def run(self, globdat):
# ADVANCE
logging.info("Advancing to the next load step")
globdat.i += 1
ndof = globdat.get("ndof")
globdat.set("fext", np.zeros(ndof))
globdat.set("loadScale", globdat.i)
globdat.model.takeAction(Action.ADVANCE, globdat)
globdat.model.takeAction(Action.GET_MATRIX_0, globdat)
globdat.model.takeAction(Action.GET_EXT_VECTOR, globdat)
globdat.model.takeAction(Action.GET_CONSTRAINTS, globdat)
# INITIAL RESIDUAL
mbuild = globdat.get("mbuild")
fext = globdat.get("fext")
cons = globdat.get("cons")
disp = globdat.get("solu")
ndof = globdat.get("ndof")
old_disp = deepcopy(disp)
cons.updateSolution(disp)
Du = globdat.set("Du", disp - old_disp)
K = mbuild.getDenseMatrix()
r = fext - np.array(K).dot(Du)
solver = Solver(self.type, cons)
solver.solve(K, disp, r, mbuild.hbw)
return Status.EXIT
def calculation(self):
while True:
try:
calculator = Solver(self.type_method, self.xy, self.x)
calculator.solve()
del calculator
break
except TypeError:
break
except ValueError:
break
def p_convergence(config: ConfigParser, solver: Solver, sol: GridFunction, var: str) -> None:
"""
Function to check p (interpolat polynomial order) convergence and print results.
Args:
config: Config file from which to grab
solver: The solver used
sol: Gridfunction that contains the current solution
var: The variable of interest.
"""
num_refinements = config.get_item(['ERROR ANALYSIS', 'num_refinements'], int)
average_lst = config.get_list(['ERROR ANALYSIS', 'error_average'], str, quiet=True)
component = solver.model.model_components[var]
average = component in average_lst
# Reload the model's mesh and finite element space so convergence tests can be chained and don't affect each other.
solver.model.load_mesh_fes(mesh=True, fes=True)
# First solve used the default settings.
if component is None:
err = norm('l2_norm', sol, solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes, average)
else:
err = norm('l2_norm', sol.components[component], solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes.components[component], average)
# Track the convergence information.
num_dofs_lst = [solver.model.fes.ndof]
interp_ord_lst = [solver.model.interp_ord[var]]
error_lst = [err]
# Then run through a series of interpolant refinements.
for n in range(num_refinements):
solver.model.interp_ord = {key: val + 1 for key, val in solver.model.interp_ord.items()}
solver.model.load_mesh_fes(mesh=False, fes=True)
solver.reset_model()
sol = solver.solve()
if component is None:
err = norm('l2_norm', sol, solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes, average)
else:
err = norm('l2_norm', sol.components[component], solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes.components[component], average)
num_dofs_lst.append(solver.model.fes.ndof)
interp_ord_lst.append(solver.model.interp_ord[var])
error_lst.append(err)
print('L2 norm at refinement {0}: {1}'.format(n, err))
# Display the results nicely.
convergence_table = [['Interpolant Order', 'DOFs', 'Error', 'Convergence Rate']]
convergence_table.append([interp_ord_lst[0], num_dofs_lst[0], error_lst[0], 0])
for n in range(num_refinements):
# TODO: Not sure if this is the correct equation for p-convergence.
convergence_rate = math.log(error_lst[n] / error_lst[n + 1]) / math.log(num_dofs_lst[n + 1] / num_dofs_lst[n])
convergence_table.append([interp_ord_lst[n + 1], num_dofs_lst[n + 1], error_lst[n + 1], convergence_rate])
print(tabulate.tabulate(convergence_table, headers='firstrow', floatfmt=['.1f', '.1f', '.3e', '.2f']))
def solve(solver_name, problem_name, options):
'Solve the problem by solver with options.'
# Set cpp_compiler options
parameters["form_compiler"]["cpp_optimize"] = True
# Set debug level
set_log_active(options['debug'])
# Set refinement level
options['N'] = mesh_sizes[options['refinement_level']]
# Create problem and solver
problem = Problem(problem_name, options)
solver = Solver(solver_name, options)
time_step = solver.get_timestep(problem)[0]
if MPI.process_number() == 0 and options['verbose']:
print 'Problem: ' + str(problem)
print 'Solver: ' + str(solver)
# Solve problem with solver
wct = time.time()
u, p = solver.solve(problem)
# Compute elapsed time
wct = time.time() - wct
# Compute number of degrees of freedom
num_dofs = u.vector().size() + p.vector().size()
# Get the mesh size
mesh_size = u.function_space().mesh().hmin()
# Get functional value and error
functional, error = solver.eval()
# Save results
cpu_time = solver.cputime()
save_results(problem, solver, num_dofs, mesh_size, time_step, functional,
error)
return 0
def solve(solver_name, problem_name, options):
'Solve the problem by solver with options.'
# Set cpp_compiler options
parameters["form_compiler"]["cpp_optimize"] = True
# Set debug level
set_log_active(options['debug'])
# Set refinement level
options['N'] = mesh_sizes[options['refinement_level']]
# Create problem and solver
problem = Problem(problem_name, options)
solver = Solver(solver_name, options)
time_step = solver.get_timestep(problem)[0]
if MPI.process_number() == 0 and options['verbose']:
print 'Problem: ' + str(problem)
print 'Solver: ' + str(solver)
# Solve problem with solver
wct = time.time()
u, p = solver.solve(problem)
# Compute elapsed time
wct = time.time() - wct
# Compute number of degrees of freedom
num_dofs = u.vector().size() + p.vector().size()
# Get the mesh size
mesh_size = u.function_space().mesh().hmin()
# Get functional value and error
functional, error = solver.eval()
# Save results
cpu_time = solver.cputime()
save_results(problem, solver, num_dofs, mesh_size, time_step, functional, error)
return 0
def h_convergence(config: ConfigParser, solver: Solver, sol: GridFunction, var: str) -> None:
"""
Function to check h (mesh element size) convergence and print results.
Args:
config: Config file from which to grab
solver: The solver used
sol: Gridfunction that contains the current solution
var: The variable of interest.
"""
num_refinements = config.get_item(['ERROR ANALYSIS', 'num_refinements'], int)
average_lst = config.get_list(['ERROR ANALYSIS', 'error_average'], str, quiet=True)
component = solver.model.model_components[var]
average = component in average_lst
# Reload the model's mesh and finite element space so convergence tests can be chained and don't affect each other.
solver.model.load_mesh_fes(mesh=True, fes=True)
# First solve used the default settings.
if component is None:
err = norm('l2_norm', sol, solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes, average)
else:
err = norm('l2_norm', sol.components[component], solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes.components[component], average)
# Track the convergence information.
num_dofs_lst = [solver.model.fes.ndof]
error_lst = [err]
# Then run through a series of mesh refinements and resolve on each
# refined mesh.
for n in range(num_refinements):
solver.model.mesh.Refine()
solver.model.fes.Update()
solver.reset_model()
sol = solver.solve()
if component is None:
err = norm('l2_norm', sol, solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes, average)
else:
err = norm('l2_norm', sol.components[component], solver.model.ref_sol['ref_sols'][var],
solver.model.mesh, solver.model.fes.components[component], average)
num_dofs_lst.append(solver.model.fes.ndof)
error_lst.append(err)
print('L2 norm at refinement {0}: {1}'.format(n, err))
# Display the results nicely.
convergence_table = [['Refinement Level', 'DOFs', 'Error', 'Convergence Rate']]
convergence_table.append([1, num_dofs_lst[0], error_lst[0], 0])
for n in range(num_refinements):
ref_level = '1/{}'.format(int(2 ** (n + 1)))
convergence_rate = math.log(error_lst[n] / error_lst[n + 1]) / \
(math.log(num_dofs_lst[n + 1] / (num_dofs_lst[n] * 2.0 ** (solver.model.mesh.dim - 1))))
convergence_table.append([ref_level, num_dofs_lst[n + 1], error_lst[n + 1], convergence_rate])
print(tabulate.tabulate(convergence_table, headers='firstrow', floatfmt=('.1f', '.1f', '.3e', '.2f')))
def run(self, globdat):
# ADVANCE
logging.info("Advancing to the next load step")
globdat.i += 1
ndof = globdat.get("ndof")
globdat.set("fext", np.zeros(ndof))
globdat.set("loadScale", globdat.i)
globdat.model.takeAction(Action.ADVANCE, globdat)
globdat.model.takeAction(Action.GET_MATRIX_0, globdat)
globdat.model.takeAction(Action.GET_EXT_VECTOR, globdat)
globdat.model.takeAction(Action.GET_CONSTRAINTS, globdat)
mbuild = globdat.get("mbuild")
fext = globdat.get("fext")
fint = globdat.get("fint")
cons = globdat.get("cons")
disp = globdat.get("solu")
old_disp = deepcopy(disp)
cons.updateSolution(disp)
du = disp - old_disp
K = mbuild.getDenseMatrix()
r = fext - fint - np.array(K).dot(du)
solver = Solver(self.type, cons)
fdof = cons.getFdof()
for iter in range(self.niter):
# Update displacement vector
solver.solve(K, du, r, mbuild.hbw)
disp[fdof] += du[fdof]
# Find interal force vector
globdat.set("fint", np.zeros(ndof))
if self.nrkey == "full":
globdat.model.takeAction(Action.GET_MATRIX_0, globdat)
elif self.nrkey == "mod" or self.nrkey == "LE":
globdat.model.takeAction(Action.GET_INT_VECTOR, globdat)
else:
raise ValueError("{} not implemented !".format(self.nrkey))
# Find out-of-balance force vector
r = fext - globdat.get("fint")
nrm = norm(r[fdof])
logging.info(" Iteration {}: norm = {:.10f} ".format(iter, nrm))
# Check convergence in first iteration
if iter == 0 and nrm <= self.tiny:
logging.info(" Converged in {} iterations".format(iter + 1))
return Status.OK
elif iter == 0 and nrm > self.tiny:
nrm1 = deepcopy(nrm)
# Check convergence in later iterations
if nrm < self.tol * nrm1:
logging.info(" Converged in {} iterations".format(iter + 1))
return Status.OK
def run(self, globdat):
# ADVANCE
logging.info("Advancing to the next load step")
globdat.i += 1
ndof = globdat.get("ndof")
globdat.set("du", np.zeros(ndof))
globdat.set("Du", np.zeros(ndof))
globdat.set("fext", np.zeros(ndof))
globdat.set("fint", np.zeros(ndof))
globdat.set("loadScale", globdat.i)
globdat.model.takeAction(Action.ADVANCE, globdat)
globdat.model.takeAction(Action.GET_MATRIX_0, globdat)
globdat.model.takeAction(Action.GET_EXT_VECTOR, globdat)
globdat.model.takeAction(Action.GET_CONSTRAINTS, globdat)
mbuild = globdat.get("mbuild")
fext = globdat.get("fext")
fint = globdat.get("fint")
cons = globdat.get("cons")
disp = globdat.get("solu")
old_disp = deepcopy(disp)
cons.updateSolution(disp)
du = globdat.set("du", disp - old_disp)
Du = globdat.set("Du", deepcopy(du))
K = mbuild.getDenseMatrix()
r = fext - fint - np.array(K).dot(Du)
solver = Solver(self.type, cons)
fdof = cons.getFdof()
nrm1 = 0.0
for iter in range(self.niter):
# Update displacement vector
solver.solve(K, du, r, mbuild.hbw)
disp[fdof] += du[fdof]
Du[fdof] += du[fdof]
# Find interal force vector
globdat.set("fint", np.zeros(ndof))
globdat.model.takeAction(self.action, globdat)
# Find out-of-balance force vector
r = fext - globdat.get("fint")
nrm = norm(r[fdof])
logging.info(" Iteration {}: norm = {:.10f} ".format(iter, nrm))
# Check convergence
if (iter == 0 and nrm <= self.tiny) or (iter > 0
and nrm < self.tol * nrm1):
logging.info(" Converged in {} iterations".format(iter + 1))
globdat.model.takeAction(Action.COMMIT, globdat)
return Status.OK
elif iter == 0 and nrm > self.tiny:
nrm1 = deepcopy(nrm)
return Status.EXIT
