#2418 fixing tests

pybamm-team · Nov 20, 2022 · ca8f3ad · ca8f3ad
1 parent 8d8d658
commit ca8f3ad
Show file tree

Hide file tree

Showing 4 changed files with 61 additions and 71 deletions.
diff --git a/pybamm/expression_tree/variable.py b/pybamm/expression_tree/variable.py
@@ -61,6 +61,10 @@ def __init__(
         scale=1,
         reference=0,
     ):
+        if isinstance(scale, numbers.Number):
+            scale = pybamm.Scalar(scale)
+        if isinstance(reference, numbers.Number):
+            reference = pybamm.Scalar(reference)
         self._scale = scale
         self._reference = reference
         super().__init__(

diff --git a/pybamm/models/full_battery_models/lead_acid/basic_full.py b/pybamm/models/full_battery_models/lead_acid/basic_full.py
@@ -118,14 +118,16 @@ def __init__(self, name="Basic full model"):
         j0_p = param.p.prim.j0(c_e_p, T)
         j_p = 2 * j0_p * pybamm.sinh(param.p.prim.ne / 2 * (Feta_RT_p))
 
-        a_j_n = param.n.a * j_n
-        a_j_p = param.p.a * j_p
+        a_n = pybamm.Parameter("Negative electrode surface area to volume ratio [m-1]")
+        a_p = pybamm.Parameter("Positive electrode surface area to volume ratio [m-1]")
+        a_j_n = a_n * j_n
+        a_j_p = a_p * j_p
         a_j = pybamm.concatenation(a_j_n, j_s, a_j_p)
 
         ######################
         # State of Charge
         ######################
-        I = param.dimensional_current_with_time
+        I = param.current_with_time
         # The `rhs` dictionary contains differential equations, with the key being the
         # variable in the d/dt
         self.rhs[Q] = I / 3600
@@ -137,19 +139,19 @@ def __init__(self, name="Basic full model"):
         ######################
         v_n = -pybamm.grad(pressure_n)
         v_p = -pybamm.grad(pressure_p)
-        l_s = param.s.l
-        l_n = param.n.l
+        L_s = param.s.L
+        L_n = param.n.L
         x_s = pybamm.SpatialVariable("x_s", domain="separator")
 
         # Difference in negative and positive electrode velocities determines the
         # velocity in the separator
-        v_n_right = param.n.beta * i_cell
-        v_p_left = param.p.beta * i_cell
-        d_v_s__dx = (v_p_left - v_n_right) / l_s
+        v_n_right = param.n.DeltaV * i_cell
+        v_p_left = param.p.DeltaV * i_cell
+        d_v_s__dx = (v_p_left - v_n_right) / L_s
 
         # Simple formula for velocity in the separator
         div_V_s = -d_v_s__dx
-        v_s = d_v_s__dx * (x_s - l_n) + v_n_right
+        v_s = d_v_s__dx * (x_s - L_n) + v_n_right
 
         # v is the velocity in the x-direction
         # div_V is the divergence of the velocity in the yz-directions
@@ -160,8 +162,8 @@ def __init__(self, name="Basic full model"):
             pybamm.PrimaryBroadcast(0, "positive electrode"),
         )
         # Simple formula for velocity in the separator
-        self.algebraic[pressure_n] = pybamm.div(v_n) - param.n.beta * a_j_n
-        self.algebraic[pressure_p] = pybamm.div(v_p) - param.p.beta * a_j_p
+        self.algebraic[pressure_n] = pybamm.div(v_n) - param.n.DeltaV * a_j_n
+        self.algebraic[pressure_p] = pybamm.div(v_p) - param.p.DeltaV * a_j_p
         self.boundary_conditions[pressure_n] = {
             "left": (pybamm.Scalar(0), "Neumann"),
             "right": (pybamm.Scalar(0), "Dirichlet"),
@@ -213,12 +215,12 @@ def __init__(self, name="Basic full model"):
         ######################
         # Porosity
         ######################
-        Delta_V = pybamm.concatenation(
-            pybamm.PrimaryBroadcast(param.n.Delta_V, "negative electrode"),
+        DeltaVsurf = pybamm.concatenation(
+            pybamm.PrimaryBroadcast(param.n.DeltaVsurf, "negative electrode"),
             pybamm.PrimaryBroadcast(0, "separator"),
-            pybamm.PrimaryBroadcast(param.p.Delta_V, "positive electrode"),
+            pybamm.PrimaryBroadcast(param.p.DeltaVsurf, "positive electrode"),
         )
-        deps_dt = -Delta_V * a_j / param.F
+        deps_dt = -DeltaVsurf * a_j / param.F
         self.rhs[eps] = deps_dt
         self.initial_conditions[eps] = param.epsilon_init
         self.events.extend(

diff --git a/pybamm/models/submodels/particle/base_particle.py b/pybamm/models/submodels/particle/base_particle.py
@@ -183,22 +183,24 @@ def _get_distribution_variables(self, R):
         domain, Domain = self.domain_Domain
         phase_name = self.phase_name
 
-        R_typ = self.phase_param.R_typ
+        R_typ = self.phase_param.R_typ  # [m]
         # Particle-size distribution (area-weighted)
-        f_a_dist = self.phase_param.f_a_dist(R)
+        f_a_dist = self.phase_param.f_a_dist(R)  # [m-1]
 
         # Ensure the distribution is normalised, irrespective of discretisation
         # or user input
-        f_a_dist = f_a_dist / pybamm.Integral(f_a_dist, R)
+        f_a_dist = f_a_dist / pybamm.Integral(f_a_dist, R)  # [m-1]
 
         # Volume-weighted particle-size distribution
-        f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R)
+        f_v_dist = R * f_a_dist / pybamm.Integral(R * f_a_dist, R)  # [m-1]
 
         # Number-based particle-size distribution
-        f_num_dist = (f_a_dist / R**2) / pybamm.Integral(f_a_dist / R**2, R)
+        f_num_dist = (f_a_dist / R**2) / pybamm.Integral(
+            f_a_dist / R**2, R
+        )  # [m-1]
 
         # True mean radii and standard deviations, calculated from the f_a_dist that
-        # was given
+        # was given, all have units [m]
         R_num_mean = pybamm.Integral(R * f_num_dist, R)
         R_a_mean = pybamm.Integral(R * f_a_dist, R)
         R_v_mean = pybamm.Integral(R * f_v_dist, R)
@@ -233,50 +235,30 @@ def _get_distribution_variables(self, R):
             )
 
         variables = {
-            f"{Domain} {phase_name}particle sizes": R,
-            f"{Domain} {phase_name}particle sizes [m]": R * R_typ,
-            f"{Domain} area-weighted {phase_name}particle-size distribution": f_a_dist,
+            f"{Domain} {phase_name}particle sizes": R / R_typ,
+            f"{Domain} {phase_name}particle sizes [m]": R,
             f"{Domain} area-weighted {phase_name}particle-size"
-            " distribution [m-1]": f_a_dist / R_typ,
+            " distribution [m-1]": f_a_dist,
             f"{Domain} volume-weighted {phase_name}particle-size"
-            " distribution": f_v_dist,
-            f"{Domain} volume-weighted {phase_name}particle-size"
-            " distribution [m-1]": f_v_dist / R_typ,
-            f"{Domain} number-based {phase_name}particle-size"
-            " distribution": f_num_dist,
+            " distribution [m-1]": f_v_dist,
             f"{Domain} number-based {phase_name}particle-size"
-            " distribution [m-1]": f_num_dist / R_typ,
-            f"{Domain} area-weighted mean particle radius": R_a_mean,
-            f"{Domain} area-weighted mean particle radius [m]": R_a_mean * R_typ,
-            f"{Domain} volume-weighted mean particle radius": R_v_mean,
-            f"{Domain} volume-weighted mean particle radius [m]": R_v_mean * R_typ,
-            f"{Domain} number-based mean particle radius": R_num_mean,
-            f"{Domain} number-based mean particle radius [m]": R_num_mean * R_typ,
+            " distribution [m-1]": f_num_dist,
+            f"{Domain} area-weighted mean particle radius [m]": R_a_mean,
+            f"{Domain} volume-weighted mean particle radius [m]": R_v_mean,
+            f"{Domain} number-based mean particle radius [m]": R_num_mean,
             f"{Domain} area-weighted {phase_name}particle-size"
-            " standard deviation": sd_a,
-            f"{Domain} area-weighted {phase_name}particle-size"
-            " standard deviation [m]": sd_a * R_typ,
-            f"{Domain} volume-weighted {phase_name}particle-size"
-            " standard deviation": sd_v,
+            " standard deviation [m]": sd_a,
             f"{Domain} volume-weighted {phase_name}particle-size"
-            " standard deviation [m]": sd_v * R_typ,
+            " standard deviation [m]": sd_v,
             f"{Domain} number-based {phase_name}particle-size"
-            " standard deviation": sd_num,
-            f"{Domain} number-based {phase_name}particle-size"
-            " standard deviation [m]": sd_num * R_typ,
+            " standard deviation [m]": sd_num,
             # X-averaged distributions
             f"X-averaged {domain} area-weighted {phase_name}particle-size "
-            "distribution": f_a_dist_xav,
-            f"X-averaged {domain} area-weighted {phase_name}particle-size "
-            "distribution [m-1]": f_a_dist_xav / R_typ,
+            "distribution [m-1]": f_a_dist_xav,
             f"X-averaged {domain} volume-weighted {phase_name}particle-size "
-            "distribution": f_v_dist_xav,
-            f"X-averaged {domain} volume-weighted {phase_name}particle-size "
-            "distribution [m-1]": f_v_dist_xav / R_typ,
-            f"X-averaged {domain} number-based {phase_name}particle-size "
-            "distribution": f_num_dist_xav,
+            "distribution [m-1]": f_v_dist_xav,
             f"X-averaged {domain} number-based {phase_name}particle-size "
-            "distribution [m-1]": f_num_dist_xav / R_typ,
+            "distribution [m-1]": f_num_dist_xav,
         }
 
         return variables
@@ -364,27 +346,27 @@ def _get_standard_concentration_distribution_variables(self, c_s):
 
         variables = {
             f"Average {domain} {phase_name}particle concentration "
-            "distribution": c_s_av_distribution,
+            "distribution": c_s_av_distribution / c_scale,
             f"{Domain} {phase_name}particle concentration "
-            "distribution": c_s_distribution,
+            "distribution": c_s_distribution / c_scale,
             f"{Domain} {phase_name}particle concentration distribution "
-            "[mol.m-3]": c_scale * c_s_distribution,
+            "[mol.m-3]": c_s_distribution,
             f"R-averaged {domain} {phase_name}particle concentration "
-            "distribution": c_s_rav_distribution,
+            "distribution": c_s_rav_distribution / c_scale,
             f"R-averaged {domain} {phase_name}particle concentration distribution "
-            "[mol.m-3]": c_scale * c_s_rav_distribution,
+            "[mol.m-3]": c_s_rav_distribution,
             f"X-averaged {domain} {phase_name}particle concentration "
-            "distribution": c_s_xav_distribution,
+            "distribution": c_s_xav_distribution / c_scale,
             f"X-averaged {domain} {phase_name}particle concentration distribution "
-            "[mol.m-3]": c_scale * c_s_xav_distribution,
+            "[mol.m-3]": c_s_xav_distribution,
             f"X-averaged {domain} {phase_name}particle surface concentration"
-            " distribution": c_s_surf_xav_distribution,
+            " distribution": c_s_surf_xav_distribution / c_scale,
             f"X-averaged {domain} {phase_name}particle surface concentration "
-            "distribution [mol.m-3]": c_scale * c_s_surf_xav_distribution,
+            "distribution [mol.m-3]": c_s_surf_xav_distribution,
             f"{Domain} {phase_name}particle surface concentration"
-            " distribution": c_s_surf_distribution,
+            " distribution": c_s_surf_distribution / c_scale,
             f"{Domain} {phase_name}particle surface concentration"
-            " distribution [mol.m-3]": c_scale * c_s_surf_distribution,
+            " distribution [mol.m-3]": c_s_surf_distribution,
         }
         return variables
 
@@ -416,8 +398,9 @@ def _get_standard_flux_distribution_variables(self, N_s):
 
         variables = {
             f"X-averaged {domain} {phase_name}particle flux "
-            "distribution": N_s_xav_distribution,
-            f"{Domain} {phase_name}particle flux distribution": N_s_distribution,
+            "distribution [mol.m-2.s-1]": N_s_xav_distribution,
+            f"{Domain} {phase_name}particle flux "
+            "distribution [mol.m-2.s-1]": N_s_distribution,
         }
 
         return variables

diff --git a/pybamm/models/submodels/particle/fickian_diffusion.py b/pybamm/models/submodels/particle/fickian_diffusion.py
@@ -83,7 +83,8 @@ def get_fundamental_variables(self):
                 )
                 variables = self._get_distribution_variables(R)
                 f_v_dist = variables[
-                    f"{Domain} volume-weighted particle-size distribution [m]"
+                    f"{Domain} volume-weighted {phase_name}"
+                    "particle-size distribution [m-1]"
                 ]
             else:
                 c_s_distribution = pybamm.Variable(
@@ -105,8 +106,8 @@ def get_fundamental_variables(self):
                 )
                 variables = self._get_distribution_variables(R)
                 f_v_dist = variables[
-                    f"X-averaged {domain} volume-weighted "
-                    "particle-size distribution [m]"
+                    f"X-averaged {domain} volume-weighted {phase_name}"
+                    "particle-size distribution [m-1]"
                 ]
 
             # Standard concentration distribution variables (size-dependent)