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 __init__(self, model, parameter_values, disc, solution):
# Process variables
model.variables = pybamm.post_process_variables(
model.variables, solution.t, solution.y, disc.mesh
)
# Assign attributes
self.model = model
self.parameter_values = parameter_values
self.disc = disc
self.solution = solution
if isinstance(self.model, pybamm.lithium_ion.BaseModel):
self.chemistry = "Lithium-ion"
elif isinstance(self.model, pybamm.lead_acid.BaseModel):
self.chemistry = "Lead acid"
# Only for constant current
current_sign = np.sign(
parameter_values["Current function"].parameters_eval["Current [A]"]
)
if current_sign == 1:
self.operating_condition = "discharge"
elif current_sign == -1:
self.operating_condition = "charge"
else:
self.operating_condition = "off"
def model_comparison(models, Crates, t_eval, extra_parameter_values=None):
" Solve models at a range of Crates "
# load parameter values and geometry
geometry = models[0].default_geometry
extra_parameter_values = extra_parameter_values or {}
param = models[0].default_parameter_values
param.update(extra_parameter_values)
# Process parameters (same parameters for all models)
for model in models:
param.process_model(model)
param.process_geometry(geometry)
# set mesh
var = pybamm.standard_spatial_vars
var_pts = {var.x_n: 20, var.x_s: 20, var.x_p: 20}
mesh = pybamm.Mesh(geometry, models[-1].default_submesh_types, var_pts)
# discretise models
discs = {}
for model in models:
disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
disc.process_model(model)
# Store discretisation
discs[model] = disc
# solve model for range of Crates
all_variables = {}
for Crate in Crates:
all_variables[Crate] = {}
current = Crate * 17
pybamm.logger.info("Setting typical current to {} A".format(current))
param.update({"Typical current [A]": current})
for model in models:
param.update_model(model, discs[model])
solution = model.default_solver.solve(model, t_eval)
variables = pybamm.post_process_variables(
model.variables, solution.t, solution.y, mesh
)
variables["solution"] = solution
all_variables[Crate][model.name] = variables
return all_variables, t_eval
def __init__(self, models, discs, solutions):
# Process variables
for model in models:
disc = discs[model]
solution = solutions[model]
model.variables = pybamm.post_process_variables(
model.variables, solution.t, solution.y, disc.mesh)
self.models = models
self.discs = discs
self.solutions = solutions
if isinstance(self.models[0], pybamm.lithium_ion.BaseModel):
self.chemistry = "Lithium-ion"
elif isinstance(self.models[0], pybamm.lead_acid.BaseModel):
self.chemistry = "Lead acid"
self.t = self.get_output_times()
self.mesh = self.get_mesh()
def set_output_variables(self, output_variables, solutions, models, meshes):
# Set up output variables
self.variables = {}
self.spatial_variable = {}
# Calculate subplot positions based on number of variables supplied
self.subplot_positions = {}
self.n_rows = int(len(output_variables) // np.sqrt(len(output_variables)))
self.n_cols = int(np.ceil(len(output_variables) / self.n_rows))
# Process output variables into a form that can be plotted
processed_variables = {}
for i, model in enumerate(models):
variables_to_process = {}
for variable_list in output_variables:
# Make sure we always have a list of lists of variables
if isinstance(variable_list, str):
variable_list = [variable_list]
# Add all variables to the list of variables that should be processed
variables_to_process.update(
{var: model.variables[var] for var in variable_list}
)
processed_variables[model] = pybamm.post_process_variables(
variables_to_process, solutions[i].t, solutions[i].y, meshes[i]
)
# Prepare dictionary of variables
for k, variable_list in enumerate(output_variables):
# Make sure we always have a list of lists of variables
if isinstance(variable_list, str):
variable_list = [variable_list]
# Prepare list of variables
key = tuple(variable_list)
self.variables[key] = [None] * len(models)
# process each variable in variable_list for each model
for i, model in enumerate(models):
# self.variables is a dictionary of lists of lists
self.variables[key][i] = [
processed_variables[model][var] for var in variable_list
]
# Make sure variables have the same dimensions and domain
domain = self.variables[key][0][0].domain
for variable in self.variables[key][0]:
if variable.domain != domain:
raise ValueError("mismatching variable domains")
# Set the x variable for any two-dimensional variables
if self.variables[key][0][0].dimensions == 2:
variable_key = self.variables[key][0][0].spatial_var_name
variable_value = meshes[0].combine_submeshes(*domain)[0].edges
self.spatial_variable[key] = (variable_key, variable_value)
# Don't allow 3D variables
elif any(var.dimensions == 3 for var in self.variables[key][0]):
raise NotImplementedError("cannot plot 3D variables")
# Define subplot position
self.subplot_positions[key] = (self.n_rows, self.n_cols, k + 1)
var.x_p: 5,
var.r_n: 5,
var.r_p: 5,
var.y: 5,
var.z: 5,
}
mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)
disc = pybamm.Discretisation(mesh, model.default_spatial_methods)
disc.process_model(model)
# solve model
solutions = [None] * len(models)
t_eval = np.linspace(0, 3, 1000)
for i, model in enumerate(models):
solution = model.default_solver.solve(model, t_eval)
solutions[i] = solution
pybamm.post_process_variables(model.variables, solution.t, solution.y, mesh=mesh)
# plot
output_variables = [
"Local current collector potential difference [V]",
"Negative current collector potential [V]",
"Positive current collector potential [V]",
"X-averaged electrolyte concentration",
# "Leading-order current collector current density",
"Current collector current density",
"Terminal voltage [V]",
]
plot = pybamm.QuickPlot(models, mesh, solutions, output_variables)
plot.dynamic_plot()
