Many-Particle Models

examples/notebooks/using-submodels.ipynb

@@ -403,7 +403,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the Single Particle Model, the overpotential can be obtianed by inverting the Butler-Volmer relation, so we choose the `InverseButlerVolmer` submodel for the interface, with the \"main\" lithium-ion reaction. Because of how the current is implemented, we also need to separately specify the `CurrentForInverseButlerVolmer` submodel"
+    "In the Single Particle Model, the overpotential can be obtianed by inverting the Butler-Volmer relation, so we choose the `InverseButlerVolmer` submodel for the interface, with the \"main\" lithium-ion reaction (and default lithium ion options). Because of how the current is implemented, we also need to separately specify the `CurrentForInverseButlerVolmer` submodel"
    ]
   },
   {
@@ -414,10 +414,14 @@
    "source": [
     "model.submodels[\n",
     "    \"negative interface\"\n",
-    "] = pybamm.interface.InverseButlerVolmer(model.param, \"Negative\", \"lithium-ion main\")\n",
+    "] = pybamm.interface.InverseButlerVolmer(\n",
+    "    model.param, \"Negative\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
     "model.submodels[\n",
     "    \"positive interface\"\n",
-    "] = pybamm.interface.InverseButlerVolmer(model.param, \"Positive\", \"lithium-ion main\")\n",
+    "] = pybamm.interface.InverseButlerVolmer(\n",
+    "    model.param, \"Positive\", \"lithium-ion main\", options=model.options\n",
+    ")\n",
     "model.submodels[\n",
     "    \"negative interface current\"\n",
     "] = pybamm.interface.CurrentForInverseButlerVolmer(\n",
@@ -540,23 +544,14 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+   "display_name": "Python 2.7.17 64-bit",
+   "name": "python375jvsc74a57bd0fd69f43f58546b570e94fd7eba7b65e6bcc7a5bbc4eab0408017d18902915d69"
   },
   "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
    "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": ""
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
-}
+}

examples/scripts/custom_model.py

@@ -36,10 +36,10 @@
     model.param, "Positive", "uniform profile"
 )
 model.submodels["negative interface"] = pybamm.interface.InverseButlerVolmer(
-    model.param, "Negative", "lithium-ion main"
+    model.param, "Negative", "lithium-ion main", options=model.options
 )
 model.submodels["positive interface"] = pybamm.interface.InverseButlerVolmer(
-    model.param, "Positive", "lithium-ion main"
+    model.param, "Positive", "lithium-ion main", options=model.options
 )
 model.submodels[
     "negative interface current"

pybamm/models/submodels/particle/size_distribution/fickian_single_distribution.py

@@ -106,18 +106,15 @@ def get_coupled_variables(self, variables):
             [self.domain.lower() + " particle size"],
         )
         T_k_xav = pybamm.PrimaryBroadcast(T_k_xav, [self.domain.lower() + " particle"],)
-        R = pybamm.PrimaryBroadcast(
-            R_spatial_variable, [self.domain.lower() + " particle"],
-        )
 
         if self.domain == "Negative":
             N_s_xav_distribution = -self.param.D_n(
                 c_s_xav_distribution, T_k_xav
-            ) * pybamm.grad(c_s_xav_distribution) / R
+            ) * pybamm.grad(c_s_xav_distribution)
         elif self.domain == "Positive":
             N_s_xav_distribution = -self.param.D_p(
                 c_s_xav_distribution, T_k_xav
-            ) * pybamm.grad(c_s_xav_distribution) / R
+            ) * pybamm.grad(c_s_xav_distribution)
 
         # Standard R-averaged flux variables. Average using the area-weighted
         # distribution
@@ -166,13 +163,13 @@ def set_rhs(self, variables):
             self.rhs = {
                 c_s_xav_distribution: -(1 / self.param.C_n)
                 * pybamm.div(N_s_xav_distribution)
-                / R
+                / R ** 2
             }
         elif self.domain == "Positive":
             self.rhs = {
                 c_s_xav_distribution: -(1 / self.param.C_p)
                 * pybamm.div(N_s_xav_distribution)
-                / R
+                / R ** 2
             }
 
     def set_boundary_conditions(self, variables):

pybamm/parameters/size_distribution_parameters.py

@@ -11,8 +11,8 @@ def get_size_distribution_parameters(
     param,
     R_n_av=None,
     R_p_av=None,
-    sd_n=None,
-    sd_p=None,
+    sd_n=0.3,
+    sd_p=0.3,
     R_min_n=None,
     R_min_p=None,
     R_max_n=None,
@@ -64,10 +64,6 @@ def get_size_distribution_parameters(
     R_n_av = R_n_av or R_n_typ
     R_p_av = R_p_av or R_p_typ
 
-    # Standard deviations (scaled by the mean radius)
-    sd_n = sd_n or 0.3
-    sd_p = sd_p or 0.3
-
     # Minimum radii
     R_min_n = R_min_n or np.max([0, 1 - sd_n * 5])
     R_min_p = R_min_p or np.max([0, 1 - sd_p * 5])

tests/integration/test_models/standard_output_tests.py

@@ -304,6 +304,8 @@ def test_concentration_increase_decrease(self):
 
         t, x_n, x_p, r_n, r_p = self.t, self.x_n, self.x_p, self.r_n, self.r_p
 
+        tol = 1e-16
+
         if self.model.options["particle"] in ["quadratic profile", "quartic profile"]:
             # For the assumed polynomial concentration profiles the values
             # can increase/decrease within the particle as the polynomial shifts,
@@ -331,20 +333,21 @@ def test_concentration_increase_decrease(self):
                 self.c_s_p_dist(t[-1], r=r_p, R=R_p) -
                 self.c_s_p_dist(t[0], r=r_p, R=R_p)
             )
+            tol = 1e-15
         else:
             neg_diff = self.c_s_n(t[1:], x_n, r_n) - self.c_s_n(t[:-1], x_n, r_n)
             pos_diff = self.c_s_p(t[1:], x_p, r_p) - self.c_s_p(t[:-1], x_p, r_p)
             neg_end_vs_start = self.c_s_n(t[-1], x_n, r_n) - self.c_s_n(t[0], x_n, r_n)
             pos_end_vs_start = self.c_s_p(t[-1], x_p, r_p) - self.c_s_p(t[0], x_p, r_p)
 
         if self.operating_condition == "discharge":
-            np.testing.assert_array_less(neg_diff, 1e-16)
-            np.testing.assert_array_less(-1e-16, pos_diff)
+            np.testing.assert_array_less(neg_diff, tol)
+            np.testing.assert_array_less(-tol, pos_diff)
             np.testing.assert_array_less(neg_end_vs_start, 0)
             np.testing.assert_array_less(0, pos_end_vs_start)
         elif self.operating_condition == "charge":
-            np.testing.assert_array_less(-1e-16, neg_diff)
-            np.testing.assert_array_less(pos_diff, 1e-16)
+            np.testing.assert_array_less(-tol, neg_diff)
+            np.testing.assert_array_less(pos_diff, tol)
             np.testing.assert_array_less(0, neg_end_vs_start)
             np.testing.assert_array_less(pos_end_vs_start, 0)
         elif self.operating_condition == "off":