Line data Source code
1 : //* This file is part of the MOOSE framework
2 : //* https://mooseframework.inl.gov
3 : //*
4 : //* All rights reserved, see COPYRIGHT for full restrictions
5 : //* https://github.com/idaholab/moose/blob/master/COPYRIGHT
6 : //*
7 : //* Licensed under LGPL 2.1, please see LICENSE for details
8 : //* https://www.gnu.org/licenses/lgpl-2.1.html
9 :
10 : #include "FEProblemSolve.h"
11 :
12 : #include "FEProblem.h"
13 : #include "NonlinearSystemBase.h"
14 : #include "LinearSystem.h"
15 : #include "Convergence.h"
16 : #include "Executioner.h"
17 : #include "ConvergenceIterationTypes.h"
18 : #include "MooseUtils.h"
19 :
20 : std::set<std::string> const FEProblemSolve::_moose_line_searches = {"contact", "project"};
21 :
22 : const std::set<std::string> &
23 159132 : FEProblemSolve::mooseLineSearches()
24 : {
25 159132 : return _moose_line_searches;
26 : }
27 :
28 : InputParameters
29 292282 : FEProblemSolve::feProblemDefaultConvergenceParams()
30 : {
31 292282 : InputParameters params = emptyInputParameters();
32 :
33 1169128 : params.addParam<unsigned int>("nl_max_its", 50, "Max Nonlinear Iterations");
34 1169128 : params.addParam<unsigned int>("nl_forced_its", 0, "The Number of Forced Nonlinear Iterations");
35 1169128 : params.addParam<unsigned int>("nl_max_funcs", 10000, "Max Nonlinear solver function evaluations");
36 1169128 : params.addParam<Real>("nl_abs_tol", 1.0e-50, "Nonlinear Absolute Tolerance");
37 1169128 : params.addParam<Real>("nl_rel_tol", 1.0e-8, "Nonlinear Relative Tolerance");
38 876846 : params.addParam<Real>(
39 : "nl_div_tol",
40 584564 : 1.0e10,
41 : "Nonlinear Relative Divergence Tolerance. A negative value disables this check.");
42 876846 : params.addParam<Real>(
43 : "nl_abs_div_tol",
44 584564 : 1.0e50,
45 : "Nonlinear Absolute Divergence Tolerance. A negative value disables this check.");
46 1169128 : params.addParam<Real>("nl_abs_step_tol", 0., "Nonlinear Absolute step Tolerance");
47 1169128 : params.addParam<Real>("nl_rel_step_tol", 0., "Nonlinear Relative step Tolerance");
48 876846 : params.addParam<unsigned int>("n_max_nonlinear_pingpong",
49 584564 : 100,
50 : "The maximum number of times the nonlinear residual can ping pong "
51 : "before requesting halting the current evaluation and requesting "
52 : "timestep cut for transient simulations");
53 :
54 876846 : params.addParamNamesToGroup(
55 : "nl_max_its nl_forced_its nl_max_funcs nl_abs_tol nl_rel_tol "
56 : "nl_rel_step_tol nl_abs_step_tol nl_div_tol nl_abs_div_tol n_max_nonlinear_pingpong",
57 : "Nonlinear Solver");
58 :
59 292282 : return params;
60 0 : }
61 :
62 : InputParameters
63 159132 : FEProblemSolve::validParams()
64 : {
65 159132 : InputParameters params = MultiSystemSolveObject::validParams();
66 159132 : params += FEProblemSolve::feProblemDefaultConvergenceParams();
67 :
68 159132 : std::set<std::string> line_searches = mooseLineSearches();
69 :
70 159132 : std::set<std::string> alias_line_searches = {"default", "none", "basic"};
71 159132 : line_searches.insert(alias_line_searches.begin(), alias_line_searches.end());
72 159132 : std::set<std::string> petsc_line_searches = Moose::PetscSupport::getPetscValidLineSearches();
73 159132 : line_searches.insert(petsc_line_searches.begin(), petsc_line_searches.end());
74 636528 : std::string line_search_string = Moose::stringify(line_searches, " ");
75 318264 : MooseEnum line_search(line_search_string, "default");
76 159132 : std::string addtl_doc_str(" (Note: none = basic)");
77 159132 : params.addParam<MooseEnum>(
78 318264 : "line_search", line_search, "Specifies the line search type" + addtl_doc_str);
79 636528 : MooseEnum line_search_package("petsc moose", "petsc");
80 636528 : params.addParam<MooseEnum>("line_search_package",
81 : line_search_package,
82 : "The solver package to use to conduct the line-search");
83 :
84 477396 : params.addParam<unsigned>("contact_line_search_allowed_lambda_cuts",
85 318264 : 2,
86 : "The number of times lambda is allowed to be cut in half in the "
87 : "contact line search. We recommend this number be roughly bounded by 0 "
88 : "<= allowed_lambda_cuts <= 3");
89 477396 : params.addParam<Real>("contact_line_search_ltol",
90 : "The linear relative tolerance to be used while the contact state is "
91 : "changing between non-linear iterations. We recommend that this tolerance "
92 : "be looser than the standard linear tolerance");
93 :
94 159132 : params += Moose::PetscSupport::getPetscValidParams();
95 636528 : params.addParam<Real>("l_tol", 1.0e-5, "Linear Relative Tolerance");
96 636528 : params.addParam<Real>("l_abs_tol", 1.0e-50, "Linear Absolute Tolerance");
97 636528 : params.addParam<unsigned int>("l_max_its", 10000, "Max Linear Iterations");
98 636528 : params.addParam<std::vector<ConvergenceName>>(
99 : "nonlinear_convergence",
100 : "Name of the Convergence object(s) to use to assess convergence of the "
101 : "nonlinear system(s) solve. If not provided, the default Convergence "
102 : "associated with the Problem will be constructed internally.");
103 636528 : params.addParam<std::vector<ConvergenceName>>(
104 : "linear_convergence",
105 : "Name of the Convergence object(s) to use to assess convergence of the "
106 : "linear system(s) solve. If not provided, the linear solver tolerance parameters are used");
107 477396 : params.addParam<bool>(
108 : "snesmf_reuse_base",
109 318264 : true,
110 : "Specifies whether or not to reuse the base vector for matrix-free calculation");
111 477396 : params.addParam<bool>(
112 318264 : "skip_exception_check", false, "Specifies whether or not to skip exception check");
113 477396 : params.addParam<bool>(
114 : "use_pre_SMO_residual",
115 318264 : false,
116 : "Compute the pre-SMO residual norm and use it in the relative convergence check. The "
117 : "pre-SMO residual is computed at the begining of the time step before solution-modifying "
118 : "objects are executed. Solution-modifying objects include preset BCs, constraints, "
119 : "predictors, etc.");
120 636528 : params.addParam<bool>("automatic_scaling", "Whether to use automatic scaling for the variables.");
121 636528 : params.addParam<std::vector<bool>>(
122 : "compute_scaling_once",
123 : {true},
124 : "Whether the scaling factors should only be computed once at the beginning of the simulation "
125 : "through an extra Jacobian evaluation. If this is set to false, then the scaling factors "
126 : "will be computed during an extra Jacobian evaluation at the beginning of every time step. "
127 : "Vector entries correspond to each nonlinear system.");
128 636528 : params.addParam<std::vector<bool>>(
129 : "off_diagonals_in_auto_scaling",
130 : {false},
131 : "Whether to consider off-diagonals when determining automatic scaling factors. Vector "
132 : "entries correspond to each nonlinear system.");
133 1273056 : params.addRangeCheckedParam<std::vector<Real>>(
134 : "resid_vs_jac_scaling_param",
135 : {0},
136 : "0<=resid_vs_jac_scaling_param<=1",
137 : "A parameter that indicates the weighting of the residual vs the Jacobian in determining "
138 : "variable scaling parameters. A value of 1 indicates pure residual-based scaling. A value of "
139 : "0 indicates pure Jacobian-based scaling. Vector entries correspond to each nonlinear "
140 : "system.");
141 636528 : params.addParam<std::vector<std::vector<std::vector<std::string>>>>(
142 : "scaling_group_variables",
143 : "Name of variables that are grouped together for determining scale factors. (Multiple "
144 : "groups can be provided, separated by semicolon). Vector entries correspond to each "
145 : "nonlinear system.");
146 636528 : params.addParam<std::vector<std::vector<std::string>>>(
147 : "ignore_variables_for_autoscaling",
148 : "List of variables that do not participate in autoscaling. Vector entries correspond to each "
149 : "nonlinear system.");
150 795660 : params.addRangeCheckedParam<unsigned int>(
151 : "num_grids",
152 318264 : 1,
153 : "num_grids>0",
154 : "The number of grids to use for a grid sequencing algorithm. This includes the final grid, "
155 : "so num_grids = 1 indicates just one solve in a time-step");
156 636528 : params.addParam<std::vector<bool>>("residual_and_jacobian_together",
157 : {false},
158 : "Whether to compute the residual and Jacobian together. "
159 : "Vector entries correspond to each nonlinear system.");
160 :
161 477396 : params.addParam<bool>("reuse_preconditioner",
162 318264 : false,
163 : "If true reuse the previously calculated "
164 : "preconditioner for the linearized "
165 : "system across multiple solves "
166 : "spanning nonlinear iterations and time steps. "
167 : "The preconditioner resets as controlled by "
168 : "reuse_preconditioner_max_linear_its");
169 477396 : params.addParam<unsigned int>("reuse_preconditioner_max_linear_its",
170 318264 : 25,
171 : "Reuse the previously calculated "
172 : "preconditioner for the linear system "
173 : "until the number of linear iterations "
174 : "exceeds this number");
175 :
176 636528 : params.addParamNamesToGroup("l_tol l_abs_tol l_max_its reuse_preconditioner "
177 : "reuse_preconditioner_max_linear_its",
178 : "Linear Solver");
179 636528 : params.addParamNamesToGroup(
180 : "solve_type snesmf_reuse_base use_pre_SMO_residual "
181 : "num_grids residual_and_jacobian_together nonlinear_convergence linear_convergence",
182 : "Nonlinear Solver");
183 636528 : params.addParamNamesToGroup(
184 : "automatic_scaling compute_scaling_once off_diagonals_in_auto_scaling "
185 : "scaling_group_variables resid_vs_jac_scaling_param ignore_variables_for_autoscaling",
186 : "Solver variable scaling");
187 636528 : params.addParamNamesToGroup("line_search line_search_package contact_line_search_ltol "
188 : "contact_line_search_allowed_lambda_cuts",
189 : "Solver line search");
190 477396 : params.addParamNamesToGroup("skip_exception_check", "Advanced");
191 :
192 318264 : return params;
193 159132 : }
194 :
195 60680 : FEProblemSolve::FEProblemSolve(Executioner & ex)
196 : : MultiSystemSolveObject(ex),
197 121360 : _num_grid_steps(cast_int<unsigned int>(getParam<unsigned int>("num_grids") - 1))
198 : {
199 121360 : if (_moose_line_searches.find(getParam<MooseEnum>("line_search").operator std::string()) !=
200 121360 : _moose_line_searches.end())
201 0 : _problem.addLineSearch(_pars);
202 :
203 61023 : auto set_solver_params = [this, &ex](const SolverSystem & sys)
204 : {
205 61023 : const auto prefix = sys.prefix();
206 61023 : if (dynamic_cast<const LinearSystem *>(&sys))
207 1228 : Moose::PetscSupport::dontAddCommonSNESOptions(_problem, prefix);
208 61023 : Moose::PetscSupport::storePetscOptions(_problem, prefix, ex);
209 61023 : Moose::PetscSupport::setConvergedReasonFlags(_problem, prefix);
210 :
211 : // Set solver parameter prefix and system number
212 61023 : auto & solver_params = _problem.solverParams(sys.number());
213 61023 : solver_params._prefix = prefix;
214 61023 : solver_params._solver_sys_num = sys.number();
215 61023 : };
216 :
217 : // Extract and store PETSc related settings on FEProblemBase
218 121703 : for (const auto * const sys : _systems)
219 61023 : set_solver_params(*sys);
220 :
221 : // Set linear solve parameters in the equation system
222 : // Nonlinear solve parameters are added in the DefaultNonlinearConvergence
223 60680 : EquationSystems & es = _problem.es();
224 242720 : es.parameters.set<Real>("linear solver tolerance") = getParam<Real>("l_tol");
225 242720 : es.parameters.set<Real>("linear solver absolute tolerance") = getParam<Real>("l_abs_tol");
226 60680 : es.parameters.set<unsigned int>("linear solver maximum iterations") =
227 182040 : getParam<unsigned int>("l_max_its");
228 242720 : es.parameters.set<bool>("reuse preconditioner") = getParam<bool>("reuse_preconditioner");
229 60680 : es.parameters.set<unsigned int>("reuse preconditioner maximum linear iterations") =
230 182040 : getParam<unsigned int>("reuse_preconditioner_max_linear_its");
231 :
232 : // Transfer to the Problem misc nonlinear solve optimization parameters
233 60680 : _problem.setSNESMFReuseBase(getParam<bool>("snesmf_reuse_base"),
234 182040 : _pars.isParamSetByUser("snesmf_reuse_base"));
235 121360 : _problem.skipExceptionCheck(getParam<bool>("skip_exception_check"));
236 :
237 182040 : if (isParamValid("nonlinear_convergence"))
238 : {
239 396 : if (_problem.onlyAllowDefaultNonlinearConvergence())
240 0 : mooseError("The selected problem does not allow 'nonlinear_convergence' to be set.");
241 1188 : _problem.setNonlinearConvergenceNames(
242 : getParam<std::vector<ConvergenceName>>("nonlinear_convergence"));
243 : }
244 : else
245 60284 : _problem.setNeedToAddDefaultNonlinearConvergence();
246 182040 : if (isParamValid("linear_convergence"))
247 : {
248 134 : if (_problem.numLinearSystems() == 0)
249 0 : paramError(
250 : "linear_convergence",
251 : "Setting 'linear_convergence' is currently only possible for solving linear systems");
252 402 : _problem.setLinearConvergenceNames(
253 : getParam<std::vector<ConvergenceName>>("linear_convergence"));
254 : }
255 :
256 : // Check whether the user has explicitly requested automatic scaling and is using a solve type
257 : // without a matrix. If so, then we warn them
258 183663 : if ((_pars.isParamSetByUser("automatic_scaling") && getParam<bool>("automatic_scaling")) &&
259 485 : std::all_of(_systems.begin(),
260 : _systems.end(),
261 485 : [this](const auto & solver_sys)
262 485 : { return _problem.solverParams(solver_sys->number())._type == Moose::ST_JFNK; }))
263 : {
264 0 : paramWarning("automatic_scaling",
265 : "Automatic scaling isn't implemented for the case where you do not have a "
266 : "preconditioning matrix. No scaling will be applied");
267 0 : _problem.automaticScaling(false);
268 : }
269 : else
270 : // Check to see whether automatic_scaling has been specified anywhere, including at the
271 : // application level. No matter what: if we don't have a matrix, we don't do scaling
272 60680 : _problem.automaticScaling(
273 182040 : isParamValid("automatic_scaling")
274 62387 : ? getParam<bool>("automatic_scaling")
275 60111 : : (getMooseApp().defaultAutomaticScaling() &&
276 0 : std::any_of(_systems.begin(),
277 : _systems.end(),
278 0 : [this](const auto & solver_sys)
279 : {
280 0 : return _problem.solverParams(solver_sys->number())._type !=
281 0 : Moose::ST_JFNK;
282 : })));
283 :
284 180338 : if (!_using_multi_sys_fp_iterations && isParamValid("multi_system_fixed_point_convergence"))
285 6 : paramError("multi_system_fixed_point_convergence",
286 : "Cannot set a convergence object for multi-system fixed point iterations if "
287 : "'multi_system_fixed_point' is set to false");
288 62379 : if (_using_multi_sys_fp_iterations && !isParamValid("multi_system_fixed_point_convergence"))
289 6 : paramError("multi_system_fixed_point_convergence",
290 : "Must set a convergence object for multi-system fixed point iterations if using "
291 : "multi-system fixed point iterations");
292 :
293 : // Set the same parameters to every nonlinear system by default
294 60674 : int i_nl_sys = -1;
295 121670 : for (const auto i_sys : index_range(_systems))
296 : {
297 61005 : auto nl_ptr = dynamic_cast<NonlinearSystemBase *>(_systems[i_sys]);
298 : // Linear systems have very different parameters at the moment
299 61005 : if (!nl_ptr)
300 1228 : continue;
301 59777 : auto & nl = *nl_ptr;
302 59777 : i_nl_sys++;
303 :
304 119554 : nl.setPreSMOResidual(getParam<bool>("use_pre_SMO_residual"));
305 :
306 : const auto res_and_jac =
307 119554 : getParamFromNonlinearSystemVectorParam<bool>("residual_and_jacobian_together", i_nl_sys);
308 59771 : if (res_and_jac)
309 481 : nl.residualAndJacobianTogether();
310 :
311 : // Automatic scaling parameters
312 59771 : nl.computeScalingOnce(
313 119542 : getParamFromNonlinearSystemVectorParam<bool>("compute_scaling_once", i_nl_sys));
314 119542 : nl.autoScalingParam(
315 : getParamFromNonlinearSystemVectorParam<Real>("resid_vs_jac_scaling_param", i_nl_sys));
316 59771 : nl.offDiagonalsInAutoScaling(
317 119542 : getParamFromNonlinearSystemVectorParam<bool>("off_diagonals_in_auto_scaling", i_nl_sys));
318 179313 : if (isParamValid("scaling_group_variables"))
319 15 : nl.scalingGroupVariables(
320 60 : getParamFromNonlinearSystemVectorParam<std::vector<std::vector<std::string>>>(
321 : "scaling_group_variables", i_nl_sys));
322 179313 : if (isParamValid("ignore_variables_for_autoscaling"))
323 : {
324 : // Before setting ignore_variables_for_autoscaling, check that they are not present in
325 : // scaling_group_variables
326 36 : if (isParamValid("scaling_group_variables"))
327 : {
328 : const auto & ignore_variables_for_autoscaling =
329 : getParamFromNonlinearSystemVectorParam<std::vector<std::string>>(
330 6 : "ignore_variables_for_autoscaling", i_nl_sys);
331 : const auto & scaling_group_variables =
332 : getParamFromNonlinearSystemVectorParam<std::vector<std::vector<std::string>>>(
333 6 : "scaling_group_variables", i_nl_sys);
334 3 : for (const auto & group : scaling_group_variables)
335 6 : for (const auto & var_name : group)
336 6 : if (std::find(ignore_variables_for_autoscaling.begin(),
337 : ignore_variables_for_autoscaling.end(),
338 12 : var_name) != ignore_variables_for_autoscaling.end())
339 6 : paramError("ignore_variables_for_autoscaling",
340 : "Variables cannot be in a scaling grouping and also be ignored");
341 0 : }
342 9 : nl.ignoreVariablesForAutoscaling(
343 36 : getParamFromNonlinearSystemVectorParam<std::vector<std::string>>(
344 : "ignore_variables_for_autoscaling", i_nl_sys));
345 : }
346 : }
347 :
348 : // Multi-grid options
349 60665 : _problem.numGridSteps(_num_grid_steps);
350 60665 : }
351 :
352 : template <typename T>
353 : T
354 239120 : FEProblemSolve::getParamFromNonlinearSystemVectorParam(const std::string & param_name,
355 : unsigned int index) const
356 : {
357 239120 : const auto & param_vec = getParam<std::vector<T>>(param_name);
358 239120 : if (index > _num_nl_systems)
359 0 : paramError(param_name,
360 : "Vector parameter is requested at index (" + std::to_string(index) +
361 : ") which is larger than number of nonlinear systems (" +
362 0 : std::to_string(_num_nl_systems) + ").");
363 239120 : if (param_vec.size() == 0)
364 3 : paramError(
365 : param_name,
366 : "This parameter was passed to a routine which cannot handle empty vector parameters");
367 239117 : if (param_vec.size() != 1 && param_vec.size() != _num_nl_systems)
368 3 : paramError(param_name,
369 : "Vector parameter size (" + std::to_string(param_vec.size()) +
370 : ") is different than the number of nonlinear systems (" +
371 3 : std::to_string(_num_nl_systems) + ").");
372 :
373 : // User passed only one parameter, assume it applies to all nonlinear systems
374 239114 : if (param_vec.size() == 1)
375 239114 : return param_vec[0];
376 : else
377 0 : return param_vec[index];
378 : }
379 :
380 : void
381 57723 : FEProblemSolve::initialSetup()
382 : {
383 57723 : MultiSystemSolveObject::initialSetup();
384 57723 : convergenceSetup();
385 : // Keep track of the solution warnings from the setup
386 : // before a count reset at the beginning of the time step
387 57717 : if (!_app.isRecovering())
388 : {
389 53737 : _app.solutionInvalidity().syncIteration();
390 53737 : _app.solutionInvalidity().accumulateIterationIntoTimeStepOccurences();
391 53737 : _app.solutionInvalidity().accumulateTimeStepIntoTotalOccurences(0);
392 : }
393 57717 : }
394 :
395 : void
396 57723 : FEProblemSolve::convergenceSetup()
397 : {
398 : // nonlinear
399 57723 : const auto conv_names = _problem.getNonlinearConvergenceNames();
400 114413 : for (const auto & conv_name : conv_names)
401 : {
402 56696 : auto & conv = _problem.getConvergence(conv_name);
403 56696 : conv.checkIterationType(ConvergenceIterationTypes::NONLINEAR);
404 : }
405 :
406 : // linear
407 173151 : if (isParamValid("linear_convergence"))
408 : {
409 268 : const auto conv_names = getParam<std::vector<ConvergenceName>>("linear_convergence");
410 268 : for (const auto & conv_name : conv_names)
411 : {
412 134 : auto & conv = _problem.getConvergence(conv_name);
413 134 : conv.checkIterationType(ConvergenceIterationTypes::LINEAR);
414 : }
415 134 : }
416 :
417 : // multisystem fixed point
418 173151 : if (isParamValid("multi_system_fixed_point_convergence"))
419 : {
420 842 : _multi_sys_fp_convergence =
421 1684 : &_problem.getConvergence(getParam<ConvergenceName>("multi_system_fixed_point_convergence"));
422 842 : _multi_sys_fp_convergence->checkIterationType(
423 : ConvergenceIterationTypes::MULTISYSTEM_FIXED_POINT);
424 : }
425 57717 : }
426 :
427 : bool
428 313086 : FEProblemSolve::solve()
429 : {
430 : // Outer loop for multi-grid convergence
431 313086 : bool converged = false;
432 313086 : unsigned int fp_iter = 0;
433 :
434 624261 : for (MooseIndex(_num_grid_steps) grid_step = 0; grid_step <= _num_grid_steps; ++grid_step)
435 : {
436 : // Multi-system fixed point loop
437 313136 : fp_iter = 0;
438 313136 : converged = false;
439 648999 : while (!converged)
440 : {
441 337833 : if (_using_multi_sys_fp_iterations)
442 25535 : _console << COLOR_MAGENTA << "Multi-system fixed point iteration " << fp_iter << ":"
443 25535 : << COLOR_DEFAULT << "\n"
444 25535 : << std::endl;
445 :
446 : // Loop over each system
447 677946 : for (const auto sys_i : index_range(_systems))
448 : {
449 342074 : auto * const sys = _systems[sys_i];
450 342074 : const bool is_nonlinear = (dynamic_cast<NonlinearSystemBase *>(sys) != nullptr);
451 : const Real fp_relax =
452 342074 : _using_multi_sys_fp_iterations ? _multi_sys_fp_relax_factors[sys_i] : 1.0;
453 : const bool apply_fp_relax =
454 342074 : _using_multi_sys_fp_iterations && !MooseUtils::absoluteFuzzyEqual(fp_relax, 1.0);
455 342074 : if (apply_fp_relax)
456 : {
457 20875 : sys->setFixedPointRelaxationFactor(fp_relax);
458 20875 : sys->saveOldSolutionForFixedPointRelaxation();
459 : }
460 :
461 : // Call solve on the problem for that system
462 342074 : if (is_nonlinear)
463 316356 : _problem.solve(sys->number());
464 : else
465 : {
466 : const auto linear_sys_number =
467 25718 : cast_int<unsigned int>(sys->number() - _problem.numNonlinearSystems());
468 25718 : _problem.solveLinearSystem(linear_sys_number, &_problem.getPetscOptions());
469 : }
470 :
471 : // Check convergence
472 : const auto solve_name =
473 675550 : _systems.size() == 1 ? " Solve" : "System " + sys->name() + ": Solve";
474 342016 : if (_problem.shouldSolve())
475 : {
476 308288 : if (_problem.converged(sys->number()))
477 : {
478 306385 : if (apply_fp_relax)
479 20875 : sys->applyFixedPointRelaxation();
480 306385 : _console << COLOR_GREEN << solve_name << " Converged!" << COLOR_DEFAULT << "\n"
481 306385 : << std::endl;
482 : }
483 : else
484 : {
485 1900 : _console << COLOR_RED << solve_name << " Did NOT Converge!" << COLOR_DEFAULT << "\n"
486 1900 : << std::endl;
487 1900 : if (apply_fp_relax)
488 0 : sys->clearFixedPointRelaxation();
489 1900 : return false;
490 : }
491 : }
492 : else
493 33728 : _console << COLOR_GREEN << solve_name << " Skipped!" << COLOR_DEFAULT << "\n"
494 33728 : << std::endl;
495 :
496 340113 : if (!is_nonlinear)
497 : {
498 : const auto linear_sys_number =
499 25709 : cast_int<unsigned int>(sys->number() - _problem.numNonlinearSystems());
500 25709 : auto & linear_sys = _problem.getLinearSystem(linear_sys_number);
501 :
502 : // This is for postprocessing purposes in case none of the objects request the gradients.
503 : // TODO: Somehow collect information if the postprocessors need gradients and if nothing
504 : // needs this, just skip it
505 25709 : linear_sys.computeGradients();
506 : }
507 :
508 340113 : if (apply_fp_relax)
509 20875 : sys->clearFixedPointRelaxation();
510 342013 : }
511 :
512 : // Assess convergence of the multi-system fixed point iteration
513 335872 : if (!_using_multi_sys_fp_iterations)
514 310346 : converged = true;
515 : else
516 : {
517 25526 : _problem.execute(EXEC_MULTISYSTEM_FIXED_POINT_CONVERGENCE);
518 :
519 : // checkConvergence expects the number of iterations performed, not the iteration index:
520 25526 : const auto n_fp_iter = fp_iter + 1;
521 25526 : const auto convergence_status = _multi_sys_fp_convergence->checkConvergence(n_fp_iter);
522 25526 : converged = convergence_status == Convergence::MooseConvergenceStatus::CONVERGED;
523 25526 : if (convergence_status == Convergence::MooseConvergenceStatus::DIVERGED)
524 9 : break;
525 : }
526 335863 : fp_iter++;
527 : }
528 :
529 311175 : if (grid_step != _num_grid_steps)
530 50 : _problem.uniformRefine();
531 : }
532 :
533 311125 : return converged;
534 : }
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