Merge pull request #745 from suhaklee/issue-744-particledistribution

Issue 744 particledistribution

.gitignore

@@ -4,6 +4,7 @@
 *.png
 /local/
 *.DS_Store
+*.mat
 
 # don't ignore important .txt files
 !requirements*

CHANGELOG.md

@@ -3,6 +3,7 @@
 ## Features
 
 -   Added `InputParameter` node for quickly changing parameter values ([#752](https://github.com/pybamm-team/PyBaMM/pull/752))
+-   Added optional R(x) distribution in particle models ([#745](https://github.com/pybamm-team/PyBaMM/pull/745))
 -   Generalized importing of external variables ([#728](https://github.com/pybamm-team/PyBaMM/pull/728))
 -   Separated active and inactive material volume fractions ([#726](https://github.com/pybamm-team/PyBaMM/pull/726))
 -   Added submodels for tortuosity ([#726](https://github.com/pybamm-team/PyBaMM/pull/726))

examples/scripts/SPMe_SOC.py

@@ -27,14 +27,14 @@
     w = 0.207 / factor
     A = h * w
     l_s = 2.5e-5
-    l1d = (l_n + l_p + l_s)
+    l1d = l_n + l_p + l_s
     vol = h * w * l1d
     vol_cm3 = vol * 1e6
     tot_cap = 0.0
     tot_time = 0.0
     fig, axes = plt.subplots(1, 2, sharey=True)
     I_mag = 1.01 / factor
-    print('*' * 30)
+    print("*" * 30)
     for enum, I_app in enumerate([-1.0, 1.0]):
         I_app *= I_mag
         # load model
@@ -44,27 +44,33 @@
         # load parameter values and process model and geometry
         param = model.default_parameter_values
         param.update(
-            {"Electrode height [m]": h,
-             "Electrode width [m]": w,
-             "Negative electrode thickness [m]": l_n,
-             "Positive electrode thickness [m]": l_p,
-             "Separator thickness [m]": l_s,
-             "Lower voltage cut-off [V]": 2.8,
-             "Upper voltage cut-off [V]": 4.7,
-             "Maximum concentration in negative electrode [mol.m-3]": 25000,
-             "Maximum concentration in positive electrode [mol.m-3]": 50000,
-             "Initial concentration in negative electrode [mol.m-3]": 12500,
-             "Initial concentration in positive electrode [mol.m-3]": 25000,
-             "Negative electrode surface area density [m-1]": 180000.0,
-             "Positive electrode surface area density [m-1]": 150000.0,
-             "Typical current [A]": I_app,
-             }
+            {
+                "Electrode height [m]": h,
+                "Electrode width [m]": w,
+                "Negative electrode thickness [m]": l_n,
+                "Positive electrode thickness [m]": l_p,
+                "Separator thickness [m]": l_s,
+                "Lower voltage cut-off [V]": 2.8,
+                "Upper voltage cut-off [V]": 4.7,
+                "Maximum concentration in negative electrode [mol.m-3]": 25000,
+                "Maximum concentration in positive electrode [mol.m-3]": 50000,
+                "Initial concentration in negative electrode [mol.m-3]": 12500,
+                "Initial concentration in positive electrode [mol.m-3]": 25000,
+                "Negative electrode surface area density [m-1]": 180000.0,
+                "Positive electrode surface area density [m-1]": 150000.0,
+                "Typical current [A]": I_app,
+            }
         )
         param.process_model(model)
         param.process_geometry(geometry)
         s_var = pybamm.standard_spatial_vars
-        var_pts = {s_var.x_n: 5, s_var.x_s: 5, s_var.x_p: 5,
-                   s_var.r_n: 5, s_var.r_p: 10}
+        var_pts = {
+            s_var.x_n: 5,
+            s_var.x_s: 5,
+            s_var.x_p: 5,
+            s_var.r_n: 5,
+            s_var.r_p: 10,
+        }
         # set mesh
         mesh = pybamm.Mesh(geometry, model.default_submesh_types, var_pts)
         # discretise model
@@ -74,58 +80,61 @@
         t_eval = np.linspace(0, 0.2, 100)
         sol = model.default_solver.solve(model, t_eval)
         var = "Positive electrode average extent of lithiation"
-        xpext = pybamm.ProcessedVariable(model.variables[var],
-                                         sol.t, sol.y, mesh=mesh)
+        xpext = pybamm.ProcessedVariable(model.variables[var], sol.t, sol.y, mesh=mesh)
         var = "Negative electrode average extent of lithiation"
-        xnext = pybamm.ProcessedVariable(model.variables[var],
-                                         sol.t, sol.y, mesh=mesh)
+        xnext = pybamm.ProcessedVariable(model.variables[var], sol.t, sol.y, mesh=mesh)
         var = "X-averaged positive particle surface concentration"
-        xpsurf = pybamm.ProcessedVariable(model.variables[var],
-                                          sol.t, sol.y, mesh=mesh)
+        xpsurf = pybamm.ProcessedVariable(model.variables[var], sol.t, sol.y, mesh=mesh)
         var = "X-averaged negative particle surface concentration"
-        xnsurf = pybamm.ProcessedVariable(model.variables[var],
-                                          sol.t, sol.y, mesh=mesh)
-        time = pybamm.ProcessedVariable(model.variables["Time [h]"],
-                                        sol.t, sol.y, mesh=mesh)
+        xnsurf = pybamm.ProcessedVariable(model.variables[var], sol.t, sol.y, mesh=mesh)
+        time = pybamm.ProcessedVariable(
+            model.variables["Time [h]"], sol.t, sol.y, mesh=mesh
+        )
         # Coulomb counting
         time_hours = time(sol.t)
         dc_time = np.around(time_hours[-1], 3)
         # Capacity mAh
         cap = np.absolute(I_app * 1000 * dc_time)
         cap_time = np.absolute(I_app * 1000 * time_hours)
-        axes[enum].plot(cap_time,
-                        xnext(sol.t), 'r-', label='Average Neg')
-        axes[enum].plot(cap_time,
-                        xpext(sol.t), 'b-', label='Average Pos')
-        axes[enum].plot(cap_time,
-                        xnsurf(sol.t), 'r--', label='Surface Neg')
-        axes[enum].plot(cap_time,
-                        xpsurf(sol.t), 'b--', label='Surface Pos')
-        axes[enum].set_xlabel('Capacity [mAh]')
+        axes[enum].plot(cap_time, xnext(sol.t), "r-", label="Average Neg")
+        axes[enum].plot(cap_time, xpext(sol.t), "b-", label="Average Pos")
+        axes[enum].plot(cap_time, xnsurf(sol.t), "r--", label="Surface Neg")
+        axes[enum].plot(cap_time, xpsurf(sol.t), "b--", label="Surface Pos")
+        axes[enum].set_xlabel("Capacity [mAh]")
         handles, labels = axes[enum].get_legend_handles_labels()
         axes[enum].legend(handles, labels)
         if I_app < 0.0:
-            axes[enum].set_ylabel('Extent of Lithiation, Elecrode Ratio: '
-                                  + str(e_ratio))
-            axes[enum].title.set_text('Charge')
+            axes[enum].set_ylabel(
+                "Extent of Lithiation, Elecrode Ratio: " + str(e_ratio)
+            )
+            axes[enum].title.set_text("Charge")
         else:
-            axes[enum].title.set_text('Discharge')
-        print('Applied Current', I_app, 'A', 'Time',
-              dc_time, 'hrs', 'Capacity', cap, 'mAh')
+            axes[enum].title.set_text("Discharge")
+        print(
+            "Applied Current",
+            I_app,
+            "A",
+            "Time",
+            dc_time,
+            "hrs",
+            "Capacity",
+            cap,
+            "mAh",
+        )
         tot_cap += cap
         tot_time += dc_time
 
-    print('Anode : Cathode thickness', e_ratio)
-    print('Total Charge/Discharge Time', tot_time, 'hrs')
-    print('Total Capacity', np.around(tot_cap, 3), 'mAh')
+    print("Anode : Cathode thickness", e_ratio)
+    print("Total Charge/Discharge Time", tot_time, "hrs")
+    print("Total Capacity", np.around(tot_cap, 3), "mAh")
     specific_cap = np.around(tot_cap, 3) / vol_cm3
-    print('Total Capacity', specific_cap, 'mAh.cm-3')
+    print("Total Capacity", specific_cap, "mAh.cm-3")
     capacities.append(tot_cap)
     specific_capacities.append(specific_cap)
 
 fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
 ax1.plot(thicknesses / l_p, capacities)
 ax2.plot(thicknesses / l_p, specific_capacities)
-ax1.set_ylabel('Capacity [mAh]')
-ax2.set_ylabel('Specific Capacity [mAh.cm-3]')
-ax2.set_xlabel('Anode : Cathode thickness')
+ax1.set_ylabel("Capacity [mAh]")
+ax2.set_ylabel("Specific Capacity [mAh.cm-3]")
+ax2.set_xlabel("Anode : Cathode thickness")

examples/scripts/SPMe_step.py

@@ -49,7 +49,6 @@
 # plot
 voltage = pybamm.ProcessedVariable(
     model.variables["Terminal voltage [V]"], solution.t, solution.y, mesh=mesh
-
 )
 step_voltage = pybamm.ProcessedVariable(
     model.variables["Terminal voltage [V]"], step_solution.t, step_solution.y, mesh=mesh

examples/scripts/compare_extrapolations.py

@@ -29,4 +29,3 @@
 solutions = [sim_lin.solution, sim_quad.solution]
 plot = pybamm.QuickPlot(models, sim_lin.mesh, solutions)
 plot.dynamic_plot()
-

examples/scripts/compare_lithium_ion_particle_distribution.py

@@ -0,0 +1,82 @@
+#
+# Compare lithium-ion battery models
+#
+import argparse
+import numpy as np
+import pybamm
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    "--debug", action="store_true", help="Set logging level to 'DEBUG'."
+)
+args = parser.parse_args()
+if args.debug:
+    pybamm.set_logging_level("DEBUG")
+else:
+    pybamm.set_logging_level("INFO")
+
+# load models
+options = {"thermal": "isothermal"}
+models = [
+    pybamm.lithium_ion.DFN(options, name="standard DFN"),
+    pybamm.lithium_ion.DFN(options, name="particle DFN"),
+]
+
+
+# load parameter values and process models and geometry
+params = [models[0].default_parameter_values, models[1].default_parameter_values]
+params[0]["Typical current [A]"] = 1.0
+params[0].process_model(models[0])
+
+
+params[1]["Typical current [A]"] = 1.0
+
+
+def negative_distribution(x):
+    return 1 + x
+
+
+def positive_distribution(x):
+    return 1 + (x - (1 - models[1].param.l_p))
+
+
+params[1]["Negative particle distribution in x"] = negative_distribution
+params[1]["Positive particle distribution in x"] = positive_distribution
+params[1].process_model(models[1])
+
+# set mesh
+var = pybamm.standard_spatial_vars
+var_pts = {var.x_n: 10, var.x_s: 10, var.x_p: 10, var.r_n: 5, var.r_p: 5}
+
+# discretise models
+for param, model in zip(params, models):
+    # create geometry
+    geometry = model.default_geometry
+    param.process_geometry(geometry)
+    mesh = pybamm.Mesh(geometry, models[-1].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, 0.3, 100)
+for i, model in enumerate(models):
+    solutions[i] = model.default_solver.solve(model, t_eval)
+
+
+output_variables = [
+    "Negative particle surface concentration",
+    "Electrolyte concentration",
+    "Positive particle surface concentration",
+    "Current [A]",
+    "Negative electrode potential [V]",
+    "Electrolyte potential [V]",
+    "Positive electrode potential [V]",
+    "Terminal voltage [V]",
+    "Negative particle distribution in x",
+    "Positive particle distribution in x",
+]
+
+# plot
+plot = pybamm.QuickPlot(models, mesh, solutions, output_variables=output_variables)
+plot.dynamic_plot()

examples/scripts/heat_equation.py

@@ -122,11 +122,7 @@ def T_exact(x, t):
         label="Numerical" if i == 0 else "",
     )
     plt.plot(
-        xx,
-        T_exact(xx, t),
-        "-",
-        color=color,
-        label="Exact (t={})".format(plot_times[i]),
+        xx, T_exact(xx, t), "-", color=color, label="Exact (t={})".format(plot_times[i])
     )
 plt.xlabel("x", fontsize=16)
 plt.ylabel("T", fontsize=16)

input/parameters/lithium-ion/anodes/graphite_mcmb2528_Marquis2019/parameters.csv

@@ -11,6 +11,7 @@ Negative electrode OCP [V],[function]graphite_mcmb2528_ocp_Dualfoil1998,
 Negative electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction
 Negative electrode active material volume fraction,0.7,,assuming zero binder volume fraction
 Negative particle radius [m],1E-05,Scott Moura FastDFN,
+Negative particle distribution in x,1,,
 Negative electrode surface area density [m-1],180000,Scott Moura FastDFN,
 Negative electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN,
 Negative electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN,

input/parameters/lithium-ion/cathodes/lico2_Marquis2019/parameters.csv

@@ -11,6 +11,7 @@ Positive electrode OCP [V],[function]lico2_ocp_Dualfoil1998,
 Positive electrode porosity,0.3,Scott Moura FastDFN,electrolyte volume fraction
 Positive electrode active material volume fraction,0.7,,assuming zero binder volume fraction
 Positive particle radius [m],1E-05,Scott Moura FastDFN,
+Positive particle distribution in x,1,,
 Positive electrode surface area density [m-1],150000,Scott Moura FastDFN,
 Positive electrode Bruggeman coefficient (electrolyte),1.5,Scott Moura FastDFN,
 Positive electrode Bruggeman coefficient (electrode),1.5,Scott Moura FastDFN,

pybamm/__init__.py

@@ -100,7 +100,12 @@ def version(formatted=False):
 from .expression_tree.interpolant import Interpolant
 from .expression_tree.input_parameter import InputParameter
 from .expression_tree.parameter import Parameter, FunctionParameter
-from .expression_tree.broadcasts import Broadcast, PrimaryBroadcast, FullBroadcast
+from .expression_tree.broadcasts import (
+    Broadcast,
+    PrimaryBroadcast,
+    FullBroadcast,
+    ones_like,
+)
 from .expression_tree.scalar import Scalar
 from .expression_tree.variable import Variable
 from .expression_tree.independent_variable import (

pybamm/expression_tree/broadcasts.py

@@ -184,3 +184,26 @@ def evaluate_for_shape(self):
         )
 
         return child_eval * vec
+
+
+def ones_like(*symbols):
+    """
+    Create a symbol with the same shape as the input symbol and with constant value '1',
+    using `FullBroadcast`.
+
+    Parameters
+    ----------
+    symbols : :class:`Symbol`
+        Symbols whose shape to copy
+    """
+    # Make a symbol that combines all the children, to get the right domain
+    # that takes all the child symbols into account
+    sum_symbol = symbols[0]
+    for sym in symbols:
+        sum_symbol += sym
+
+    # Just return scalar 1 if symbol has no domain (no broadcasting necessary)
+    if sum_symbol.domain == []:
+        return pybamm.Scalar(1)
+    else:
+        return FullBroadcast(1, sum_symbol.domain, sum_symbol.auxiliary_domains)

pybamm/models/submodels/particle/fickian/base_fickian_particle.py

@@ -35,16 +35,6 @@ def _flux_law(self, c, T):
     def _unpack(self, variables):
         raise NotImplementedError
 
-    def set_rhs(self, variables):
-
-        c, N, _ = self._unpack(variables)
-
-        if self.domain == "Negative":
-            self.rhs = {c: -(1 / self.param.C_n) * pybamm.div(N)}
-
-        elif self.domain == "Positive":
-            self.rhs = {c: -(1 / self.param.C_p) * pybamm.div(N)}
-
     def set_boundary_conditions(self, variables):
 
         c, _, j = self._unpack(variables)

pybamm/models/submodels/particle/fickian/fickian_many_particles.py

@@ -46,8 +46,30 @@ def get_coupled_variables(self, variables):
         N_s = self._flux_law(c_s, T_k)
 
         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", 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", x)
+            variables.update({"Positive particle distribution in x": R})
         return variables
 
+    def set_rhs(self, variables):
+
+        c, N, _ = self._unpack(variables)
+
+        if self.domain == "Negative":
+            R = variables["Negative particle distribution in x"]
+            self.rhs = {c: -(1 / (R ** 2 * self.param.C_n)) * pybamm.div(N)}
+
+        elif self.domain == "Positive":
+            R = variables["Positive particle distribution in x"]
+            self.rhs = {c: -(1 / (R ** 2 * self.param.C_p)) * pybamm.div(N)}
+
     def _unpack(self, variables):
         c_s = variables[self.domain + " particle concentration"]
         N_s = variables[self.domain + " particle flux"]