from __future__ import absolute_import
# This file is part of BurnMan - a thermoelastic and thermodynamic toolkit for the Earth and Planetary Sciences
# Copyright (C) 2012 - 2017 by the BurnMan team, released under the GNU
# GPL v2 or later.
import scipy.optimize as opt
from scipy.special import gamma, gammainc
from . import equation_of_state as eos
from ..utils.math import bracket
import warnings
import numpy as np
# Try to import the jit from numba. If it is
# not available, just go with the standard
# python interpreter
try:
from numba import jit
except ImportError:
def jit(fn):
return fn
@jit(nopython=True)
def _delta_PoverK_from_P(PoverK, pressure, K_0, Kprime_0, Kprime_inf):
return PoverK - (pressure / K_0) * np.power(
(1.0 - Kprime_inf * PoverK), Kprime_0 / Kprime_inf
) # eq. 58
@jit(nopython=True)
def _delta_PoverK_from_V(PoverK, V, V_0, K_0, Kprime_0, Kprime_inf):
Kprime_ratio = Kprime_0 / Kprime_inf
return (
np.log(V_0 / V)
+ Kprime_ratio / Kprime_inf * np.log(1.0 - Kprime_inf * PoverK)
+ (Kprime_ratio - 1.0) * PoverK
) # eq. 61
def _upper_incomplete_gamma(z, a):
"""
An implementation of the non-regularised upper incomplete gamma
function. Computed using the relationship with the regularised
lower incomplete gamma function (scipy.special.gammainc).
Uses the recurrence relation wherever z<0.
"""
n = int(-np.floor(z))
if n > 0:
z = z + n
u_gamma = (1.0 - gammainc(z, a)) * gamma(z)
for i in range(n):
z = z - 1.0
u_gamma = (u_gamma - np.power(a, z) * np.exp(-a)) / z
return u_gamma
else:
return (1.0 - gammainc(z, a)) * gamma(z)
def _PoverK_from_P(pressure, params):
"""
Calculates the pressure:bulk modulus ratio
from a given pressure using brentq optimization
"""
args = (
(pressure - params["P_0"]),
params["K_0"],
params["Kprime_0"],
params["Kprime_inf"],
)
return opt.brentq(
_delta_PoverK_from_P,
1.0 / (params["Kprime_inf"] - params["Kprime_0"]) + np.finfo(float).eps,
1.0 / params["Kprime_inf"] - np.finfo(float).eps,
args=args,
)
def _PoverK_from_V(volume, params):
"""
Calculates the pressure:bulk modulus ratio
from a given volume using brentq optimization
"""
args = (
volume,
params["V_0"],
params["K_0"],
params["Kprime_0"],
params["Kprime_inf"],
)
return opt.brentq(
_delta_PoverK_from_V,
1.0 / (params["Kprime_inf"] - params["Kprime_0"]) + np.finfo(float).eps,
1.0 / params["Kprime_inf"] - np.finfo(float).eps,
args=args,
)
def bulk_modulus(pressure, params):
"""
Returns the bulk modulus at a given pressure
"""
PoverK = _PoverK_from_P(pressure, params)
K = params["K_0"] * np.power(
(1.0 - params["Kprime_inf"] * PoverK),
-params["Kprime_0"] / params["Kprime_inf"],
)
return K
def shear_modulus(pressure, params):
"""
Returns the shear modulus at a given pressure
"""
G = (
params["G_0"] / params["K_0"] * bulk_modulus(pressure, params)
- (params["G_0"] / params["K_0"] * params["Kprime_inf"] - params["Gprime_inf"])
* pressure
)
return G # eq. 78
[docs]class RKprime(eos.EquationOfState):
"""
Class for the isothermal reciprocal K-prime equation of state
detailed in :cite:`StaceyDavis2004`. This equation of state is
a development of work by :cite:`Keane1954` and :cite:`Stacey2000`,
making use of the fact that :math:`K'` typically varies smoothly
as a function of :math:`P/K`, and is thermodynamically required to
exceed 5/3 at infinite pressure.
It is worth noting that this equation of state rapidly becomes
unstable at negative pressures, so should not be trusted to provide
a good *HT-LP* equation of state using a thermal pressure
formulation. The negative root of :math:`dP/dK`
can be found at :math:`K/P = K'_{\infty} - K'_0`,
which corresponds to a bulk modulus of
:math:`K = K_0 ( 1 - K'_{\infty}/K'_0 )^{K'_0/K'_{\infty}}`
and a volume of
:math:`V = V_0 ( K'_0 / (K'_0 - K'_{\infty}) )^{K'_0/{K'}^2_{\infty}} \exp{(-1/K'_{\infty})}`.
This equation of state has no temperature dependence.
"""
[docs] def volume(self, pressure, temperature, params):
"""
Returns volume :math:`[m^3]` as a function of pressure :math:`[Pa]`.
"""
Kprime_ratio = params["Kprime_0"] / params["Kprime_inf"]
PoverK = _PoverK_from_P(pressure, params)
V = params["V_0"] * np.exp(
Kprime_ratio
/ params["Kprime_inf"]
* np.log(1.0 - params["Kprime_inf"] * PoverK)
+ (Kprime_ratio - 1.0) * PoverK
) # Eq. 61
return V
[docs] def pressure(self, temperature, volume, params):
"""
Returns pressure :math:`[Pa]` as a function of volume :math:`[m^3]`.
"""
PoverK = _PoverK_from_V(volume, params)
return params["P_0"] + (
params["K_0"]
* PoverK
* np.power(
1.0 - params["Kprime_inf"] * PoverK,
-params["Kprime_0"] / params["Kprime_inf"],
)
)
[docs] def isothermal_bulk_modulus(self, pressure, temperature, volume, params):
"""
Returns isothermal bulk modulus :math:`K_T` :math:`[Pa]` as a function of pressure :math:`[Pa]`,
temperature :math:`[K]` and volume :math:`[m^3]`.
"""
return bulk_modulus(pressure, params)
[docs] def adiabatic_bulk_modulus(self, pressure, temperature, volume, params):
"""
Returns adiabatic bulk modulus :math:`K_s` of the mineral. :math:`[Pa]`.
"""
return bulk_modulus(pressure, params)
[docs] def shear_modulus(self, pressure, temperature, volume, params):
"""
Returns shear modulus :math:`G` of the mineral. :math:`[Pa]`
"""
return shear_modulus(pressure, params)
[docs] def entropy(self, pressure, temperature, volume, params):
"""
Returns the molar entropy :math:`\mathcal{S}` of the mineral. :math:`[J/K/mol]`
"""
return 0.0
def _intVdP(self, xi, params):
a = params["Kprime_inf"]
b = (
params["Kprime_0"] / params["Kprime_inf"] / params["Kprime_inf"]
- params["Kprime_0"] / params["Kprime_inf"]
- 1.0
)
c = params["Kprime_0"] - params["Kprime_inf"]
f = params["Kprime_0"] / params["Kprime_inf"] - 1.0
i1 = float(
params["V_0"]
* params["K_0"]
* np.exp(f / a)
* np.power(a, b - 1.0)
/ np.power(f, b + 2.0)
* (
f
* params["Kprime_0"]
* _upper_incomplete_gamma(b + 1.0, f * (1.0 / a - xi))
- a * c * _upper_incomplete_gamma(b + 2.0, f * (1.0 / a - xi))
)
)
return i1
[docs] def gibbs_free_energy(self, pressure, temperature, volume, params):
"""
Returns the Gibbs free energy :math:`\mathcal{G}` of the mineral. :math:`[J/mol]`
"""
# G = E0 + int VdP (when S = 0)
K = self.isothermal_bulk_modulus(pressure, temperature, volume, params)
return (
params["E_0"]
+ params["P_0"] * params["V_0"]
+ self._intVdP((pressure - params["P_0"]) / K, params)
- self._intVdP(0.0, params)
)
[docs] def molar_internal_energy(self, pressure, temperature, volume, params):
"""
Returns the internal energy :math:`\mathcal{E}` of the mineral. :math:`[J/mol]`
"""
# E = G - PV (+ TS)
return (
self.gibbs_free_energy(pressure, temperature, volume, params)
- pressure * volume
)
[docs] def molar_heat_capacity_v(self, pressure, temperature, volume, params):
"""
Since this equation of state does not contain temperature effects, simply return a very large number. :math:`[J/K/mol]`
"""
return 1.0e99
[docs] def molar_heat_capacity_p(self, pressure, temperature, volume, params):
"""
Since this equation of state does not contain temperature effects, simply return a very large number. :math:`[J/K/mol]`
"""
return 1.0e99
[docs] def thermal_expansivity(self, pressure, temperature, volume, params):
"""
Since this equation of state does not contain temperature effects, simply return zero. :math:`[1/K]`
"""
return 0.0
[docs] def grueneisen_parameter(self, pressure, temperature, volume, params):
"""
Since this equation of state does not contain temperature effects, simply return zero. :math:`[unitless]`
"""
return 0.0
[docs] def validate_parameters(self, params):
"""
Check for existence and validity of the parameters.
The value for :math:`K'_{\infty}` is thermodynamically bounded
between 5/3 and :math:`K'_0` :cite:`StaceyDavis2004`.
"""
if "E_0" not in params:
params["E_0"] = 0.0
if "P_0" not in params:
params["P_0"] = 0.0
# If G and Gprime_inf are not included this is presumably deliberate,
# as we can model density and bulk modulus just fine without them,
# so just add them to the dictionary as nans
if "G_0" not in params:
params["G_0"] = float("nan")
if "Gprime_inf" not in params:
params["Gprime_inf"] = float("nan")
# Check that all the required keys are in the dictionary
expected_keys = ["V_0", "K_0", "Kprime_0", "Kprime_inf", "G_0", "Gprime_inf"]
for k in expected_keys:
if k not in params:
raise KeyError("params object missing parameter : " + k)
# Finally, check that the values are reasonable.
if params["P_0"] < 0.0:
warnings.warn("Unusual value for P_0", stacklevel=2)
if params["V_0"] < 1.0e-7 or params["V_0"] > 1.0e-3:
warnings.warn("Unusual value for V_0", stacklevel=2)
if params["K_0"] < 1.0e9 or params["K_0"] > 1.0e13:
warnings.warn("Unusual value for K_0", stacklevel=2)
if params["Kprime_0"] < 0.0 or params["Kprime_0"] > 10.0:
warnings.warn("Unusual value for Kprime_0", stacklevel=2)
if (
params["Kprime_inf"] < 5.0 / 3.0
or params["Kprime_inf"] > params["Kprime_0"]
):
warnings.warn("Unusual value for Kprime_inf", stacklevel=2) # eq. 17
if params["G_0"] < 0.0 or params["G_0"] > 1.0e13:
warnings.warn("Unusual value for G_0", stacklevel=2)
if params["Gprime_inf"] < -5.0 or params["Gprime_inf"] > 10.0:
warnings.warn("Unusual value for Gprime_inf", stacklevel=2)