def __init__(self, name, atoms, formula, sites, comp, H, S, V, Cp, a, k,
flag, od):
if flag != -1 and flag != -2 and k[0] > 0:
formula = dictionarize_formula(formula)
self.params = OrderedDict([('name', name), ('formula', formula),
('equation_of_state', 'hp_tmt'),
('H_0', round(H * 1e3, 10)),
('S_0', round(S * 1e3, 10)),
('V_0', round(V * 1e-5, 15)),
('Cp', [
round(Cp[0] * 1e3, 10),
round(Cp[1] * 1e3, 10),
round(Cp[2] * 1e3, 10),
round(Cp[3] * 1e3, 10)
]), ('a_0', a),
('K_0', round(k[0] * 1e8, 10)),
('Kprime_0', k[1]),
('Kdprime_0', round(k[2] * 1e-8, 15)),
('n', sum(formula.values())),
('molar_mass',
round(formula_mass(formula), 10))])
if flag == 1:
self.landau_hp = OrderedDict([('P_0', 1e5), ('T_0', 298.15),
('Tc_0', od[0]),
('S_D', round(od[1] * 1e3, 10)),
('V_D', round(od[2] * 1e-5, 10))])
if flag == 2:
self.bragg_williams = OrderedDict([
('deltaH', round(od[0] * 1e3, 10)),
('deltaV', round(od[1] * 1e-5, 15)),
('Wh', round(od[2] * 1e3,
10)), ('Wv', round(od[3] * 1e-5,
15)), ('n', od[4]),
('factor', od[5])
])
if flag == -2:
formula = dictionarize_formula(formula)
self.params = OrderedDict([('name', name), ('formula', formula),
('equation_of_state', 'hp_tmtL'),
('H_0', round(H * 1e3, 10)),
('S_0', round(S * 1e3, 10)),
('V_0', round(V * 1e-5, 15)),
('Cp', [
round(Cp[0] * 1e3, 10),
round(Cp[1] * 1e3, 10),
round(Cp[2] * 1e3, 10),
round(Cp[3] * 1e3, 10)
]), ('a_0', a),
('K_0', round(k[0] * 1e8, 10)),
('Kprime_0', k[1]),
('Kdprime_0', round(k[2] * 1e-8, 15)),
('dKdT_0', round(k[3] * 1e8, 15)),
('n', sum(formula.values())),
('molar_mass',
round(formula_mass(formula), 10))])
def __init__(self):
formula = 'Re1.0O2.0'
formula = dictionarize_formula(formula)
self.params = {
'name': 'ReO2',
'formula': formula,
'equation_of_state': 'hp_tmt',
'H_0': -445140.0,
'S_0': 47.82,
'V_0': 1.8779e-05,
'Cp': [76.89, 0.00993, -1207130.0, -208.0],
'a_0': 4.4e-05,
'K_0': 1.8e+11,
'Kprime_0': 4.05,
'Kdprime_0': -2.25e-11,
'n': sum(formula.values()),
'molar_mass': formula_mass(formula)
}
burnman.Mineral.__init__(self)
def __init__(self):
formula = 'Re1.0'
formula = dictionarize_formula(formula)
self.params = {
'name': 'Re',
'formula': formula,
'equation_of_state': 'hp_tmt',
'H_0': 0.0,
'S_0': 36.53,
'V_0': 8.862e-06,
'Cp': [23.7, 0.005448, 68.0, 0.0],
'a_0': 1.9e-05,
'K_0': 3.6e+11,
'Kprime_0': 4.05,
'Kdprime_0': -1.1e-11,
'n': sum(formula.values()),
'molar_mass': formula_mass(formula)
}
burnman.Mineral.__init__(self)
fa = minerals.HP_2011_ds62.fa()
mt = minerals.HP_2011_ds62.mt()
qtz = minerals.HP_2011_ds62.q()
FMQ = Composite([fa, mt, qtz])
FMQ.set_state(P, T)
'''
Here we find chemical potentials of FeO, SiO2 and O2 for
an assemblage containing fayalite, magnetite and quartz,
and a second assemblage of magnetite and wustite
at 1 GPa, 1000 K
'''
component_formulae = ['FeO', 'SiO2', 'O2']
component_formulae_dict = [
chemistry.dictionarize_formula(f) for f in component_formulae
]
chem_potentials = FMQ.chemical_potential(component_formulae_dict)
oxygen = minerals.HP_2011_fluids.O2()
oxygen.set_state(P, T)
hem = minerals.HP_2011_ds62.hem()
MH = Composite([mt, hem])
MH.set_state(P, T)
print('log10(fO2) at the FMQ buffer:',
np.log10(chemistry.fugacity(oxygen, FMQ)))
print('log10(fO2) at the mt-hem buffer:',
np.log10(chemistry.fugacity(oxygen, MH)))