dE = E_after - E_before
if not initial_step and dE > 0.0:
dt.chop()
converged = False
initial_step = False
# Update time with final dt value
t += dt.get()
if tstep % 1 == 0:
ts.dump(t)
# Assigning timestep size according to grad_mu_max:
grad_mu = ts.get_function("abs_grad_mu")
grad_mu_max = mpi_max(grad_mu.vector().get_local())
dt_prev = dt.get()
dt.set(min(0.25/grad_mu_max, T-t))
info_blue("dt = {}".format(dt.get()))
ts.dump_stats(t,
[grad_mu_max, dt_prev, dt.get(),
float(h.values()),
Eout_0, Eout_2,
Eout_0 + Eout_2, float(tau.values()),
dE],
"data")
if tstep % parameters["checkpoint_intv"] == 0 or t >= T:
save_checkpoint(tstep, t, geo_map.ref_mesh,
u_, u_1, ts.folder, parameters)
(tau / 2) * K * psi_**2 + (1 / 4) * K * psi_**4)
ts.add_field(E_0, "E_0")
ts.add_field(E_2, "E_2")
ts.add_field(df.sqrt(geo_map.gab[i, j] * mu_.dx(i) * mu_.dx(j)), "abs_grad_mu")
# Step in time
ts.dump(tstep)
initial_step = bool(parameters["restart_folder"] is None)
t_prev = t
while t < T:
tstep += 1
info_cyan("tstep = {}, time = {}".format(tstep, t))
# Print max difference since last timestep:
dumax = mpi_max(abs(u_.vector().get_local() - u_1.vector().get_local()))
info_blue("max(u_ - u_1) = {}".format(dumax))
u_1.assign(u_)
converged = False
while not converged:
if parameters["anneal"]:
tau.assign(
anneal_func(t + dt.get(), parameters["tau"],
parameters["tau_ramp"], parameters["t_ramp"]))
u_.assign(u_1)
Eout_0 = df.assemble(geo_map.form(E_0))
Eout_2 = df.assemble(geo_map.form(E_2))
E_before = Eout_0 + Eout_2