Repository: pybamm-team/PyBaMM

Issue:
Evaluate dimensionless parameters
Create helper function to easily evaluate a set of dimensionless parameters from a set of dimensional ones

Discussion:

Patch:
diff --git a/docs/source/parameters/index.rst b/docs/source/parameters/index.rst
index e6b70c8b5..3212b4ef0 100644
--- a/docs/source/parameters/index.rst
+++ b/docs/source/parameters/index.rst
@@ -8,3 +8,4 @@ Parameters
   electrical_parameters
   standard_parameters_lithium_ion
   standard_parameters_lead_acid
+  print_parameters
diff --git a/docs/source/parameters/print_parameters.rst b/docs/source/parameters/print_parameters.rst
new file mode 100644
index 000000000..d32df443c
--- /dev/null
+++ b/docs/source/parameters/print_parameters.rst
@@ -0,0 +1,6 @@
+Print parameters
+================
+
+.. autofunction:: pybamm.print_parameters
+
+.. autofunction:: pybamm.print_evaluated_parameters
diff --git a/examples/notebooks/parameter-values.ipynb b/examples/notebooks/parameter-values.ipynb
index aee9630af..6812798d8 100644
--- a/examples/notebooks/parameter-values.ipynb
+++ b/examples/notebooks/parameter-values.ipynb
@@ -290,7 +290,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 936x288 with 2 Axes>"
       ]
@@ -355,6 +355,54 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Printing parameter values\n",
+    "\n",
+    "In most models, it is useful to define dimensionless parameters, which are combinations of other parameters. However, since parameters objects must be processed by the `ParameterValues` class before they can be evaluated, it can be difficult to quickly check the value of a dimensionless parameter. \n",
+    "\n",
+    "You can print all of the dimensionless parameters in a model by using the `print_parameters` function. Note that the `print_parameters` function also gives the dependence of the parameters on C-rate (as some dimensionless parameters vary with C-rate), but we can ignore that here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "a: 3.0\n",
+      "b: 2.0\n",
+      "a + b: 5.0\n",
+      "a * b: 6.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "parameters = {\"a\": a, \"b\": b, \"a + b\": a + b, \"a * b\": a * b}\n",
+    "param_eval = pybamm.print_parameters(parameters, parameter_values)\n",
+    "for name, (value,C_dependence) in param_eval.items():\n",
+    "    print(\"{}: {}\".format(name, value))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you provide an output file to `print_parameters`, the parameters will be printed to that output file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/input/parameters/lead-acid/default.csv b/input/parameters/lead-acid/default.csv
index 0a90cb840..417a545b6 100644
--- a/input/parameters/lead-acid/default.csv
+++ b/input/parameters/lead-acid/default.csv
@@ -28,8 +28,16 @@ Partial molar volume of cations [m3.mol-1],1.35E-05,,
 Cation stoichiometry,1,,Hydrogen cations
 Anion stoichiometry,1,,Hydrogen sulfate anions
 Molar mass of water [kg.mol-1],0.01801,,
-Molar mass of cations [kg.mol-1],,,Hydrogen cations
-Molar mass of anions [kg.mol-1],,,Hydrogen sulfate anions
+Molar mass of cations [kg.mol-1],0.001,lide1992crc,Hydrogen cations
+Molar mass of anions [kg.mol-1],0.097,lide1992crc,Hydrogen sulfate anions
+,,,
+# Other species properties,,,
+Oxygen diffusivity [m2.s-1],2.1e-9,cussler2009diffusion
+Hydrogen diffusivity [m2.s-1],4.5e-9,cussler2009diffusion
+Partial molar volume of oxygen molecules [m3.mol-1],3.21E-5,zhou2001partial,
+Partial molar volume of hydrogen molecules [m3.mol-1],2.31E-5,zhou2001partial,
+Molar mass of oxygen molecules [kg.mol-1],0.032,lide1992crc,
+Molar mass of hydrogen molecules [kg.mol-1],0.002,lide1992crc,
 ,,,
 # Electrode properties,,,
 Negative electrode conductivity [S.m-1],4800000,,Lead
@@ -41,6 +49,8 @@ Maximum porosity of positive electrode,0.57,,
 Molar volume of lead [m3.mol-1],1.82539682539683E-05,,Calculated as () divided by ()
 Molar volume of lead-dioxide [m3.mol-1],2.54797441364606E-05,,
 Molar volume of lead sulfate [m3.mol-1],4.81717011128776E-05,,
+Negative electrode volumetric capacity [C.m-3],3.473e9,bernardi1995mathematical,
+Positive electrode volumetric capacity [C.m-3],2.745e9,bernardi1995mathematical,
 ,,,
 # Microstructure,,,
 Negative electrode surface area density [m-1],2300000,,
@@ -54,15 +64,23 @@ Negative electrode cation signed stoichiometry,-1,,
 Positive electrode cation signed stoichiometry,-3,,
 Negative electrode electrons in reaction,2,,
 Positive electrode electrons in reaction,2,,
+Negative electrode reference exchange-current density [A.m-2],0.06,srinivasan2003mathematical,
+Positive electrode reference exchange-current density [A.m-2],0.004,srinivasan2003mathematical,
+Signed stoichiometry of cations (oxygen reaction),-4,,
+Signed stoichiometry of water (oxygen reaction),1,,
+Signed stoichiometry of oxygen (oxygen reaction),-1,,
+Electrons in oxygen reaction,4,,
+Negative electrode reference exchange-current density (oxygen) [A.m-2],2.5E-32,srinivasan2003mathematical,
+Positive electrode reference exchange-current density (oxygen) [A.m-2],2.5E-23,srinivasan2003mathematical,
+Reference oxygen molecule concentration [mol.m-3],1000,srinivasan2003mathematical,
+Oxygen reference OCP vs SHE [V],1.229,srinivasan2003mathematical,
+Signed stoichiometry of cations (hydrogen reaction),-2,,
+Signed stoichiometry of hydrogen (hydrogen reaction),1,,
+Electrons in hydrogen reaction,2,,
+Negative electrode reference exchange-current density (hydrogen) [A.m-2],1.56E-11,srinivasan2003mathematical,
+Positive electrode reference exchange-current density (hydrogen) [A.m-2],0,srinivasan2003mathematical,
+Hydrogen reference OCP vs SHE [V],0,srinivasan2003mathematical,
 Double-layer capacity [F.m-2],0.2,,
-Negative electrode reference exchange-current density [A.m-2],0.06,,
-Positive electrode reference exchange-current density [A.m-2],0.004,,
-Oxygen reference OCP vs SHE [V],1.229,,
-"Ref exchange-current density (O2 reaction, neg electrode) [A.m-2]",2.5e-32,,
-"Ref exchange-current density (O2 reaction, pos electrode) [A.m-2]",1.56e-11,,
-Hydrogen reference OCP vs SHE [V],0,,
-"Ref exchange-current density (H2 reaction, neg electrode) [A.m-2]",2.5e-23,,
-"Ref exchange-current density (H2 reactio, pos electrode) [A.m-2]",0,,
 ,,,
 # Temperature,,,
 Maximum temperature [K],333.15,,
diff --git a/pybamm/__init__.py b/pybamm/__init__.py
index 4da92c650..4e51a4ed0 100644
--- a/pybamm/__init__.py
+++ b/pybamm/__init__.py
@@ -151,6 +151,7 @@ from .parameters import standard_current_functions
 from .parameters import geometric_parameters
 from .parameters import electrical_parameters
 from .parameters import standard_parameters_lithium_ion, standard_parameters_lead_acid
+from .parameters.print_parameters import print_parameters, print_evaluated_parameters
 
 #
 # Geometry
diff --git a/pybamm/models/base_models.py b/pybamm/models/base_models.py
index b67719571..859ec3fb7 100644
--- a/pybamm/models/base_models.py
+++ b/pybamm/models/base_models.py
@@ -574,6 +574,9 @@ class LeadAcidBaseModel(StandardBatteryBaseModel):
                 "Electrolyte conductivity": os.path.join(
                     input_path, "electrolyte_conductivity_Gu1997.py"
                 ),
+                "Electrolyte viscosity": os.path.join(
+                    input_path, "electrolyte_viscosity_Chapman1968.py"
+                ),
                 "Darken thermodynamic factor": os.path.join(
                     input_path, "darken_thermodynamic_factor_Chapman1968.py"
                 ),
diff --git a/pybamm/parameters/print_parameters.py b/pybamm/parameters/print_parameters.py
new file mode 100644
index 000000000..9c8916918
--- /dev/null
+++ b/pybamm/parameters/print_parameters.py
@@ -0,0 +1,118 @@
+#
+# Print parameters
+#
+import pybamm
+from collections import defaultdict
+
+# Set list of attributes to ignore, for when we are evaluating parameters from a class
+# of parameters
+ignore = [
+    "__name__",
+    "__doc__",
+    "__package__",
+    "__loader__",
+    "__spec__",
+    "__file__",
+    "__cached__",
+    "__builtins__",
+    "absolute_import",
+    "division",
+    "print_function",
+    "unicode_literals",
+    "pybamm",
+    "constants",
+]
+
+
+def print_parameters(parameters, parameter_values, output_file=None):
+    """
+    Return dictionary of evaluated parameters, and optionally print these evaluated
+    parameters to an output file.
+    For dimensionless parameters that depend on the C-rate, the value is given as a
+    function of the C-rate (either x * Crate or x / Crate depending on the dependence)
+
+    Parameters
+    ----------
+    parameters : class or dict containing :class:`pybamm.Parameter` objects
+        Class or dictionary containing all the parameters to be evaluated
+    parameter_values : :class:`pybamm.ParameterValues`
+        The class of parameter values
+    output_file : string, optional
+        The file to print parameters to. If None, the parameters are not printed, and
+        this function simply acts as a test that all the parameters can be evaluated,
+        and returns the dictionary of evaluated parameters.
+
+    Returns
+    -------
+    evaluated_parameters : defaultdict
+        The evaluated parameters, for further processing if needed
+
+    Notes
+    -----
+    A C-rate of 1 C is the current required to fully discharge the battery in 1 hour,
+    2 C is current to discharge to the battery in 0.5 hours, etc
+    """
+    # If 'parameters' is a class, extract the dict
+    if not isinstance(parameters, dict):
+        parameters = {k: v for k, v in parameters.__dict__.items() if k not in ignore}
+
+    evaluated_parameters = defaultdict(list)
+    # Calculate the currents required for C-rates of 1C and C / 10
+    try:
+        current_for_1C = parameter_values["Cell capacity [A.h]"]
+    except KeyError:
+        current_for_1C = 1
+    current_for_C_over_10 = current_for_1C / 10
+    # Calculate parameters for each C-rate
+    for current in [current_for_1C, current_for_C_over_10]:
+        # Update Crate
+        parameter_values.update({"Typical current [A]": current})
+        for name, symbol in parameters.items():
+            if not callable(symbol):
+                proc_symbol = parameter_values.process_symbol(symbol)
+                if not (
+                    callable(proc_symbol)
+                    or isinstance(proc_symbol, pybamm.Concatenation)
+                ):
+                    evaluated_parameters[name].append(proc_symbol.evaluate(t=0))
+
+    # Calculate C-dependence of the parameters based on the difference between the
+    # value at 1C and the value at C / 10
+    for name, values in evaluated_parameters.items():
+        if abs(values[0] - values[1]) < 1e-10:
+            C_dependence = ""
+        elif abs(values[0] - 10 * values[1]) < 1e-10:
+            C_dependence = " * Crate"
+        elif abs(10 * values[0] - values[1]) < 1e-10:
+            C_dependence = " / Crate"
+        evaluated_parameters[name] = (values[0], C_dependence)
+
+    # Print the evaluated_parameters dict to output_file
+    if output_file:
+        print_evaluated_parameters(evaluated_parameters, output_file)
+
+    return evaluated_parameters
+
+
+def print_evaluated_parameters(evaluated_parameters, output_file):
+    """
+    Print a dictionary of evaluated parameters to an output file
+
+    Parameters
+    ----------
+    evaluated_parameters : defaultdict
+        The evaluated parameters, for further processing if needed
+    output_file : string, optional
+        The file to print parameters to. If None, the parameters are not printed, and
+        this function simply acts as a test that all the parameters can be evaluated
+
+    """
+    # Get column width for pretty printing
+    column_width = max(len(name) for name in evaluated_parameters.keys())
+    s = "{{:>{}}}".format(column_width)
+    with open(output_file, "w") as file:
+        for name, (value, C_dependence) in sorted(evaluated_parameters.items()):
+            if 0.001 < abs(value) < 1000:
+                file.write((s + " : {:10.4g}{!s}\n").format(name, value, C_dependence))
+            else:
+                file.write((s + " : {:10.3E}{!s}\n").format(name, value, C_dependence))
diff --git a/pybamm/parameters/standard_parameters_lead_acid.py b/pybamm/parameters/standard_parameters_lead_acid.py
index ab84a8875..07618a5fd 100644
--- a/pybamm/parameters/standard_parameters_lead_acid.py
+++ b/pybamm/parameters/standard_parameters_lead_acid.py
@@ -2,7 +2,7 @@
 # Standard parameters for lead-acid battery models
 #
 """
-Standard parameters for lead-acid battery models
+Standard Parameters for lead-acid battery models
 """
 import pybamm
 from scipy import constants
@@ -31,6 +31,9 @@ L_x = pybamm.geometric_parameters.L_x
 L_y = pybamm.geometric_parameters.L_y
 L_z = pybamm.geometric_parameters.L_z
 A_cc = pybamm.geometric_parameters.A_cc
+W = L_y
+H = L_z
+A_cs = A_cc
 
 # Electrical
 I_typ = pybamm.electrical_parameters.I_typ
@@ -61,29 +64,49 @@ sigma_n_dimensional = pybamm.Parameter("Negative electrode conductivity [S.m-1]"
 sigma_p_dimensional = pybamm.Parameter("Positive electrode conductivity [S.m-1]")
 
 # Microstructure
-a_n_dim = pybamm.Parameter("Negative electrode surface area density [m-1]")
-a_p_dim = pybamm.Parameter("Positive electrode surface area density [m-1]")
+a_n_dimensional = pybamm.Parameter("Negative electrode surface area density [m-1]")
+a_p_dimensional = pybamm.Parameter("Positive electrode surface area density [m-1]")
 b = pybamm.Parameter("Bruggeman coefficient")
 
 # Electrochemical reactions
-m_n_dimensional = pybamm.Parameter(
+j0_n_S_ref_dimensional = pybamm.Parameter(
     "Negative electrode reference exchange-current density [A.m-2]"
 )
-m_p_dimensional = pybamm.Parameter(
+j0_p_S_ref_dimensional = pybamm.Parameter(
     "Positive electrode reference exchange-current density [A.m-2]"
 )
-s_plus_n = pybamm.Parameter("Negative electrode cation signed stoichiometry")
-s_plus_p = pybamm.Parameter("Positive electrode cation signed stoichiometry")
-ne_n = pybamm.Parameter("Negative electrode electrons in reaction")
-ne_p = pybamm.Parameter("Positive electrode electrons in reaction")
+j0_n_Ox_ref_dimensional = pybamm.Parameter(
+    "Negative electrode reference exchange-current density (oxygen) [A.m-2]"
+)
+j0_p_Ox_ref_dimensional = pybamm.Parameter(
+    "Positive electrode reference exchange-current density (oxygen) [A.m-2]"
+)
+j0_n_Hy_ref_dimensional = pybamm.Parameter(
+    "Negative electrode reference exchange-current density (hydrogen) [A.m-2]"
+)
+j0_p_Hy_ref_dimensional = pybamm.Parameter(
+    "Positive electrode reference exchange-current density (hydrogen) [A.m-2]"
+)
+s_plus_n_S_dim = pybamm.Parameter("Negative electrode cation signed stoichiometry")
+s_plus_p_S_dim = pybamm.Parameter("Positive electrode cation signed stoichiometry")
+ne_n_S = pybamm.Parameter("Negative electrode electrons in reaction")
+ne_p_S = pybamm.Parameter("Positive electrode electrons in reaction")
+s_plus_Ox_dim = pybamm.Parameter("Signed stoichiometry of cations (oxygen reaction)")
+s_w_Ox_dim = pybamm.Parameter("Signed stoichiometry of water (oxygen reaction)")
+s_ox_Ox_dim = pybamm.Parameter("Signed stoichiometry of oxygen (oxygen reaction)")
+ne_Ox = pybamm.Parameter("Electrons in oxygen reaction")
+c_ox_ref = pybamm.Parameter("Reference oxygen molecule concentration [mol.m-3]")
+s_plus_Hy_dim = pybamm.Parameter("Signed stoichiometry of cations (hydrogen reaction)")
+s_hy_Hy_dim = pybamm.Parameter("Signed stoichiometry of hydrogen (hydrogen reaction)")
+ne_Hy = pybamm.Parameter("Electrons in hydrogen reaction")
 C_dl_dimensional = pybamm.Parameter("Double-layer capacity [F.m-2]")
 
 
 # Electrolyte properties
 M_w = pybamm.Parameter("Molar mass of water [kg.mol-1]")
-M_p = pybamm.Parameter("Molar mass of cations [kg.mol-1]")
-M_n = pybamm.Parameter("Molar mass of anions [kg.mol-1]")
-M_e = M_n + M_p  # Molar mass of electrolyte [kg.mol-1]
+M_plus = pybamm.Parameter("Molar mass of cations [kg.mol-1]")
+M_minus = pybamm.Parameter("Molar mass of anions [kg.mol-1]")
+M_e = M_minus + M_plus  # Molar mass of electrolyte [kg.mol-1]
 
 DeltaVliq_n = (
     V_minus - V_plus
@@ -92,6 +115,14 @@ DeltaVliq_p = (
     2 * V_w - V_minus - 3 * V_plus
 )  # Net Molar Volume consumed in electrolyte (neg) [m3.mol-1]
 
+# Other species properties
+D_ox_dimensional = pybamm.Parameter("Oxygen diffusivity [m2.s-1]")
+D_hy_dimensional = pybamm.Parameter("Hydrogen diffusivity [m2.s-1]")
+V_ox = pybamm.Parameter("Partial molar volume of oxygen molecules [m3.mol-1]")
+V_hy = pybamm.Parameter("Partial molar volume of hydrogen molecules [m3.mol-1]")
+M_ox = pybamm.Parameter("Molar mass of oxygen molecules [kg.mol-1]")
+M_hy = pybamm.Parameter("Molar mass of hydrogen molecules [kg.mol-1]")
+
 # Electrode properties
 V_Pb = pybamm.Parameter("Molar volume of lead [m3.mol-1]")
 V_PbO2 = pybamm.Parameter("Molar volume of lead-dioxide [m3.mol-1]")
@@ -102,7 +133,8 @@ d = pybamm.Parameter("Pore size [m]")
 eps_n_max = pybamm.Parameter("Maximum porosity of negative electrode")
 eps_s_max = pybamm.Parameter("Maximum porosity of separator")
 eps_p_max = pybamm.Parameter("Maximum porosity of positive electrode")
-
+Q_n_max_dimensional = pybamm.Parameter("Negative electrode volumetric capacity [C.m-3]")
+Q_p_max_dimensional = pybamm.Parameter("Positive electrode volumetric capacity [C.m-3]")
 
 # --------------------------------------------------------------------------------------
 "2. Dimensional Functions"
@@ -113,48 +145,45 @@ def D_e_dimensional(c_e):
     return pybamm.FunctionParameter("Electrolyte diffusivity", c_e)
 
 
-def D_e(c_e):
-    "Dimensionless electrolyte diffusivity"
-    c_e_dimensional = c_e * c_e_typ
-    return D_e_dimensional(c_e_dimensional) / D_e_dimensional(c_e_typ)
-
-
 def kappa_e_dimensional(c_e):
     "Dimensional electrolyte conductivity"
     return pybamm.FunctionParameter("Electrolyte conductivity", c_e)
 
 
-def kappa_e(c_e):
-    "Dimensionless electrolyte conductivity"
-    c_e_dimensional = c_e * c_e_typ
-    kappa_scale = F ** 2 * D_e_dimensional(c_e_typ) * c_e_typ / (R * T_ref)
-    return kappa_e_dimensional(c_e_dimensional) / kappa_scale
-
-
 def chi_dimensional(c_e):
     return pybamm.FunctionParameter("Darken thermodynamic factor", c_e)
 
 
-def c_w_dimensional(c_e):
+def c_w_dimensional(c_e, c_ox=0, c_hy=0):
     """
     Water concentration [mol.m-3], from thermodynamics. c_k in [mol.m-3].
     """
-    return (1 - c_e * V_e) / V_w
+    return (1 - c_e * V_e - c_ox * V_ox - c_hy * V_hy) / V_w
 
 
-def rho_dimensional(c_e):
+def c_T(c_e, c_ox=0, c_hy=0):
     """
-    Dimensional density of electrolyte [kg.m-3], from thermodynamics. c_e in [mol.m-3].
+    Total liquid molarity [mol.m-3], from thermodynamics. c_k in [mol.m-3].
+    """
+    return (1 + (2 * V_w - V_e) * c_e + (V_w - V_ox) * c_ox + (V_w - V_hy) * c_hy) / V_w
+
 
+def rho_dimensional(c_e, c_ox=0, c_hy=0):
     """
-    return M_w / V_w * (1 + (M_e * V_w / M_w - V_e) * c_e)
+    Dimensional density of electrolyte [kg.m-3], from thermodynamics. c_k in [mol.m-3].
+    """
+    return (
+        M_w / V_w
+        + (M_e - V_e * M_w / V_w) * c_e
+        + (M_ox - V_ox * M_w / V_w) * c_ox
+        + (M_hy - V_hy * M_w / V_w) * c_hy
+    )
 
 
 def m_dimensional(c_e):
     """
     Dimensional electrolyte molar mass [mol.kg-1], from thermodynamics.
     c_e in [mol.m-3].
-
     """
     return c_e * V_w / ((1 - c_e * V_e) * M_w)
 
@@ -162,7 +191,6 @@ def m_dimensional(c_e):
 def mu_dimensional(c_e):
     """
     Dimensional viscosity of electrolyte [kg.m-1.s-1].
-
     """
     return pybamm.FunctionParameter("Electrolyte viscosity", c_e)
 
@@ -177,6 +205,9 @@ def U_p_dimensional(c_e):
     return pybamm.FunctionParameter("Positive electrode OCV", m_dimensional(c_e))
 
 
+D_e_typ = D_e_dimensional(c_e_typ)
+rho_typ = rho_dimensional(c_e_typ)
+mu_typ = mu_dimensional(c_e_typ)
 U_n_ref = pybamm.FunctionParameter("Negative electrode OCV", pybamm.Scalar(1))
 U_p_ref = pybamm.FunctionParameter("Positive electrode OCV", pybamm.Scalar(1))
 
@@ -190,8 +221,8 @@ electrolyte_concentration_scale = c_e_typ
 # electrical
 potential_scale = R * T_ref / F
 current_scale = i_typ
-interfacial_current_scale_n = i_typ / (a_n_dim * L_x)
-interfacial_current_scale_p = i_typ / (a_p_dim * L_x)
+interfacial_current_scale_n = i_typ / (a_n_dimensional * L_x)
+interfacial_current_scale_p = i_typ / (a_p_dimensional * L_x)
 
 velocity_scale = i_typ / (c_e_typ * F)  # Reaction velocity scale
 
@@ -200,11 +231,11 @@ tau_discharge = F * c_e_typ * L_x / i_typ
 
 # Reaction timescales
 # should this be * F?
-tau_r_n = 1 / (m_n_dimensional * a_n_dim * c_e_typ ** 0.5)
-tau_r_p = 1 / (m_p_dimensional * a_p_dim * c_e_typ ** 0.5)
+tau_r_n = 1 / (j0_n_S_ref_dimensional * a_n_dimensional * c_e_typ ** 0.5)
+tau_r_p = 1 / (j0_p_S_ref_dimensional * a_p_dimensional * c_e_typ ** 0.5)
 
 # Electrolyte diffusion timescale
-tau_diffusion_e = L_x ** 2 / D_e_dimensional(c_e_typ)
+tau_diffusion_e = L_x ** 2 / D_e_typ
 
 
 # --------------------------------------------------------------------------------------
@@ -217,52 +248,78 @@ l_p = pybamm.geometric_parameters.l_p
 l_y = pybamm.geometric_parameters.l_y
 l_z = pybamm.geometric_parameters.l_z
 
+# Diffusive kinematic relationship coefficient
+omega_i = c_e_typ * M_e / rho_typ * (t_plus + M_minus / M_e)
+# Migrative kinematic relationship coefficient (electrolyte)
+omega_c_e = c_e_typ * M_e / rho_typ * (1 - M_w * V_e / V_w * M_e)
+C_e = tau_diffusion_e / tau_discharge
+# Ratio of viscous pressure scale to osmotic pressure scale (electrolyte)
+pi_os_e = mu_typ * velocity_scale * L_x / (d ** 2 * R * T_ref * c_e_typ)
+# ratio of electrolyte concentration to electrode concentration, undefined
+gamma_e = pybamm.Scalar(1)
+# Reynolds number
+Re = rho_typ * velocity_scale * L_x / mu_typ
+
+# Other species properties
+curlyD_ox = D_ox_dimensional / D_e_typ
+curlyD_hy = D_hy_dimensional / D_e_typ
+omega_c_ox = c_e_typ * M_ox / rho_typ * (1 - M_w * V_ox / V_w * M_ox)
+omega_c_hy = c_e_typ * M_hy / rho_typ * (1 - M_w * V_hy / V_w * M_hy)
+
 # Electrode Properties
 sigma_n = sigma_n_dimensional * potential_scale / current_scale / L_x
 sigma_p = sigma_p_dimensional * potential_scale / current_scale / L_x
+delta_pore_n = 1 / (a_n_dimensional * L_x)
+delta_pore_p = 1 / (a_p_dimensional * L_x)
+Q_n_max = Q_n_max_dimensional / (c_e_typ * F)
+Q_p_max = Q_p_max_dimensional / (c_e_typ * F)
 
-# Electrolyte properties
-beta_surf_n = -c_e_typ * DeltaVsurf_n / ne_n  # Molar volume change (lead)
-beta_surf_p = -c_e_typ * DeltaVsurf_p / ne_p  # Molar volume change (lead dioxide)
-beta_surf = pybamm.Concatenation(
-    pybamm.Broadcast(beta_surf_n, ["negative electrode"]),
+# Electrochemical reactions
+s_plus_n_S = s_plus_n_S_dim / ne_n_S
+s_plus_p_S = s_plus_p_S_dim / ne_p_S
+s_plus_Ox = s_plus_Ox_dim / ne_Ox
+s_w_Ox = s_w_Ox_dim / ne_Ox
+s_ox_Ox = s_ox_Ox_dim / ne_Ox
+s_plus_Hy = s_plus_Hy_dim / ne_Hy
+s_hy_Hy = s_hy_Hy_dim / ne_Hy
+s_n = -(s_plus_n_S + t_plus)  # Dimensionless rection rate (neg)
+s_p = -(s_plus_p_S + t_plus)  # Dimensionless rection rate (pos)
+s = pybamm.Concatenation(
+    pybamm.Broadcast(s_n, ["negative electrode"]),
     pybamm.Broadcast(0, ["separator"]),
-    pybamm.Broadcast(beta_surf_p, ["positive electrode"]),
+    pybamm.Broadcast(s_p, ["positive electrode"]),
 )
-beta_liq_n = -c_e_typ * DeltaVliq_n / ne_n  # Molar volume change (electrolyte, neg)
-beta_liq_p = -c_e_typ * DeltaVliq_p / ne_p  # Molar volume change (electrolyte, pos)
-beta_n = beta_surf_n + beta_liq_n  # Total molar volume change (neg)
-beta_p = beta_surf_p + beta_liq_p  # Total molar volume change (pos)
-omega_i = (
-    c_e_typ * M_e / rho_dimensional(c_e_typ) * (1 - M_w * V_e / V_w * M_e)
-)  # Diffusive kinematic relationship coefficient
-omega_c = (
-    c_e_typ * M_e / rho_dimensional(c_e_typ) * (t_plus + M_n / M_e)
-)  # Migrative kinematic relationship coefficient
-C_e = tau_diffusion_e / tau_discharge
-pi_os = (
-    mu_dimensional(c_e_typ) * velocity_scale * L_x / (d ** 2 * R * T_ref * c_e_typ)
-)  # Ratio of viscous pressure scale to osmotic pressure scale
-gamma_e = 1  # ratio of electrolyte concentration to electrode concentration, undef.
-
-# Electrochemical reactions
+j0_n_S_ref = j0_n_S_ref_dimensional / interfacial_current_scale_n
+j0_p_S_ref = j0_p_S_ref_dimensional / interfacial_current_scale_p
+m_n = j0_n_S_ref
+m_p = j0_p_S_ref
+j0_n_Ox_ref = j0_n_Ox_ref_dimensional / interfacial_current_scale_n
+j0_p_Ox_ref = j0_p_Ox_ref_dimensional / interfacial_current_scale_p
+j0_n_Hy_ref = j0_n_Hy_ref_dimensional / interfacial_current_scale_n
+j0_p_Hy_ref = j0_p_Hy_ref_dimensional / interfacial_current_scale_p
 C_dl_n = (
     C_dl_dimensional * potential_scale / interfacial_current_scale_n / tau_discharge
 )
 C_dl_p = (
     C_dl_dimensional * potential_scale / interfacial_current_scale_p / tau_discharge
 )
+ne_n = ne_n_S
+ne_p = ne_p_S
 
-# Electrochemical Reactions
-s_n = -(s_plus_n + ne_n * t_plus) / ne_n  # Dimensionless rection rate (neg)
-s_p = -(s_plus_p + ne_p * t_plus) / ne_p  # Dimensionless rection rate (pos)
-s = pybamm.Concatenation(
-    pybamm.Broadcast(s_n, ["negative electrode"]),
+# Electrolyte properties
+beta_surf_n = -c_e_typ * DeltaVsurf_n / ne_n_S  # Molar volume change (lead)
+beta_surf_p = -c_e_typ * DeltaVsurf_p / ne_p_S  # Molar volume change (lead dioxide)
+beta_surf = pybamm.Concatenation(
+    pybamm.Broadcast(beta_surf_n, ["negative electrode"]),
     pybamm.Broadcast(0, ["separator"]),
-    pybamm.Broadcast(s_p, ["positive electrode"]),
+    pybamm.Broadcast(beta_surf_p, ["positive electrode"]),
 )
-m_n = m_n_dimensional / interfacial_current_scale_n
-m_p = m_p_dimensional / interfacial_current_scale_p
+beta_liq_n = -c_e_typ * DeltaVliq_n / ne_n_S  # Molar volume change (electrolyte, neg)
+beta_liq_p = -c_e_typ * DeltaVliq_p / ne_p_S  # Molar volume change (electrolyte, pos)
+beta_n = beta_surf_n + beta_liq_n  # Total molar volume change (neg)
+beta_p = beta_surf_p + beta_liq_p  # Total molar volume change (pos)
+beta_Ox = -c_e_typ * (s_plus_Ox * V_plus + s_w_Ox * V_w + s_ox_Ox * V_ox)
+beta_Hy = -c_e_typ * (s_plus_Hy * V_plus + s_hy_Hy * V_hy)
 
 # Electrical
 voltage_low_cut = (voltage_low_cut_dimensional - (U_p_ref - U_n_ref)) / potential_scale
@@ -270,22 +327,24 @@ voltage_high_cut = (
     voltage_high_cut_dimensional - (U_p_ref - U_n_ref)
 ) / potential_scale
 
+# Electrolyte volumetric capacity
+Q_e_max = (l_n * eps_n_max + l_s * eps_s_max + l_p * eps_p_max) / (s_p - s_n)
+Q_e_max_dimensional = Q_e_max * c_e_typ * F
+capacity = Q_e_max_dimensional * 8 * A_cs * L_x
+
 # Initial conditions
 q_init = pybamm.Parameter("Initial State of Charge")
-q_max = (
-    (L_n * eps_n_max + L_s * eps_s_max + L_p * eps_p_max) / L_x / (s_p - s_n)
-)  # Dimensionless max capacity
-epsDelta_n = beta_surf_n / L_n * q_max
-epsDelta_p = beta_surf_p / L_p * q_max
 c_e_init = q_init
-eps_n_init = eps_n_max - epsDelta_n * (1 - q_init)  # Initial pororsity (neg)
-eps_s_init = eps_s_max  # Initial pororsity (sep)
-eps_p_init = eps_p_max - epsDelta_p * (1 - q_init)  # Initial pororsity (pos)
+eps_n_init = eps_n_max - beta_surf_n * Q_e_max / l_n * (1 - q_init)
+eps_s_init = eps_s_max
+eps_p_init = eps_p_max + beta_surf_p * Q_e_max / l_p * (1 - q_init)
 eps_init = pybamm.Concatenation(
     pybamm.Broadcast(eps_n_init, ["negative electrode"]),
     pybamm.Broadcast(eps_s_init, ["separator"]),
     pybamm.Broadcast(eps_p_init, ["positive electrode"]),
 )
+curlyU_n_init = Q_e_max * (1 - q_init) / (Q_n_max * l_n)
+curlyU_p_init = Q_e_max * (1 - q_init) / (Q_p_max * l_p)
 
 
 # hack to make consistent ic with lithium-ion
@@ -298,12 +357,28 @@ c_p_init = c_e_init
 "5. Dimensionless Functions"
 
 
+def D_e(c_e):
+    "Dimensionless electrolyte diffusivity"
+    c_e_dimensional = c_e * c_e_typ
+    return D_e_dimensional(c_e_dimensional) / D_e_typ
+
+
+def kappa_e(c_e):
+    "Dimensionless electrolyte conductivity"
+    c_e_dimensional = c_e * c_e_typ
+    kappa_scale = F ** 2 * D_e_typ * c_e_typ / (R * T_ref)
+    return kappa_e_dimensional(c_e_dimensional) / kappa_scale
+
+
 # (1-2*t_plus) is for Nernst-Planck
 # 2*(1-t_plus) for Stefan-Maxwell
-def chi(c_e):
-    c_e_dimensional = c_e * c_e_typ
-    alpha = (nu * V_w - V_e) * c_e_typ
-    return chi_dimensional(c_e_dimensional) * 2 * (1 - t_plus) / (1 - alpha * c_e)
+def chi(c_e, c_ox=0, c_hy=0):
+    return (
+        chi_dimensional(c_e_typ * c_e)
+        * 2
+        * (1 - t_plus)
+        / (V_w * c_T(c_e_typ * c_e, c_e_typ * c_ox, c_e_typ * c_hy))
+    )
 
 
 def c_w(c_e):
diff --git a/pybamm/solvers/dae_solver.py b/pybamm/solvers/dae_solver.py
index 2af497ad6..c3d10938a 100644
--- a/pybamm/solvers/dae_solver.py
+++ b/pybamm/solvers/dae_solver.py
@@ -142,6 +142,8 @@ class DaeSolver(pybamm.BaseSolver):
             Initial conditions that are consistent with the algebraic equations (roots
             of the algebraic equations)
         """
+        pybamm.logger.info("Start calculating consistent initial conditions")
+
         # Split y0_guess into differential and algebraic
         len_rhs = rhs(0, y0_guess).shape[0]
         y0_diff, y0_alg_guess = np.split(y0_guess, [len_rhs])
@@ -149,7 +151,13 @@ class DaeSolver(pybamm.BaseSolver):
         def root_fun(y0_alg):
             "Evaluates algebraic using y0_diff (fixed) and y0_alg (changed by algo)"
             y0 = np.concatenate([y0_diff, y0_alg])
-            return algebraic(0, y0)
+            out = algebraic(0, y0)
+            pybamm.logger.debug(
+                "Evaluating algebraic equations at t=0, L2-norm is {}".format(
+                    np.linalg.norm(out)
+                )
+            )
+            return out
 
         # Find the values of y0_alg that are roots of the algebraic equations
         sol = optimize.root(
@@ -159,11 +167,17 @@ class DaeSolver(pybamm.BaseSolver):
         y0_consistent = np.concatenate([y0_diff, sol.x])
 
         if sol.success and np.all(sol.fun < self.root_tol):
+            pybamm.logger.info("Finish calculating consistent initial conditions")
             return y0_consistent
-        else:
+        elif not sol.success:
             raise pybamm.SolverError(
                 "Could not find consistent initial conditions: {}".format(sol.message)
             )
+        else:
+            raise pybamm.SolverError(
+                "Could not find consistent initial conditions: "
+                + "solver terminated successfully, but solution above tolerance"
+            )
 
     def integrate(
         self, residuals, y0, t_eval, events=None, mass_matrix=None, jacobian=None
diff --git a/results/sulzer_thesis/print_lead_acid_parameters.py b/results/sulzer_thesis/print_lead_acid_parameters.py
new file mode 100644
index 000000000..8b43b5a02
--- /dev/null
+++ b/results/sulzer_thesis/print_lead_acid_parameters.py
@@ -0,0 +1,10 @@
+#
+# Print parameters for lead-acid models
+#
+import pybamm
+
+parameters = pybamm.standard_parameters_lead_acid
+parameter_values = pybamm.LeadAcidBaseModel().default_parameter_values
+output_file = "results/sulzer_thesis/parameters.txt"
+
+pybamm.print_parameters(parameters, parameter_values, output_file)


Test patch:
diff --git a/tests/integration/test_models/standard_output_tests.py b/tests/integration/test_models/standard_output_tests.py
index 09ab11bc1..000830612 100644
--- a/tests/integration/test_models/standard_output_tests.py
+++ b/tests/integration/test_models/standard_output_tests.py
@@ -459,7 +459,7 @@ class CurrentTests(BaseOutputTest):
         """Test that average of the interfacial current density is equal to the true
         value."""
         np.testing.assert_array_almost_equal(
-            self.j_n_av(self.t), self.i_cell / self.l_n
+            self.j_n_av(self.t), self.i_cell / self.l_n, decimal=5
         )
         np.testing.assert_array_almost_equal(
             self.j_p_av(self.t), -self.i_cell / self.l_p, decimal=5
diff --git a/tests/integration/test_models/test_lead_acid/test_asymptotics_convergence.py b/tests/integration/test_models/test_lead_acid/test_asymptotics_convergence.py
index 72ec74557..fefd79374 100644
--- a/tests/integration/test_models/test_lead_acid/test_asymptotics_convergence.py
+++ b/tests/integration/test_models/test_lead_acid/test_asymptotics_convergence.py
@@ -37,6 +37,7 @@ class TestAsymptoticConvergence(unittest.TestCase):
         full_disc.process_model(full_model)
 
         def get_max_error(current):
+            pybamm.logger.info("current = {}".format(current))
             # Update current (and hence C_e) in the parameters
             param = pybamm.ParameterValues(
                 base_parameters=full_model.default_parameter_values,
diff --git a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
index a46a37900..168e4025f 100644
--- a/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
+++ b/tests/unit/test_parameters/test_standard_parameters_lead_acid.py
@@ -15,6 +15,19 @@ class TestStandardParametersLeadAcid(unittest.TestCase):
         F = pybamm.standard_parameters_lead_acid.F
         self.assertAlmostEqual(F.evaluate(), 96485, places=0)
 
+    def test_all_defined(self):
+        parameters = pybamm.standard_parameters_lead_acid
+        parameter_values = pybamm.LeadAcidBaseModel().default_parameter_values
+        output_file = "results/sulzer_thesis/parameters.txt"
+        pybamm.print_parameters(parameters, parameter_values, output_file)
+        # test print_parameters with dict and without C-rate
+        del parameter_values["Cell capacity [A.h]"]
+        parameters = {
+            "C_e": pybamm.standard_parameters_lead_acid.C_e,
+            "sigma_n": pybamm.standard_parameters_lead_acid.sigma_n,
+        }
+        pybamm.print_parameters(parameters, parameter_values)
+
     def test_parameters_defaults_lead_acid(self):
         # Load parameters to be tested
         parameters = {
diff --git a/tests/unit/test_solvers/test_dae_solver.py b/tests/unit/test_solvers/test_dae_solver.py
index 0614a3243..fc2e73315 100644
--- a/tests/unit/test_solvers/test_dae_solver.py
+++ b/tests/unit/test_solvers/test_dae_solver.py
@@ -41,10 +41,13 @@ class TestDaeSolver(unittest.TestCase):
             # algebraic equation has no root
             return y ** 2 + 1
 
-        solver = pybamm.DaeSolver()
+        solver = pybamm.DaeSolver(root_method="hybr")
         y0 = np.array([2])
         with self.assertRaises(pybamm.SolverError):
             solver.calculate_consistent_initial_conditions(rhs, algebraic, y0)
+        solver = pybamm.DaeSolver()
+        with self.assertRaises(pybamm.SolverError):
+            solver.calculate_consistent_initial_conditions(rhs, algebraic, y0)
 
 
 if __name__ == "__main__":