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Tompa.py
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import numpy as np
from PolyLLE import PolyLLE
class Tompa (PolyLLE):
def __init__ (self, label = '', z_sol=0.98,
shulz_kind = '1P', r_pol = np.arange(10,10**4),
p = [0.999,0.995],
teta1 = None, teta2 = None,
coarsen = False,
alpha = None, xs_fraction = 0.05,
equilibrium_method = 'equations'):
super().__init__ (label, z_sol,
shulz_kind, r_pol, p, teta1, teta2,
coarsen, alpha, xs_fraction, equilibrium_method)
# quotients used in equilibrium equations
self.quoc1 = np.zeros(self.n_pol)
self.quoc2 = np.zeros(self.n_pol)
for i in range(self.n_pol):
self.quoc1[i] = ((self.r_pol[i] - self.r_pol[0])/
(self.r_pol[self.n_pol-1]-self.r_pol[0]))
self.quoc2[i] = ((self.r_pol[self.n_pol-1] - self.r_pol[i])/
(self.r_pol[self.n_pol-1]-self.r_pol[0]))
# equilibrium factors
self.K = np.zeros(self.n_comp)
self.lnK = np.zeros(self.n_comp)
# compositions of phases I and II
self.phiI = np.zeros(self.n_comp)
self.phiII = np.zeros(self.n_comp)
# mole numbers of phases I and II
self.nI = np.zeros(self.n_comp)
self.nII = np.zeros(self.n_comp)
#########################
def generate_pseudo_comp (self):
pass
#########################
def equilibrium_equations (self,x):
self.lnK[0] = x[0]
for i in range(1,self.n_comp):
self.lnK[i] = self.quoc1[i-1] * x[2] + self.quoc2[i-1] * x[1]
self.K = np.exp(self.lnK)
for i in range(self.n_comp):
self.phiI[i] = self.K[i]*self.z[i]/(self.K[i]*x[3]+(1-x[3]))
self.phiII[i] = self.z[i]/(self.K[i]*x[3]+1-x[3])
soma1 = 0
soma2 = 0
for i in range(self.n_pol):
soma1 += (((self.r_pol[i]-self.r[0])/
self.r_pol[i])*self.phiI[i+1])
soma2 += (((self.r_pol[i]-self.r[0])/
self.r_pol[i])*self.phiII[i+1])
y1 = (x[0] +
soma1 -
soma2 +
self.A*((self.r[0]-self.phiI[0])**2-
(self.r[0]-self.phiII[0])**2))
y2 = ((self.r[0]/self.r[1])*x[1] -
x[0] +
2*self.A*(self.phiI[0]-self.phiII[0]))
y3 = ((self.r[0]/self.r[self.n_comp-1])*x[2] -
x[0] +
2*self.A*(self.phiI[0]-self.phiII[0]))
y4 = sum(self.phiI) - sum(self.phiII)
return [y1,y2,y3,y4]
#########################
def gibbs_energy (self, x):
for i in range(self.n_comp):
self.phiI[i] = x[i]/sum(x)
self.phiII[i] = (self.z[i]-x[i])/sum(self.z-x)
self.nI[i] = x[i]/self.r[i]
self.nII[i] = self.z[i]/self.r[i] - self.nI[i]
soma1 = 0
soma2 = 0
soma3 = 0
soma4 = 0
for i in range(self.n_pol):
soma1 +=((self.r_pol[i]-self.r[0])/self.r_pol[i])*self.phiI[i+1]
soma2+=((self.r_pol[i]-self.r[0])/self.r_pol[i])*self.phiII[i+1]
soma3 += (self.r_pol[i]-self.r[0])*self.phiI[i+1]
soma4 += (self.r_pol[i]-self.r[0])*self.phiII[i+1]
delta_muI_0 = (np.log(self.phiI[0]) +
soma1 +
self.A*(self.r[0]-self.phiI[0])**2)
delta_muII_0 = (np.log(self.phiII[0]) +
soma2 +
self.A*(self.r[0]-self.phiII[0])**2)
dG = self.nI[0]*delta_muI_0 + self.nII[0]*delta_muII_0
for i in range(1,self.n_comp):
delta_muI = (np.log(self.phiI[i]) +
soma3 +
self.A*self.r[i]*(self.phiI[0])**2)
delta_muII = (np.log(self.phiII[i]) +
soma4 +
self.A*self.r[i]*(self.phiII[0])**2)
dG += self.nI[i]*delta_muI + self.nII[i]*delta_muII
return dG