#936 remove some inputs

CHANGELOG.md

@@ -10,6 +10,10 @@
 
 -   Reformatted electrolyte submodels ([#927](https://github.com/pybamm-team/PyBaMM/pull/927))
 
+## Breaking changes
+
+-   Removed some inputs like `T_inf`, `R_g` and activation energies to some of the standard function parameters. This is because each of those inputs is specific to a particular function (e.g. the reference temperature at which the function was measured). To change a property such as the activation energy, users should create a new function, specifying the relevant property as a `Parameter` or `InputParameter` ([#942](https://github.com/pybamm-team/PyBaMM/pull/942))
+
 # [v0.2.1](https://github.com/pybamm-team/PyBaMM/tree/v0.2.1) - 2020-03-31
 
 New expression tree node types, models, parameter sets and solvers, as well as general bug fixes and new examples.

pybamm/__init__.py

@@ -158,6 +158,7 @@ def version(formatted=False):
 # Parameters class and methods
 #
 from .parameters.parameter_values import ParameterValues
+from .parameters import constants
 from .parameters import geometric_parameters
 from .parameters import electrical_parameters
 from .parameters import thermal_parameters

pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_diffusivity_Chen2020.py

@@ -1,35 +1,33 @@
-from pybamm import exp
+from pybamm import exp, constants
 
 
-def graphite_LGM50_diffusivity_Chen2020(sto, T, T_inf, E_D_s, R_g):
+def graphite_LGM50_diffusivity_Chen2020(sto, T):
     """
       LG M50 Graphite diffusivity as a function of stochiometry, in this case the
       diffusivity is taken to be a constant. The value is taken from [1].
+
       References
       ----------
       .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
       Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
       Parameterization of Multi-scale Lithium-ion Battery Models." Submitted for
       publication (2020).
+
       Parameters
       ----------
-      sto: :class: `numpy.Array`
+      sto: :class:`pybamm.Symbol`
          Electrode stochiometry
-      T: :class: `numpy.Array`
+      T: :class:`pybamm.Symbol`
          Dimensional temperature
-      T_inf: double
-         Reference temperature
-      E_D_s: double
-         Solid diffusion activation energy
-      R_g: double
-         The ideal gas constant
+
       Returns
       -------
-      : double
+      :class:`pybamm.Symbol`
          Solid diffusivity
    """
 
     D_ref = 3.3e-14
-    arrhenius = exp(E_D_s / R_g * (1 / T_inf - 1 / T))
+    E_D_s = 42770
+    arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))
 
     return D_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/graphite_LGM50_electrolyte_reaction_rate_Chen2020.py

@@ -1,32 +1,30 @@
-from pybamm import exp
+from pybamm import exp, constants
 
 
-def graphite_LGM50_electrolyte_reaction_rate_Chen2020(T, T_inf, E_r, R_g):
+def graphite_LGM50_electrolyte_reaction_rate_Chen2020(T):
     """
     Reaction rate for Butler-Volmer reactions between graphite and LiPF6 in EC:DMC.
+
     References
     ----------
     .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W.
     Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for
     Parameterization of Multi-scale Lithium-ion Battery Models." Submitted for
     publication (2020).
+
     Parameters
     ----------
-    T: :class: `numpy.Array`
+    T: :class:`pybamm.Symbol`
         Dimensional temperature
-    T_inf: double
-        Reference temperature
-    E_r: double
-        Reaction activation energy
-    R_g: double
-        The ideal gas constant
+
     Returns
     -------
-    :`numpy.Array`
+    :class:`pybamm.Symbol`
         Reaction rate
     """
 
     m_ref = 6.48e-7
-    arrhenius = exp(E_r / R_g * (1 / T_inf - 1 / T))
+    E_r = 35000
+    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))
 
     return m_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Chen2020/parameters.csv

@@ -31,8 +31,5 @@ Negative electrode specific heat capacity [J.kg-1.K-1],700,default,
 Negative electrode thermal conductivity [W.m-1.K-1],1.7,default,
 Negative electrode OCP entropic change [V.K-1],0,,
 ,,,
-# Activation energies,,,
-Reference temperature [K],298.15,25C,
-Negative electrode reaction rate,[function]graphite_LGM50_electrolyte_reaction_rate_Chen2020,,
-Negative reaction rate activation energy [J.mol-1],35000,Chen 2020,
-Negative solid diffusion activation energy [J.mol-1],42770,default,
+# Reaction rate,,,
+Negative electrode reaction rate,[function]graphite_LGM50_electrolyte_reaction_rate_Chen2020,,

pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_diffusivity_Ecker2015.py

@@ -1,7 +1,7 @@
-from pybamm import exp
+from pybamm import exp, constants
 
 
-def graphite_diffusivity_Ecker2015(sto, T, T_inf, E_D_s, R_g):
+def graphite_diffusivity_Ecker2015(sto, T):
     """
     Graphite diffusivity as a function of stochiometry [1, 2, 3].
 
@@ -19,24 +19,19 @@ def graphite_diffusivity_Ecker2015(sto, T, T_inf, E_D_s, R_g):
 
     Parameters
     ----------
-    sto: :class: `numpy.Array`
+    sto: :class:`pybamm.Symbol`
         Electrode stochiometry
-    T: :class: `numpy.Array`
+    T: :class:`pybamm.Symbol`
         Dimensional temperature
-    T_inf: double
-        Reference temperature
-    E_D_s: double
-        Solid diffusion activation energy
-    R_g: double
-        The ideal gas constant
 
     Returns
     -------
-    : double
+    :class:`pybamm.Symbol`
         Solid diffusivity
    """
 
     D_ref = 8.4e-13 * exp(-11.3 * sto) + 8.2e-15
-    arrhenius = exp(-E_D_s / (R_g * T)) * exp(E_D_s / (R_g * 296))
+    E_D_s = 3.03e4
+    arrhenius = exp(-E_D_s / (constants.R * T)) * exp(E_D_s / (constants.R * 296))
 
     return D_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_electrolyte_reaction_rate_Ecker2015.py

@@ -1,8 +1,7 @@
-from pybamm import exp
-from scipy import constants
+from pybamm import exp, constants
 
 
-def graphite_electrolyte_reaction_rate_Ecker2015(T, T_inf, E_r, R_g):
+def graphite_electrolyte_reaction_rate_Ecker2015(T):
     """
     Reaction rate for Butler-Volmer reactions between graphite and LiPF6 in EC:DMC.
 
@@ -20,27 +19,21 @@ def graphite_electrolyte_reaction_rate_Ecker2015(T, T_inf, E_r, R_g):
 
     Parameters
     ----------
-    T: :class: `numpy.Array`
+    T: :class:`pybamm.Symbol`
         Dimensional temperature
-    T_inf: double
-        Reference temperature
-    E_r: double
-        Reaction activation energy
-    R_g: double
-        The ideal gas constant
 
     Returns
     -------
-    :`numpy.Array`
+    :class:`pybamm.Symbol`
         Reaction rate
     """
 
     k_ref = 1.995 * 1e-10
 
     # multiply by Faraday's constant to get correct units
-    F = constants.physical_constants["Faraday constant"][0]
-    m_ref = F * k_ref
+    m_ref = constants.F * k_ref
+    E_r = 53400
 
-    arrhenius = exp(-E_r / (R_g * T)) * exp(E_r / (R_g * T_inf))
+    arrhenius = exp(-E_r / (constants.R * T)) * exp(E_r / (constants.R * 296.15))
 
     return m_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/graphite_ocp_Ecker2015_function.py

@@ -19,12 +19,12 @@ def graphite_ocp_Ecker2015_function(sto):
 
     Parameters
     ----------
-    sto: :class: `numpy.Array`
+    sto: :class:`pybamm.Symbol`
         Electrode stochiometry
 
     Returns
     -------
-    : double
+    :class:`pybamm.Symbol`
         Open circuit potential
     """
 

pybamm/input/parameters/lithium-ion/anodes/graphite_Ecker2015/parameters.csv

@@ -25,8 +25,5 @@ Negative electrode electrons in reaction,1,,
 # Thermal parameters,,,
 Negative electrode OCP entropic change [V.K-1],0,,
 ,,,
-# Activation energies,,,
-Reference temperature [K],296.15,23C,
+# Reaction rate,,,
 Negative electrode reaction rate,[function]graphite_electrolyte_reaction_rate_Ecker2015,,
-Negative reaction rate activation energy [J.mol-1],53400,,
-Negative solid diffusion activation energy [J.mol-1],3.03E+04,,

pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_diffusivity_Kim2011.py

@@ -1,37 +1,32 @@
-from pybamm import exp
+from pybamm import exp, constants
 
 
-def graphite_diffusivity_Kim2011(sto, T, T_inf, E_D_s, R_g):
+def graphite_diffusivity_Kim2011(sto, T):
     """
-        Graphite diffusivity [1].
+    Graphite diffusivity [1].
 
-        References
-        ----------
-        .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
-        (2011). Multi-domain modeling of lithium-ion batteries encompassing
-        multi-physics in varied length scales. Journal of The Electrochemical
-        Society, 158(8), A955-A969.
+    References
+    ----------
+    .. [1] Kim, G. H., Smith, K., Lee, K. J., Santhanagopalan, S., & Pesaran, A.
+    (2011). Multi-domain modeling of lithium-ion batteries encompassing
+    multi-physics in varied length scales. Journal of The Electrochemical
+    Society, 158(8), A955-A969.
 
-        Parameters
-        ----------
-        sto: :class: `numpy.Array`
-            Electrode stochiometry
-        T: :class: `numpy.Array`
-            Dimensional temperature
-        T_inf: double
-            Reference temperature
-        E_D_s: double
-            Solid diffusion activation energy
-        R_g: double
-            The ideal gas constant
+    Parameters
+    ----------
+    sto: :class:`pybamm.Symbol`
+        Electrode stochiometry
+    T: :class:`pybamm.Symbol`
+        Dimensional temperature
 
-        Returns
-        -------
-        : double
-            Solid diffusivity
+    Returns
+    -------
+    :class:`pybamm.Symbol`
+        Solid diffusivity
    """
 
     D_ref = 9 * 10 ** (-14)
-    arrhenius = exp(E_D_s / R_g * (1 / T_inf - 1 / T))
+    E_D_s = 4e3
+    arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))
 
     return D_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/graphite_electrolyte_reaction_rate_Kim2011.py

@@ -1,7 +1,7 @@
-from pybamm import exp
+from pybamm import exp, constants
 
 
-def graphite_electrolyte_reaction_rate_Kim2011(T, T_inf, E_r, R_g):
+def graphite_electrolyte_reaction_rate_Kim2011(T):
     """
     Reaction rate for Butler-Volmer reactions between graphite and LiPF6 in EC:DMC
     [1].
@@ -15,18 +15,11 @@ def graphite_electrolyte_reaction_rate_Kim2011(T, T_inf, E_r, R_g):
 
     Parameters
     ----------
-    T: :class: `numpy.Array`
+    T: :class:`pybamm.Symbol`
         Dimensional temperature
-    T_inf: double
-        Reference temperature
-    E_r: double
-        Reaction activation energy
-    R_g: double
-        The ideal gas constant
-
     Returns
     -------
-    :`numpy.Array`
+    :class:`pybamm.Symbol`
         Reaction rate
     """
 
@@ -43,6 +36,7 @@ def graphite_electrolyte_reaction_rate_Kim2011(T, T_inf, E_r, R_g):
         / (c_e_ref ** alpha * (c_s_n_max - c_s_n_ref) ** alpha * c_s_n_ref ** alpha)
     )
 
-    arrhenius = exp(E_r / R_g * (1 / T_inf - 1 / T))
+    E_r = 3e4
+    arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T))
 
     return m_ref * arrhenius

pybamm/input/parameters/lithium-ion/anodes/graphite_Kim2011/parameters.csv

@@ -31,8 +31,5 @@ Negative electrode specific heat capacity [J.kg-1.K-1],700,,
 Negative electrode thermal conductivity [W.m-1.K-1],1.1339,1.7 * 0.667,
 Negative electrode OCP entropic change [V.K-1],0,,
 ,,,
-# Activation energies,,,
-Reference temperature [K],298.15,25C,
+# Reaction rate,,,
 Negative electrode reaction rate,[function]graphite_electrolyte_reaction_rate_Kim2011,,
-Negative reaction rate activation energy [J.mol-1],3E4,,
-Negative solid diffusion activation energy [J.mol-1],4E3,,

pybamm/input/parameters/lithium-ion/anodes/graphite_UMBL_Mohtat2020/graphite_diffusivity_PeymanMPM.py

@@ -1,7 +1,7 @@
-import pybamm
+from pybamm import exp, constants
 
 
-def graphite_diffusivity_PeymanMPM(sto, T, T_inf, E_D_s, R_g):
+def graphite_diffusivity_PeymanMPM(sto, T):
     """
     Graphite diffusivity as a function of stochiometry, in this case the
     diffusivity is taken to be a constant. The value is taken from Peyman MPM.
@@ -12,25 +12,20 @@ def graphite_diffusivity_PeymanMPM(sto, T, T_inf, E_D_s, R_g):
 
     Parameters
     ----------
-    sto: :class: `numpy.Array`
+    sto: :class:`pybamm.Symbol`
         Electrode stochiometry
-    T: :class: `numpy.Array`
+    T: :class:`pybamm.Symbol`
         Dimensional temperature
-    T_inf: double
-        Reference temperature
-    E_D_s: double
-        Solid diffusion activation energy
-    R_g: double
-        The ideal gas constant
 
     Returns
     -------
-    : double
+    :class:`pybamm.Symbol`
         Solid diffusivity
    """
 
     D_ref = 5.0 * 10 ** (-15)
-    arrhenius = pybamm.exp(E_D_s / R_g * (1 / T_inf - 1 / T))
+    E_D_s = 42770
+    arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T))
 
     # Removing the fudge factor 0 * sto requires different handling of either
     # either simplifications or how sto is passed into this function.