_neg_tab_bc, "Neumann"),
"positive tab": (W_pos_tab_bc, "Neumann"),
},
}
# "Initial conditions" provides initial guess for solver
# TODO: better guess than zero?
self.initial_conditions = {
psi: pybamm.Scalar(0),
W: pybamm.Scalar(0),
c_psi: pybamm.Scalar(0),
c_W: pybamm.Scalar(0),
}
# Define effective current collector resistance
psi_neg_tab = pybamm.BoundaryValue(psi, "negative tab")
psi_pos_tab = pybamm.BoundaryValue(psi, "positive tab")
W_neg_tab = pybamm.BoundaryValue(W, "negative tab")
W_pos_tab = pybamm.BoundaryValue(W, "positive tab")
R_cc = ((alpha_prime / l_y) * (sigma_cn_dbl_prime * l_cn * W_pos_tab +
sigma_cp_dbl_prime * l_cp * W_neg_tab) -
(psi_pos_tab - psi_neg_tab)) / (sigma_cn_dbl_prime * l_cn +
sigma_cp_dbl_prime * l_cp)
R_cc_dim = R_cc * param.potential_scale / param.I_typ
self.variables.update({
"Current collector potential weighted sum (negative tab)":
psi_neg_tab,
"Current collector potential weighted sum (positive tab)":
psi_pos_tab,
"Perturbation to c.c. potential difference (negative tab)":
W_neg_tab,
"Perturbation to c.c. potential difference (positive tab)":
W_pos_tab,
"Effective current collector resistance":
R_cc,
"Effective current collector resistance [Ohm]":
R_cc_dim,
})
def set_voltage_variables(self):
ocp_n = self.variables["Negative electrode open circuit potential"]
ocp_p = self.variables["Positive electrode open circuit potential"]
ocp_n_av = self.variables[
"X-averaged negative electrode open circuit potential"]
ocp_p_av = self.variables[
"X-averaged positive electrode open circuit potential"]
ocp_n_dim = self.variables[
"Negative electrode open circuit potential [V]"]
ocp_p_dim = self.variables[
"Positive electrode open circuit potential [V]"]
ocp_n_av_dim = self.variables[
"X-averaged negative electrode open circuit potential [V]"]
ocp_p_av_dim = self.variables[
"X-averaged positive electrode open circuit potential [V]"]
ocp_n_left = pybamm.boundary_value(ocp_n, "left")
ocp_n_left_dim = pybamm.boundary_value(ocp_n_dim, "left")
ocp_p_right = pybamm.boundary_value(ocp_p, "right")
ocp_p_right_dim = pybamm.boundary_value(ocp_p_dim, "right")
ocv_av = ocp_p_av - ocp_n_av
ocv_av_dim = ocp_p_av_dim - ocp_n_av_dim
ocv = ocp_p_right - ocp_n_left
ocv_dim = ocp_p_right_dim - ocp_n_left_dim
# overpotentials
eta_r_n_av = self.variables[
"X-averaged negative electrode reaction overpotential"]
eta_r_n_av_dim = self.variables[
"X-averaged negative electrode reaction overpotential [V]"]
eta_r_p_av = self.variables[
"X-averaged positive electrode reaction overpotential"]
eta_r_p_av_dim = self.variables[
"X-averaged positive electrode reaction overpotential [V]"]
delta_phi_s_n_av = self.variables[
"X-averaged negative electrode ohmic losses"]
delta_phi_s_n_av_dim = self.variables[
"X-averaged negative electrode ohmic losses [V]"]
delta_phi_s_p_av = self.variables[
"X-averaged positive electrode ohmic losses"]
delta_phi_s_p_av_dim = self.variables[
"X-averaged positive electrode ohmic losses [V]"]
delta_phi_s_av = delta_phi_s_p_av - delta_phi_s_n_av
delta_phi_s_av_dim = delta_phi_s_p_av_dim - delta_phi_s_n_av_dim
eta_r_av = eta_r_p_av - eta_r_n_av
eta_r_av_dim = eta_r_p_av_dim - eta_r_n_av_dim
# terminal voltage (Note: phi_s_cn is zero at the negative tab)
phi_s_cp = self.variables["Positive current collector potential"]
phi_s_cp_dim = self.variables[
"Positive current collector potential [V]"]
if self.options["dimensionality"] == 0:
V = phi_s_cp
V_dim = phi_s_cp_dim
elif self.options["dimensionality"] in [1, 2]:
V = pybamm.BoundaryValue(phi_s_cp, "positive tab")
V_dim = pybamm.BoundaryValue(phi_s_cp_dim, "positive tab")
# TODO: add current collector losses to the voltage in 3D
self.variables.update({
"X-averaged open circuit voltage": ocv_av,
"Measured open circuit voltage": ocv,
"X-averaged open circuit voltage [V]": ocv_av_dim,
"Measured open circuit voltage [V]": ocv_dim,
"X-averaged reaction overpotential": eta_r_av,
"X-averaged reaction overpotential [V]": eta_r_av_dim,
"X-averaged solid phase ohmic losses": delta_phi_s_av,
"X-averaged solid phase ohmic losses [V]": delta_phi_s_av_dim,
"Terminal voltage": V,
"Terminal voltage [V]": V_dim,
})
# Battery-wide variables
eta_e_av_dim = self.variables.get(
"X-averaged electrolyte ohmic losses [V]", 0)
eta_c_av_dim = self.variables.get(
"X-averaged concentration overpotential [V]", 0)
num_cells = pybamm.Parameter(
"Number of cells connected in series to make a battery")
self.variables.update({
"X-averaged battery open circuit voltage [V]":
ocv_av_dim * num_cells,
"Measured battery open circuit voltage [V]":
ocv_dim * num_cells,
"X-averaged battery reaction overpotential [V]":
eta_r_av_dim * num_cells,
"X-averaged battery solid phase ohmic losses [V]":
delta_phi_s_av_dim * num_cells,
"X-averaged battery electrolyte ohmic losses [V]":
eta_e_av_dim * num_cells,
"X-averaged battery concentration overpotential [V]":
eta_c_av_dim * num_cells,
"Battery voltage [V]":
V_dim * num_cells,
})
# Cut-off voltage
voltage = self.variables["Terminal voltage"]
self.events["Minimum voltage"] = voltage - self.param.voltage_low_cut
self.events["Maximum voltage"] = voltage - self.param.voltage_high_cut
def test_process_symbol(self):
parameter_values = pybamm.ParameterValues({"a": 4, "b": 2, "c": 3})
# process parameter
a = pybamm.Parameter("a")
processed_a = parameter_values.process_symbol(a)
self.assertIsInstance(processed_a, pybamm.Scalar)
self.assertEqual(processed_a.value, 4)
# process binary operation
var = pybamm.Variable("var")
add = a + var
processed_add = parameter_values.process_symbol(add)
self.assertIsInstance(processed_add, pybamm.Addition)
self.assertIsInstance(processed_add.children[0], pybamm.Scalar)
self.assertIsInstance(processed_add.children[1], pybamm.Variable)
self.assertEqual(processed_add.children[0].value, 4)
b = pybamm.Parameter("b")
add = a + b
processed_add = parameter_values.process_symbol(add)
self.assertIsInstance(processed_add, pybamm.Scalar)
self.assertEqual(processed_add.value, 6)
scal = pybamm.Scalar(34)
mul = a * scal
processed_mul = parameter_values.process_symbol(mul)
self.assertIsInstance(processed_mul, pybamm.Scalar)
self.assertEqual(processed_mul.value, 136)
# process integral
aa = pybamm.Parameter("a", domain=["negative electrode"])
x = pybamm.SpatialVariable("x", domain=["negative electrode"])
integ = pybamm.Integral(aa, x)
processed_integ = parameter_values.process_symbol(integ)
self.assertIsInstance(processed_integ, pybamm.Integral)
self.assertIsInstance(processed_integ.children[0], pybamm.Scalar)
self.assertEqual(processed_integ.children[0].value, 4)
self.assertEqual(processed_integ.integration_variable[0].id, x.id)
# process unary operation
v = pybamm.Variable("v", domain="test")
grad = pybamm.Gradient(v)
processed_grad = parameter_values.process_symbol(grad)
self.assertIsInstance(processed_grad, pybamm.Gradient)
self.assertIsInstance(processed_grad.children[0], pybamm.Variable)
# process delta function
aa = pybamm.Parameter("a")
delta_aa = pybamm.DeltaFunction(aa, "left", "some domain")
processed_delta_aa = parameter_values.process_symbol(delta_aa)
self.assertIsInstance(processed_delta_aa, pybamm.DeltaFunction)
self.assertEqual(processed_delta_aa.side, "left")
processed_a = processed_delta_aa.children[0]
self.assertIsInstance(processed_a, pybamm.Scalar)
self.assertEqual(processed_a.value, 4)
# process boundary operator (test for BoundaryValue)
aa = pybamm.Parameter("a", domain=["negative electrode"])
x = pybamm.SpatialVariable("x", domain=["negative electrode"])
boundary_op = pybamm.BoundaryValue(aa * x, "left")
processed_boundary_op = parameter_values.process_symbol(boundary_op)
self.assertIsInstance(processed_boundary_op, pybamm.BoundaryOperator)
processed_a = processed_boundary_op.children[0].children[0]
processed_x = processed_boundary_op.children[0].children[1]
self.assertIsInstance(processed_a, pybamm.Scalar)
self.assertEqual(processed_a.value, 4)
self.assertEqual(processed_x.id, x.id)
# process broadcast
whole_cell = ["negative electrode", "separator", "positive electrode"]
broad = pybamm.PrimaryBroadcast(a, whole_cell)
processed_broad = parameter_values.process_symbol(broad)
self.assertIsInstance(processed_broad, pybamm.Broadcast)
self.assertEqual(processed_broad.domain, whole_cell)
self.assertIsInstance(processed_broad.children[0], pybamm.Scalar)
self.assertEqual(processed_broad.children[0].evaluate(), 4)
# process concatenation
conc = pybamm.concatenation(
pybamm.Vector(np.ones(10), domain="test"),
pybamm.Vector(2 * np.ones(15), domain="test 2"),
)
processed_conc = parameter_values.process_symbol(conc)
self.assertIsInstance(processed_conc.children[0], pybamm.Vector)
self.assertIsInstance(processed_conc.children[1], pybamm.Vector)
np.testing.assert_array_equal(processed_conc.children[0].entries, 1)
np.testing.assert_array_equal(processed_conc.children[1].entries, 2)
# process domain concatenation
c_e_n = pybamm.Variable("c_e_n", ["negative electrode"])
c_e_s = pybamm.Variable("c_e_p", ["separator"])
test_mesh = shared.get_mesh_for_testing()
dom_con = pybamm.DomainConcatenation([a * c_e_n, b * c_e_s], test_mesh)
processed_dom_con = parameter_values.process_symbol(dom_con)
a_proc = processed_dom_con.children[0].children[0]
b_proc = processed_dom_con.children[1].children[0]
self.assertIsInstance(a_proc, pybamm.Scalar)
self.assertIsInstance(b_proc, pybamm.Scalar)
self.assertEqual(a_proc.value, 4)
self.assertEqual(b_proc.value, 2)
# process variable
c = pybamm.Variable("c")
processed_c = parameter_values.process_symbol(c)
self.assertIsInstance(processed_c, pybamm.Variable)
self.assertEqual(processed_c.name, "c")
# process scalar
d = pybamm.Scalar(14)
processed_d = parameter_values.process_symbol(d)
self.assertIsInstance(processed_d, pybamm.Scalar)
self.assertEqual(processed_d.value, 14)
# process array types
e = pybamm.Vector(np.ones(4))
processed_e = parameter_values.process_symbol(e)
self.assertIsInstance(processed_e, pybamm.Vector)
np.testing.assert_array_equal(processed_e.evaluate(), np.ones((4, 1)))
f = pybamm.Matrix(np.ones((5, 6)))
processed_f = parameter_values.process_symbol(f)
self.assertIsInstance(processed_f, pybamm.Matrix)
np.testing.assert_array_equal(processed_f.evaluate(), np.ones((5, 6)))
# process statevector
g = pybamm.StateVector(slice(0, 10))
processed_g = parameter_values.process_symbol(g)
self.assertIsInstance(processed_g, pybamm.StateVector)
np.testing.assert_array_equal(
processed_g.evaluate(y=np.ones(10)), np.ones((10, 1))
)
# not implemented
sym = pybamm.Symbol("sym")
with self.assertRaises(NotImplementedError):
parameter_values.process_symbol(sym)
# not found
with self.assertRaises(KeyError):
x = pybamm.Parameter("x")
parameter_values.process_symbol(x)
def test_indefinite_integral(self):
# create discretisation
mesh = get_mesh_for_testing()
spatial_methods = {
"macroscale": pybamm.FiniteVolume(),
"negative particle": pybamm.FiniteVolume(),
"positive particle": pybamm.FiniteVolume(),
"current collector": pybamm.ZeroDimensionalMethod(),
}
disc = pybamm.Discretisation(mesh, spatial_methods)
# input a phi, take grad, then integrate to recover phi approximation
# (need to test this way as check evaluated on edges using if has grad
# and no div)
phi = pybamm.Variable("phi",
domain=["negative electrode", "separator"])
i = pybamm.grad(phi) # create test current (variable on edges)
x = pybamm.SpatialVariable("x", ["negative electrode", "separator"])
int_grad_phi = pybamm.IndefiniteIntegral(i, x)
disc.set_variable_slices([phi]) # i is not a fundamental variable
# Set boundary conditions (required for shape but don't matter)
disc._bcs = {
phi.id: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
int_grad_phi_disc = disc.process_symbol(int_grad_phi)
left_boundary_value = pybamm.BoundaryValue(int_grad_phi, "left")
left_boundary_value_disc = disc.process_symbol(left_boundary_value)
combined_submesh = mesh.combine_submeshes("negative electrode",
"separator")
# constant case
phi_exact = np.ones((combined_submesh[0].npts, 1))
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
phi_approx += 1 # add constant of integration
np.testing.assert_array_equal(phi_exact, phi_approx)
self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0)
# linear case
phi_exact = combined_submesh[0].nodes[:, np.newaxis]
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
np.testing.assert_array_almost_equal(phi_exact, phi_approx)
self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0)
# sine case
phi_exact = np.sin(combined_submesh[0].nodes[:, np.newaxis])
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
np.testing.assert_array_almost_equal(phi_exact, phi_approx)
self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0)
# --------------------------------------------------------------------
# region which doesn't start at zero
phi = pybamm.Variable("phi",
domain=["separator", "positive electrode"])
i = pybamm.grad(phi) # create test current (variable on edges)
x = pybamm.SpatialVariable("x", ["separator", "positive electrode"])
int_grad_phi = pybamm.IndefiniteIntegral(i, x)
disc.set_variable_slices([phi]) # i is not a fundamental variable
disc._bcs = {
phi.id: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
int_grad_phi_disc = disc.process_symbol(int_grad_phi)
left_boundary_value = pybamm.BoundaryValue(int_grad_phi, "left")
left_boundary_value_disc = disc.process_symbol(left_boundary_value)
combined_submesh = mesh.combine_submeshes("separator",
"positive electrode")
# constant case
phi_exact = np.ones((combined_submesh[0].npts, 1))
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
phi_approx += 1 # add constant of integration
np.testing.assert_array_equal(phi_exact, phi_approx)
self.assertEqual(left_boundary_value_disc.evaluate(y=phi_exact), 0)
# linear case
phi_exact = (combined_submesh[0].nodes[:, np.newaxis] -
combined_submesh[0].edges[0])
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
np.testing.assert_array_almost_equal(phi_exact, phi_approx)
np.testing.assert_array_almost_equal(
left_boundary_value_disc.evaluate(y=phi_exact), 0)
# sine case
phi_exact = np.sin(combined_submesh[0].nodes[:, np.newaxis] -
combined_submesh[0].edges[0])
phi_approx = int_grad_phi_disc.evaluate(None, phi_exact)
np.testing.assert_array_almost_equal(phi_exact, phi_approx)
np.testing.assert_array_almost_equal(
left_boundary_value_disc.evaluate(y=phi_exact), 0)
# --------------------------------------------------------------------
# micrsoscale case
c = pybamm.Variable("c", domain=["negative particle"])
N = pybamm.grad(c) # create test current (variable on edges)
r_n = pybamm.SpatialVariable("r_n", ["negative particle"])
c_integral = pybamm.IndefiniteIntegral(N, r_n)
disc.set_variable_slices([c]) # N is not a fundamental variable
disc._bcs = {
c.id: {
"left": (pybamm.Scalar(0), "Neumann"),
"right": (pybamm.Scalar(0), "Neumann"),
}
}
c_integral_disc = disc.process_symbol(c_integral)
left_boundary_value = pybamm.BoundaryValue(c_integral, "left")
left_boundary_value_disc = disc.process_symbol(left_boundary_value)
combined_submesh = mesh["negative particle"]
# constant case
c_exact = np.ones((combined_submesh[0].npts, 1))
c_approx = c_integral_disc.evaluate(None, c_exact)
c_approx += 1 # add constant of integration
np.testing.assert_array_equal(c_exact, c_approx)
self.assertEqual(left_boundary_value_disc.evaluate(y=c_exact), 0)
# linear case
c_exact = combined_submesh[0].nodes[:, np.newaxis]
c_approx = c_integral_disc.evaluate(None, c_exact)
np.testing.assert_array_almost_equal(c_exact, c_approx)
np.testing.assert_array_almost_equal(
left_boundary_value_disc.evaluate(y=c_exact), 0)
# sine case
c_exact = np.sin(combined_submesh[0].nodes[:, np.newaxis])
c_approx = c_integral_disc.evaluate(None, c_exact)
np.testing.assert_array_almost_equal(c_exact, c_approx, decimal=3)
np.testing.assert_array_almost_equal(
left_boundary_value_disc.evaluate(y=c_exact), 0)
