def test_not_constant(self):
a = pybamm.NotConstant(pybamm.Scalar(1))
self.assertEqual(a.name, "not_constant")
self.assertEqual(a.domain, [])
self.assertEqual(a.evaluate(), 1)
self.assertEqual(a.jac(pybamm.StateVector(slice(0, 1))).evaluate(), 0)
self.assertFalse(a.is_constant())
self.assertFalse((2 * a).is_constant())
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 _get_interface_variables_for_first_order(self, variables):
# This is a bit of a hack, but we need to wrap electrolyte concentration with
# the NotConstant class
# to differentiate it from the electrolyte concentration inside the
# surface potential difference when taking j.diff(c_e) later on
c_e_0 = pybamm.NotConstant(
variables["Leading-order x-averaged electrolyte concentration"])
hacked_variables = {
**variables,
self.domain + " electrolyte concentration":
pybamm.PrimaryBroadcast(c_e_0, self.domain_for_broadcast),
}
delta_phi = variables["Leading-order x-averaged " +
self.domain.lower() +
" electrode surface potential difference"]
j0 = self._get_exchange_current_density(hacked_variables)
ne = self._get_number_of_electrons_in_reaction()
ocp = self._get_open_circuit_potential(hacked_variables)[0]
if j0.domain in ["current collector", ["current collector"]]:
T = variables["X-averaged cell temperature"]
else:
T = variables[self.domain + " electrode temperature"]
return c_e_0, delta_phi, j0, ne, ocp, T
def test_process_not_constant(self):
param = pybamm.ParameterValues({"a": 4})
a = pybamm.NotConstant(pybamm.Parameter("a"))
self.assertIsInstance(param.process_symbol(a), pybamm.NotConstant)
self.assertEqual(param.process_symbol(a).evaluate(), 4)
def _process_symbol(self, symbol):
""" See :meth:`ParameterValues.process_symbol()`. """
if isinstance(symbol, pybamm.Parameter):
value = self[symbol.name]
if isinstance(value, numbers.Number):
# Scalar inherits name (for updating parameters) and domain (for
# Broadcast)
return pybamm.Scalar(value,
name=symbol.name,
domain=symbol.domain)
elif isinstance(value, pybamm.Symbol):
new_value = self.process_symbol(value)
new_value.domain = symbol.domain
return new_value
else:
raise TypeError("Cannot process parameter '{}'".format(value))
elif isinstance(symbol, pybamm.FunctionParameter):
new_children = []
for child in symbol.children:
if symbol.diff_variable is not None and any(
x.id == symbol.diff_variable.id
for x in child.pre_order()):
# Wrap with NotConstant to avoid simplification,
# which would stop symbolic diff from working properly
new_child = pybamm.NotConstant(child.new_copy())
new_children.append(self.process_symbol(new_child))
else:
new_children.append(self.process_symbol(child))
function_name = self[symbol.name]
# Create Function or Interpolant or Scalar object
if isinstance(function_name, tuple):
# If function_name is a tuple then it should be (name, data) and we need
# to create an Interpolant
name, data = function_name
function = pybamm.Interpolant(data[:, 0],
data[:, 1],
*new_children,
name=name)
# Define event to catch extrapolation. In these events the sign is
# important: it should be positive inside of the range and negative
# outside of it
self.parameter_events.append(
pybamm.Event(
"Interpolant {} lower bound".format(name),
pybamm.min(new_children[0] - min(data[:, 0])),
pybamm.EventType.INTERPOLANT_EXTRAPOLATION,
))
self.parameter_events.append(
pybamm.Event(
"Interpolant {} upper bound".format(name),
pybamm.min(max(data[:, 0]) - new_children[0]),
pybamm.EventType.INTERPOLANT_EXTRAPOLATION,
))
elif isinstance(function_name, numbers.Number):
# If the "function" is provided is actually a scalar, return a Scalar
# object instead of throwing an error.
# Also use ones_like so that we get the right shapes
function = pybamm.Scalar(
function_name,
name=symbol.name) * pybamm.ones_like(*new_children)
elif (isinstance(function_name, pybamm.Symbol)
and function_name.evaluates_to_number()):
# If the "function" provided is a pybamm scalar-like, use ones_like to
# get the right shape
# This also catches input parameters
function = function_name * pybamm.ones_like(*new_children)
elif callable(function_name):
# otherwise evaluate the function to create a new PyBaMM object
function = function_name(*new_children)
elif isinstance(function_name, pybamm.Interpolant):
function = function_name
else:
raise TypeError(
"Parameter provided for '{}' ".format(symbol.name) +
"is of the wrong type (should either be scalar-like or callable)"
)
# Differentiate if necessary
if symbol.diff_variable is None:
function_out = function
else:
# return differentiated function
new_diff_variable = self.process_symbol(symbol.diff_variable)
function_out = function.diff(new_diff_variable)
# Convert possible float output to a pybamm scalar
if isinstance(function_out, numbers.Number):
return pybamm.Scalar(function_out)
# Process again just to be sure
return self.process_symbol(function_out)
elif isinstance(symbol, pybamm.BinaryOperator):
# process children
new_left = self.process_symbol(symbol.left)
new_right = self.process_symbol(symbol.right)
# Special case for averages, which can appear as "integral of a broadcast"
# divided by "integral of a broadcast"
# this construction seems very specific but can appear often when averaging
if (isinstance(symbol, pybamm.Division)
# right is integral(Broadcast(1))
and (isinstance(new_right, pybamm.Integral)
and isinstance(new_right.child, pybamm.Broadcast)
and new_right.child.child.id == pybamm.Scalar(1).id)
# left is integral
and isinstance(new_left, pybamm.Integral)):
# left is integral(Broadcast)
if (isinstance(new_left.child, pybamm.Broadcast)
and new_left.child.child.domain == []):
integrand = new_left.child
if integrand.auxiliary_domains == {}:
return integrand.orphans[0]
else:
domain = integrand.auxiliary_domains["secondary"]
if "tertiary" not in integrand.auxiliary_domains:
return pybamm.PrimaryBroadcast(
integrand.orphans[0], domain)
else:
auxiliary_domains = {
"secondary":
integrand.auxiliary_domains["tertiary"]
}
return pybamm.FullBroadcast(
integrand.orphans[0], domain,
auxiliary_domains)
# left is "integral of concatenation of broadcasts"
elif isinstance(new_left.child, pybamm.Concatenation) and all(
isinstance(child, pybamm.Broadcast)
for child in new_left.child.children):
return self.process_symbol(pybamm.x_average(
new_left.child))
# make new symbol, ensure domain remains the same
new_symbol = symbol._binary_new_copy(new_left, new_right)
new_symbol.domain = symbol.domain
return new_symbol
# Unary operators
elif isinstance(symbol, pybamm.UnaryOperator):
new_child = self.process_symbol(symbol.child)
new_symbol = symbol._unary_new_copy(new_child)
# ensure domain remains the same
new_symbol.domain = symbol.domain
return new_symbol
# Functions
elif isinstance(symbol, pybamm.Function):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._function_new_copy(new_children)
# Concatenations
elif isinstance(symbol, pybamm.Concatenation):
new_children = [
self.process_symbol(child) for child in symbol.children
]
return symbol._concatenation_new_copy(new_children)
else:
# Backup option: return new copy of the object
try:
return symbol.new_copy()
except NotImplementedError:
raise NotImplementedError(
"Cannot process parameters for symbol of type '{}'".format(
type(symbol)))
def __init__(self):
super().__init__()
self.name = "Effective resistance in current collector model (2D)"
self.param = pybamm.LithiumIonParameters()
# Set default length scales
self.length_scales = {
"current collector y": self.param.L_z,
"current collector z": self.param.L_z,
}
# Get necessary parameters
param = self.param
l_cn = param.l_cn
l_cp = param.l_cp
l_tab_p = param.l_tab_p
A_tab_p = l_cp * l_tab_p
sigma_cn_dbl_prime = param.sigma_cn_dbl_prime
sigma_cp_dbl_prime = param.sigma_cp_dbl_prime
delta = param.delta
# Set model variables -- we solve a auxilliary problem in each current collector
# then relate this to the potentials and resistances later
f_n = pybamm.Variable("Unit solution in negative current collector",
domain="current collector")
f_p = pybamm.Variable("Unit solution in positive current collector",
domain="current collector")
# Governing equations -- we impose that the average of f_p is zero
# by introducing a Lagrange multiplier
c = pybamm.Variable("Lagrange multiplier")
self.algebraic = {
f_n:
pybamm.laplacian(f_n) + pybamm.source(1, f_n),
f_p:
pybamm.laplacian(f_p) - pybamm.source(1, f_p) +
c * pybamm.DefiniteIntegralVector(f_p, vector_type="column"),
c:
pybamm.yz_average(f_p) + pybamm.NotConstant(0) * c,
}
# Boundary conditons
pos_tab_bc = l_cp / A_tab_p
self.boundary_conditions = {
f_n: {
"negative tab": (0, "Dirichlet"),
"positive tab": (0, "Neumann")
},
f_p: {
"negative tab": (0, "Neumann"),
"positive tab": (pos_tab_bc, "Neumann"),
},
}
# "Initial conditions" provides initial guess for solver
self.initial_conditions = {
f_n: pybamm.Scalar(0),
f_p: pybamm.Scalar(0),
c: pybamm.Scalar(0),
}
# Define effective current collector resistance
R_cc_n = delta * pybamm.yz_average(f_n) / (l_cn * sigma_cn_dbl_prime)
R_cc_p = (delta * pybamm.BoundaryIntegral(f_p, "positive tab") /
(l_cp * sigma_cp_dbl_prime))
R_cc = R_cc_n + R_cc_p
R_scale = param.potential_scale / param.I_typ
self.variables = {
"Unit solution in negative current collector":
f_n,
"Unit solution in positive current collector":
f_p,
"Effective current collector resistance":
R_cc,
"Effective current collector resistance [Ohm]":
R_cc * R_scale,
"Effective negative current collector resistance":
R_cc_n,
"Effective negative current collector resistance [Ohm]":
R_cc_n * R_scale,
"Effective positive current collector resistance":
R_cc_p,
"Effective positive current collector resistance [Ohm]":
R_cc_p * R_scale,
}
# Add spatial variables
var = pybamm.standard_spatial_vars
L_y = param.L_y
L_z = param.L_z
self.variables.update({
"y": var.y,
"y [m]": var.y * L_y,
"z": var.z,
"z [m]": var.z * L_z
})
pybamm.citations.register("Timms2020")