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surfacetension.py
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surfacetension.py
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import random
from dolfin import *
import csv
import os
import time
import sys
from FHTaylor import taylorapprox_fullFH, taylorapprox_logonlyFH, heatofmixing, symquarticdoublewell, polynomialfit_fullFH
from Thermo.Thermo import RK, ThermoMix
from parameters.params import (
A_RAW,
NOISE_MAGNITUDE,
TIME_MAX,
DT,
N_CELLS,
DOMAIN_LENGTH,
theta_ch,
MESH_TYPE,
TIME_STRIDE,
SPECIES,
chi_AB,
N_A,
N_B,
GIBBS,
TEMPERATURE,
FINITE_ELEMENT,
FINITE_ELEMENT_ORDER,
SOLVER_CONFIG,
MOBILITY_MODEL,
SIZE_DISPARITY,
A_SYM,
MOBILITY_MODEL
)
class CahnHilliardEquation(NonlinearProblem):
def __init__(self, a, L):
NonlinearProblem.__init__(self)
self.L = L
self.a = a
# self.reset_sparsity = True
def F(self, b, x):
assemble(self.L, tensor=b)
def J(self, A, x):
assemble(self.a, tensor=A)#, reset_sparsity=self.reset_sparsity)
# self.reset_sparsity = False
parameters["form_compiler"]["optimize"] = True
parameters["form_compiler"]["cpp_optimize"] = True
parameters["form_compiler"]["quadrature_degree"] = 2
class InitialConditions(UserExpression):
def __init__(self, **kwargs):
random.seed(2 + MPI.rank(MPI.comm_world))
super().__init__(**kwargs)
def eval(self, values, x):
# [0] corresponds to the concentration field for species A
# [1] coresponds to the \mu_{AB} field
values[0] = A_RAW + 2.0 * NOISE_MAGNITUDE * (0.5 - random.random())
values[1] = 0.0
def value_shape(self):
return (2,)
class LeftBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and near(x[0], 0.0)
class RightBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and near(x[0], DOMAIN_LENGTH)
class BottomBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and near(x[1], 0.0)
class TopBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and near(x[1], DOMAIN_LENGTH)
class PeriodicBoundary(SubDomain):
def inside(self, x, on_boundary):
return on_boundary and (near(x[0], 0.0))
# Map RightBoundary to LeftBoundary
def map(self, x, y):
y[0] = x[0] - DOMAIN_LENGTH
N = int(N_CELLS)
mesh = IntervalMesh(N, 0.0,DOMAIN_LENGTH)
P1 = FiniteElement(FINITE_ELEMENT, mesh.ufl_cell(), FINITE_ELEMENT_ORDER)
CH = FunctionSpace(mesh, P1*P1)
dch = TrialFunction(CH)
h_1, j_1 = TestFunctions(CH)
ch = Function(CH)
ch0 = Function(CH)
# Split mixed functions
da, dmu_AB = split(dch)
x_a, mu_AB = split (ch)
x_a0, mu0_AB = split(ch0)
x_a = variable(x_a)
ch_init = InitialConditions(degree=1)
ch.interpolate(ch_init)
ch0.interpolate(ch_init)
if GIBBS == "FH":
# r = RK("FH",["PB","PS"],[N_A,N_B])
g = ( x_a * ln(x_a) )/N_A + ((1.0-x_a)*ln(1-x_a)/ N_B) + x_a*(1.0-x_a)*chi_AB
# g = r.G_RK(x_a)
print("Vanilla FH")
if GIBBS == "UNIFAC":
r = RK("UNIFAC",SPECIES,[N_A,N_B],[TEMPERATURE])
g = r.G_RK(x_a)
print("Redlich-Kister UNIFAC")
elif GIBBS == "PCSAFT_CR":
r = RK("PCSAFT",SPECIES,[N_A,N_B],[TEMPERATURE],CR="On")
g = r.G_RK(x_a)
print("Redlich-Kister PCSAFT")
elif GIBBS == "PCSAFT_Fit":
r = RK("PCSAFT",SPECIES,[N_A,N_B],[TEMPERATURE],CR="Off")
g = r.G_RK(x_a)
print("Redlich-Kister PCSAFT")
elif GIBBS == "TaylorApproxFullFH":
g = taylorapprox_fullFH(N_A, N_B, chi_AB, x_a)
print("full taylor approx of FH")
elif GIBBS == "TaylorApproxLogOnlyFH":
g = taylorapprox_logonlyFH(N_A, N_B, chi_AB, x_a)
print ("Taylor approx of log term in FH only")
elif GIBBS == "polynomialfit_fullFH":
g = polynomialfit_fullFH(N_A, N_B, chi_AB, x_a, 4)
print("Full curve fit ahaha")
elif GIBBS == "symquarticdoublewell_fullFH":
g = symquarticdoublewell(x_a, A_SYM)
print ("BS quartic polynomial")
elif GIBBS == "Heatofmixing":
g = heatofmixing(chi_AB, x_a)
else:
print ("work harder")
if SIZE_DISPARITY == "SMALL":
if GIBBS=="FH":
kappa = (2.0/3.0)*chi_AB
else:
chi_AB = r.RK(0)/(N_A*N_B)**0.5
kappa = (2.0/3.0)*chi_AB[0]
print ("about the same size")
elif SIZE_DISPARITY == "LARGE":
if GIBBS=="FH":
kappa = (1.0/3.0)*chi_AB
else:
chi_AB = r.RK(0)/(N_A*N_B)**0.5
kappa = (1.0/3.0)*chi_AB[0]
print ("big size difference")
print(chi_AB)
# Using the fenics autodifferentiation toolkit
dgdx_a = diff(g,x_a)
mu_AB_mid = (1.0 - theta_ch) * mu0_AB + theta_ch * mu_AB
dt = DT
F_a = (
x_a * h_1 * dx
- x_a0 * h_1 * dx
+ dt * x_a * (1.0 - x_a) * dot(grad(mu_AB_mid), grad(h_1)) * dx
)
F_a_constant = (
x_a * h_1 * dx
- x_a0 * h_1 * dx
+ dt * dot(grad(mu_AB_mid), grad(h_1)) * dx
)
F_mu_AB = (
mu_AB * j_1 * dx
- dgdx_a * j_1 * dx
- kappa * dot(grad(x_a), grad(j_1)) * dx
)
if MOBILITY_MODEL == "Variable":
F = F_a + F_mu_AB
print ("work hard model")
elif MOBILITY_MODEL == "Constant":
F = F_a_constant + F_mu_AB
print("less work hehe")
else:
print("wrong model implemented")
sys.exit()
a = derivative(F, ch, dch)
if SOLVER_CONFIG == "LU":
problem = CahnHilliardEquation(a, F)
# problem.set_bounds(x_a_min, x_a_max)
solver = NewtonSolver()
solver.parameters["linear_solver"] = "lu"
# solver.parameters["linear_solver"] = "gmres"
# solver.parameters["preconditioner"] = "ilu"
solver.parameters["convergence_criterion"] = "residual"
solver.parameters["relative_tolerance"] = 1e-10
solver.parameters["absolute_tolerance"] = 1e-16
solver.parameters["relaxation_parameter"] = 0.5
elif SOLVER_CONFIG == "KRYLOV":
class CustomSolver(NewtonSolver):
def __init__(self):
NewtonSolver.__init__(self, mesh.mpi_comm(),
PETScKrylovSolver(), PETScFactory.instance())
def solver_setup(self, A, P, problem, iteration):
self.linear_solver().set_operator(A)
PETScOptions.set("ksp_type", "gmres")
PETScOptions.set("ksp_monitor")
PETScOptions.set("pc_type", "hypre")
PETScOptions.set("pc_hypre_type", "euclid")
PETScOptions.set("ksp_rtol", "1.0e-8")
PETScOptions.set("ksp_atol", "1.0e-16")
PETScOptions.set('ksp_max_it', '1000')
self.linear_solver().set_from_options()
problem = CahnHilliardEquation(a, F)
solver = CustomSolver()
# Initialising the output files
gibbs_list = []
surface_tension = []
# file = XDMFFile("output.xdmf")
file = File("output.pvd", "compressed")
start = time.time()
t = 0.0
time_stride = TIME_STRIDE
timestep = 0
while (t < TIME_MAX):
t += dt
print (f"Time = {t}")
ch0.vector()[:] = ch.vector()
solver.solve(problem, ch.vector())
timestep += 1
for i in ch.vector()[::2]:
if i < 0.0:
i == 0.0 + 1e-16
elif i > 1.0:
i == 1.0 - 1e-16
# Assembling the various terms of the Landau-Ginzburg free energy functional
homogenous_energy = assemble(g * dx())
gradient_energy = assemble(Constant(0.5)*kappa * dot(grad(x_a), grad(x_a)) * dx())
gibbs= homogenous_energy + gradient_energy
gibbs_list.append(gibbs)
# Assembling the suface tension contribution
Gamma = assemble(kappa*dot(grad(x_a), grad(x_a))*dx())
surface_tension.append(Gamma)
# fpath1 = "./output_gibbs.csv"
fpath2 = "./output_surfacetension.csv"
# headers1 = ["time", "gibbs"]
headers2 = ["time", "Gamma"]
end = time.time()
print(end-start)
# Write header row (for first timestep)
if not os.path.exists(fpath2):
with open(fpath2,"w") as f:
w = csv.DictWriter(f,headers2)
w.writeheader()
if (timestep % time_stride ==0):
# file.write (ch.split()[0], t)
file << (ch.split()[0], t)
# Appending case data
with open(fpath2, "a") as f:
w = csv.DictWriter(f, headers2)
w.writerow({"time": float(t), "Gamma": float(Gamma)})