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)
info_blue("E_after = {}".format(E_after))
info_blue("dE = {}".format(dE))
info_blue("dt = {}".format(dt.get()))
dt.chop()
converged = False
initial_step = False
# Update time with final dt value
t += dt.get()
# Set dt:
grad_mu_ufl = df.sqrt(geo_map.gab[i, j] * mu_.dx(i) * mu_.dx(j))
grad_mu = df.project(grad_mu_ufl, geo_map.S_ref)
grad_mu_max = mpi_max(grad_mu.vector().get_local())
dt_prev = dt.get()
dt.set(min(min(0.25 / grad_mu_max, T - t), parameters["t_ramp"] / 100))
info_blue("dt = {}".format(dt.get()))
if tstep % 10 == 0 or np.floor(t / 1000) - np.floor(t_prev / 1000) > 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(min(0.25/grad_mu_max, T-t),parameters["t_ramp"]/100) )
#info_blue("dt = {}".format(dt.get()))
ts.dump_stats(t, [
grad_mu_max, dt_prev,
dt.get(),