def _bc_default(self):
return [
BCDof(var='u',
dof=0,
value=-0.001,
time_function=LoadingScenario())
]
def _loading_scenario_default(self):
return LoadingScenario()
while (k < self.k_max) and not self.user_wants_abort:
if self.tstep.R_norm < self.acc:
print('\titer: %g' % k)
break
self.tstep.make_iter()
k += 1
else: # handle unfinished iteration loop
if k >= self.k_max: # add step size reduction
# no success abort the simulation
self.restart = True
print('Warning: '
'convergence not reached in %g iterations' % k)
return
# accept the time step and record the state in history
self.tstep.make_incr()
# update the line - launches notifiers to subscribers
self.tline.val = t_n1
# set a new target time
t_n1 += dt
return
class MATS3DDesmoratGrad(MATS3DDesmorat):
tloop_type = BrentqTLoop
s = Simulator(model=MATS3DDesmorat())
bc = BCDof(var='u', dof=0, value=-0.001, time_function=LoadingScenario())
s.tstep.bcond_mngr.bcond_list = [bc]
run_rerun_test(s)