pybamm-team#573 update test files to check parameters work

results/LGM50/test_LGM50.py

@@ -7,7 +7,7 @@
 pybamm.set_logging_level("INFO")
 experiment = pybamm.Experiment(
     [
-        "Discharge at C/2 until 3.2 V",
+        "Discharge at 1.5C until 2.5 V",
         "Rest for 2 hours",
     ],
     period="10 seconds",
@@ -31,7 +31,7 @@
 )
 
 cspmax = 50483 * 1.25  #1.25
-csnmax = 29583 * 1.12  #1.13
+csnmax = 29583 * 1.13  #1.13
 
 param["Initial concentration in negative electrode [mol.m-3]"] = 0.90 * csnmax
 param["Initial concentration in positive electrode [mol.m-3]"] = 0.26 * cspmax
@@ -42,6 +42,8 @@
 param["Negative electrode Bruggeman coefficient (electrolyte)"] = 1.5
 param["Positive electrode Bruggeman coefficient (electrolyte)"] = 1.5
 param["Separator Bruggeman coefficient (electrolyte)"] = 1.5
+param["Positive electrode diffusivity [m2.s-1]"] = 4E-15
+param["Negative electrode diffusivity [m2.s-1]"] = 3.3E-14
 
 # param["Positive electrode conductivity [S.m-1]"] = 10 # Inf detected
 # param["Maximum concentration in positive electrode [mol.m-3]"] = 51217.9257309275   # Inf detected
@@ -54,26 +56,6 @@
 
 # param["Negative electrode OCP [V]"] = "[function]graphite_mcmb2528_ocp_Dualfoil1998"
 
-plt.figure(num=1, figsize=(6, 4))
-plt.plot(
-    param["Positive electrode OCP [V]"][1][:, 0],
-    param["Positive electrode OCP [V]"][1][:, 1]
-)
-plt.xlabel("Stoichiometry")
-plt.ylabel("OCV cathode [V]")
-plt.tight_layout()
-
-plt.figure(num=2, figsize=(6, 4))
-plt.plot(
-    param["Negative electrode OCP [V]"][1][:, 0],
-    param["Negative electrode OCP [V]"][1][:, 1]
-)
-plt.xlabel("Stoichiometry")
-plt.ylabel("OCV anode [V]")
-plt.tight_layout()
-
-plt.show()
-
 sim = pybamm.Simulation(
     model,
     parameter_values=param,

results/LGM50/test_OCVs.py

@@ -0,0 +1,18 @@
+import pybamm
+import matplotlib.pyplot as plt
+import numpy as np
+
+param = pybamm.standard_parameters_lithium_ion
+parameter_values = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Chen2020)
+sto = pybamm.Vector(np.linspace(0, 1, 200))
+T = 300
+sto_eval = sto.evaluate()
+fig, ax = plt.subplots()
+ocp_n = parameter_values.process_symbol(param.U_n_dimensional(sto, T)).evaluate()
+ocp_p = parameter_values.process_symbol(param.U_p_dimensional(sto, T)).evaluate()
+ax.plot(sto_eval, ocp_n, label="negative")
+ax.plot(sto_eval, ocp_p, label="positive")
+ax.set_xlabel("Stoichiometry")
+ax.set_ylabel("U")
+ax.legend()
+plt.show()