def regulate(ci, ci_1, EC_scheme, c_cutoff):
if EC_scheme in ["NL1", "NL2"]:
return max_value(ci, 0.)
elif EC_scheme == ["L2"]:
return max_value(ci_1, c_cutoff)
else:
return max_value(ci_1, 0.)
def equilibrium_EC(w_, test_functions,
solutes,
permittivity,
dx, ds, normal,
dirichlet_bcs, neumann_bcs, boundary_to_mark,
use_iterative_solvers,
V_lagrange,
**namespace):
""" Electrochemistry equilibrium solver. Nonlinear! """
num_solutes = len(solutes)
cV = df.split(w_["EC"])
c, V = cV[:num_solutes], cV[num_solutes]
if V_lagrange:
V0 = cV[-1]
b = test_functions["EC"][:num_solutes]
U = test_functions["EC"][num_solutes]
if V_lagrange:
U0 = test_functions["EC"][-1]
z = [] # Charge z[species]
K = [] # Diffusivity K[species]
for solute in solutes:
z.append(solute[1])
K.append(solute[2])
rho_e = sum([c_e*z_e for c_e, z_e in zip(c, z)])
veps = permittivity[0]
F_c = []
for ci, bi, Ki, zi in zip(c, b, K, z):
grad_g_ci = df.grad(alpha_c(ci) + zi*V)
F_ci = Ki*max_value(ci, 0.)*df.dot(grad_g_ci, df.grad(bi))*dx
F_c.append(F_ci)
F_V = veps*df.dot(df.grad(V), df.grad(U))*dx
for boundary_name, sigma_e in neumann_bcs["V"].iteritems():
F_V += -sigma_e*U*ds(boundary_to_mark[boundary_name])
if rho_e != 0:
F_V += -rho_e*U*dx
if V_lagrange:
F_V += veps*V0*U*dx + veps*V*U0*dx
F = sum(F_c) + F_V
J = df.derivative(F, w_["EC"])
problem = df.NonlinearVariationalProblem(F, w_["EC"],
dirichlet_bcs["EC"], J)
solver = df.NonlinearVariationalSolver(problem)
solver.parameters["newton_solver"]["relative_tolerance"] = 1e-7
if use_iterative_solvers:
solver.parameters["newton_solver"]["linear_solver"] = "bicgstab"
if not V_lagrange:
solver.parameters["newton_solver"]["preconditioner"] = "hypre_amg"
solver.solve()
def setup_NSPFEC(w_NSPFEC, w_1NSPFEC,
dirichlet_bcs_NSPFEC,
neumann_bcs,
boundary_to_mark,
dx, ds, normal,
v, q, q0, psi, h, b, U,
u_, p_, p0_, phi_, g_, c_, V_,
u_1, p_1, p0_1, phi_1, g_1, c_1, V_1,
M_, nu_, veps_, rho_, K_, beta_, rho_e_,
dbeta, dveps, drho,
per_tau, sigma_bar, eps, grav, z,
solutes,
enable_NS, enable_PF, enable_EC,
use_iterative_solvers,
p_lagrange,
q_rhs):
""" The full problem of electrohydrodynamics in two phases.
Note that it is possible to turn off the different parts at will.
"""
# Setup of the Navier-Stokes part of F
mom_ = rho_*u_
if enable_PF:
mom_ += -M_*drho * df.nabla_grad(g_)
F = []
if enable_NS:
F_NS = (per_tau * rho_ * df.dot(u_ - u_1, v) * dx
+ df.inner(df.nabla_grad(u_), df.outer(mom_, v)) * dx
+ 2*nu_*df.inner(df.sym(df.nabla_grad(u_)),
df.sym(df.nabla_grad(v))) * dx
- p_ * df.div(v) * dx
- df.div(u_) * q * dx
- df.dot(rho_ * grav, v) * dx)
# if enable_PF:
# F_NS += - sigma_bar*eps*df.inner(
# df.outer(df.grad(phi_),
# df.grad(phi_)), df.grad(v)) * dx
# if enable_EC and rho_e_ != 0:
# F_NS += rho_e_*df.dot(df.grad(V_), v) * dx
# if enable_PF and enable_EC:
# F_NS += dveps*df.dot(
# df.grad(phi_), v)*df.dot(df.grad(V_),
# df.grad(V_)) * dx
if enable_PF:
F_NS += phi_*df.dot(df.grad(g_), v) * dx
if enable_EC:
for ci_, dbetai, solute in zip(c_, dbeta, solutes):
zi = solute[1]
F_NS += df.dot(df.grad(ci_), v) * dx \
+ ci_*dbetai*df.dot(df.grad(phi_), v) * dx \
+ zi*ci_*df.dot(df.grad(V_), v) * dx
# Slip boundary condition
for boundary_name, slip_length in neumann_bcs["u"].items():
F_NS += 1./slip_length * \
df.dot(u_, v) * ds(boundary_to_mark[boundary_name])
# Pressure boundary condition
for boundary_name, pressure in neumann_bcs["p"].items():
F_NS += pressure * df.inner(
normal, v) * ds(boundary_to_mark[boundary_name])
# Lagrange pressure
if p_lagrange:
F_NS += (p_*q0 + q*p0_)*dx
# RHS source terms
if "u" in q_rhs:
F_NS += -df.dot(q_rhs["u"], v)*dx
F.append(F_NS)
# Setup of the phase-field equations
if enable_PF:
phi_1_flt = unit_interval_filter(phi_1)
F_PF_phi = (per_tau*(phi_-phi_1_flt)*psi*df.dx +
M_*df.dot(df.grad(g_), df.grad(psi)) * dx)
if enable_NS:
F_PF_phi += df.dot(u_, df.grad(phi_)) * psi * dx
F_PF_g = (g_ * h * dx
- sigma_bar*eps*df.dot(df.grad(phi_), df.grad(h)) * dx
- sigma_bar/eps*diff_pf_potential(phi_) * h * dx)
if enable_EC:
F_PF_g += (-sum([dbeta_i * ci_ * h * dx
for dbeta_i, ci_ in zip(dbeta, c_)])
+ dveps * df.dot(df.grad(V_), df.grad(V_)) * h * dx)
# Contact angle boundary condtions
for boundary_name, costheta in neumann_bcs["phi"].items():
fw_prime = diff_pf_contact(phi_)
F_PF_g += sigma_bar * costheta * fw_prime * h * ds(
boundary_to_mark[boundary_name])
# RHS source terms
if "phi" in q_rhs:
F_PF_phi += -q_rhs["phi"]*psi*dx
F_PF = F_PF_phi + F_PF_g
F.append(F_PF)
# Setup of the electrochemistry
if enable_EC:
F_E_c = []
for ci_, ci_1, bi, Ki_, zi, dbetai, solute in zip(
c_, c_1, b, K_, z, dbeta, solutes):
ci_1_flt = max_value(ci_1, 0.)
F_E_ci = (per_tau*(ci_-ci_1_flt)*bi*df.dx
+ Ki_*df.dot(df.grad(ci_), df.grad(bi))*df.dx)
if zi != 0:
F_E_ci += Ki_*zi*ci_*df.dot(df.grad(V_),
df.grad(bi))*df.dx
if enable_NS:
F_E_ci += df.dot(u_, df.grad(ci_))*bi*df.dx
if enable_PF:
F_E_ci += Ki_*ci_*dbetai*df.dot(df.grad(phi_),
df.grad(bi)) * dx
if solute[0] in q_rhs:
F_E_ci += - q_rhs[solute[0]] * bi * dx
F_E_c.append(F_E_ci)
F_E_V = veps_*df.dot(df.grad(V_), df.grad(U))*df.dx
if rho_e_ != 0:
F_E_V += -rho_e_*U*df.dx
# Surface charge boundary condition
for boundary_name, sigma_e in neumann_bcs["V"].items():
F_E_V += -sigma_e*U*ds(boundary_to_mark[boundary_name])
# RHS source terms
if "V" in q_rhs:
F_E_V += q_rhs["V"]*U*dx
F_E = sum(F_E_c) + F_E_V
F.append(F_E)
F = sum(F)
J = df.derivative(F, w_NSPFEC)
problem_NSPFEC = df.NonlinearVariationalProblem(F, w_NSPFEC, dirichlet_bcs_NSPFEC, J)
solver_NSPFEC = df.NonlinearVariationalSolver(problem_NSPFEC)
if use_iterative_solvers:
solver_NSPFEC.parameters['newton_solver']['linear_solver'] = 'gmres'
solver_NSPFEC.parameters['newton_solver']['preconditioner'] = 'ilu'
return solver_NSPFEC
def pf_mobility(phi, gamma):
""" Phase field mobility function. """
# return gamma * (phi**2-1.)**2
func = 1.-phi**2
return 0.75 * gamma * max_value(0., func)
