Steady

Executioner for steady-state simulations.

Overview

Steady is a general solver for discrete steady-state nonlinear or linear problem:

(1)var element = document.getElementById("moose-equation-9b57957d-f60d-437a-9670-6dfa1a8fdb8f");katex.render("\\mathbf{R}(\\mathbf{u}) = \\mathbf{0}.", element, {displayMode:true,throwOnError:false});

By default the line-search Newton in PETSc is used with the PJFNK (preconditioned Jacobian-free Newton Krylov) method. At each Newton iteration the executioner solves

(2)var element = document.getElementById("moose-equation-e9d05bfa-5103-49a2-8818-0676e7905208");katex.render("\\mathbf{J}(\\mathbf{u}^{i-1}) \\delta \\mathbf{u}^{i} = \\mathbf{R}(\\mathbf{u}^{i-1}),", element, {displayMode:true,throwOnError:false});

where

var element = document.getElementById("moose-equation-bc519ffb-a37f-4176-be53-74b7bbe1c07b");katex.render("\\mathbf{J}(\\mathbf{u}^{i-1}) \\equiv \\mathbf{R}'(\\mathbf{u}=\\mathbf{u}^{i-1})", element, {displayMode:true,throwOnError:false});

is the Jacobian matrix evaluated at $var element = document.getElementById("moose-equation-571df640-0ab8-4c10-b31b-c6376623b190");katex.render("\\mathbf{u}^{i-1}", element, {displayMode:false,throwOnError:false});$ . Jacobian matrix depends on $var element = document.getElementById("moose-equation-b7885d1b-fe77-4ec9-b572-a08eca128f43");katex.render("\\mathbf{u}^{i-1}", element, {displayMode:false,throwOnError:false});$ for general nonlinear problems while it is constant for linear problems. The right hand side is also typically referred to as the residual at $var element = document.getElementById("moose-equation-db6f98d5-5638-4b8f-9df6-6aa67953f3e9");katex.render("\\mathbf{u}^{i-1}", element, {displayMode:false,throwOnError:false});$ of Eq. (1). The Krylov methods are employed for solving the above linear equation, which requires only the evaluation of the matrix-vector product $var element = document.getElementById("moose-equation-09f74412-872a-4e21-a57d-9983f8ff66ed");katex.render("\\mathbf{J}(\\mathbf{u}^{i-1}) \\mathbf{y}", element, {displayMode:false,throwOnError:false});$ . Within the MOOSE framework, the Jacobian-Free Newton Krylov method is used that approximates matrix vector products by the Finite-Difference like approximation:

(3)var element = document.getElementById("moose-equation-9811d3ba-1e07-492d-b70b-755ee414eb32");katex.render("\\mathbf{J}(\\mathbf{u}^{i-1}) \\mathbf{y} \\approx \\frac{\\mathbf{R}(\\mathbf{u}^{i-1} + \\epsilon \\mathbf{y}) - \\mathbf{R}(\\mathbf{u}^{i-1})}{\\epsilon},", element, {displayMode:true,throwOnError:false});

where the scalar value $var element = document.getElementById("moose-equation-c4521926-3c58-4314-bb0b-de5b5a4de04d");katex.render("\\epsilon", element, {displayMode:false,throwOnError:false});$ is chosen by PETSc automatically to approximate $var element = document.getElementById("moose-equation-e690df6d-ff67-4cea-8968-084091fd02ab");katex.render("\\mathbf{J}(\\mathbf{u}^{i-1}) \\mathbf{y}", element, {displayMode:false,throwOnError:false});$ accurately for the linear solve. It is noted that for a linear problem of which $var element = document.getElementById("moose-equation-5f6553f2-01d1-46f3-a4a5-3701b43b4803");katex.render("\\mathbf{R}(\\mathbf{u})", element, {displayMode:false,throwOnError:false});$ can be expressed as $var element = document.getElementById("moose-equation-874569d5-5b55-45be-9688-ee1ed8ea51ee");katex.render("\\mathbf{A} \\mathbf{u}-\\mathbf{b}", element, {displayMode:false,throwOnError:false});$ , where matrix $var element = document.getElementById("moose-equation-0e9fa4ef-ef24-4d37-a838-49944bfdb600");katex.render("\\mathbf{A}", element, {displayMode:false,throwOnError:false});$ is the Jacobian independent on $var element = document.getElementById("moose-equation-3526c2a9-087f-4352-a6fc-b35c908a764b");katex.render("\\mathbf{u}", element, {displayMode:false,throwOnError:false});$ and $var element = document.getElementById("moose-equation-e44cdb98-f475-40b7-85d4-77983152f69a");katex.render("\\mathbf{b}", element, {displayMode:false,throwOnError:false});$ is the right-hand-side vector, the right hand side of Eq. (3) is independent on $var element = document.getElementById("moose-equation-92efebbd-1156-4188-8b1d-7952ff97f972");katex.render("\\epsilon", element, {displayMode:false,throwOnError:false});$ . Section 5.5 of PETSc user's manual on matrix-free methods details the algorithm for choosing the value of $var element = document.getElementById("moose-equation-88bcf7b2-7177-4acb-b703-79a89ed5ddf8");katex.render("\\epsilon", element, {displayMode:false,throwOnError:false});$ . It is actually the PETSc option -mat_mffd_err controls the $var element = document.getElementById("moose-equation-236d1936-05bb-4d4b-bdcc-833107942cd8");katex.render("\\epsilon", element, {displayMode:false,throwOnError:false});$ but not -snes_mf_err unless we set -snes_mf_version to 2 other than the default 1. This could be changed in future PETSc updates.

The Krylov methods typically also require an approximation of the actual Jacobian $var element = document.getElementById("moose-equation-bd337684-7436-46b7-b4bb-fb75e1986161");katex.render("\\mathbf{M}(\\mathbf{u}^{i-1}) \\approx \\mathbf{J}(\\mathbf{u}^{i-1})", element, {displayMode:false,throwOnError:false});$ for pre-conditioning the Krylov solution at each linear iteration. Note, the preconditioning matrix is seldom the exact Jacobian $var element = document.getElementById("moose-equation-9b656ccc-2520-495e-b7a4-808834310509");katex.render("\\mathbf{J}", element, {displayMode:false,throwOnError:false});$ because it would require too much computational time and memory to compute, and in some cases is simply impossible to compute. By default the type of Krylov method in use is GMRES because it does not have assumptions on the underlying Jacobian. The initial guess for each linear solve is always set to zero, which implies that the initial linear residual is the same of the nonlinear residual. The residual norm at each linear iteration is evaluated by PETSc, for instance, during updating the Hessenberg matrix if GMRES method is used. At the conclusion of the nonlinear iteration, the solution is updated as follows

(4)var element = document.getElementById("moose-equation-a0ec7c7b-878c-41f6-a673-b569e7045ae0");katex.render("\\mathbf{u}^{i} = \\mathbf{u}^{i-1} + \\alpha \\delta \\mathbf{u}^{i}", element, {displayMode:true,throwOnError:false});

where $var element = document.getElementById("moose-equation-92b55370-adb8-478a-a1c4-fa725fff6fbb");katex.render("\\alpha", element, {displayMode:false,throwOnError:false});$ is determined by the line-search algorithm. We can see that at each nonlinear or Newton iteration, we will need to update the preconditioning matrix and evaluate the residual with the updated solution. At each linear iteration, we simply need a residual evaluation and the operation of the preconditioner built from the preconditioning matrix. In PETSc the preconditioner type refers to the method to obtain an approximation of the inverse of $var element = document.getElementById("moose-equation-249ef0f7-5aa6-467e-9eec-e0e5ad622305");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ and not a means to compute the elements of $var element = document.getElementById("moose-equation-6ba1b889-97b5-40f9-a919-37a1ab53975f");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ . It is noted that the default preconditioner type depends on the number of processors and also depends on the assembled preconditioning matrix $var element = document.getElementById("moose-equation-284eab1b-4a79-4d6c-bcda-b5f3fac8e910");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ . Typically incomplete LU (PCILU) is the default type with one processor and block Jacobi (PCBJACOBI) is the type with multiple processors. Consequently, you will not see the same convergence with the different number of processors. Note, there are two approximations in play here: (1) the Jacobian $var element = document.getElementById("moose-equation-344869e2-7064-442a-82a8-1c7aeeab339b");katex.render("\\mathbf{J}", element, {displayMode:false,throwOnError:false});$ is approximated by a matrix $var element = document.getElementById("moose-equation-7d82346f-ea29-40d8-aa04-2a2d52b75d82");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ that is easier to compute, and (2) the matrix $var element = document.getElementById("moose-equation-a72e4267-0c1e-4dd1-a0a2-7fd5ff2dd004");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ is inverted approximately. The preconditioning matrix $var element = document.getElementById("moose-equation-dbb3bf63-6148-41de-a404-30188f0b4f7c");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ can be viewed with the PETSc option -ksp_view_pmat.

Solve Type

The general method in which the nonlinear system is solved is controlled by the "solve_type" parameter. Below is a description of each of the options:

PJFNK is the default solve type. It makes the executioner perform Jacobian-free linear solves at each Newton iteration with the preconditioner built from the preconditioning matrix $var element = document.getElementById("moose-equation-a21e60c6-e6b9-4f91-80e6-ef2a0d4e8c80");katex.render("\\mathbf{M}", element, {displayMode:false,throwOnError:false});$ . By default, the preconditioning matrix is block-diagonal with each block corresponding to a single MOOSE variable without custom preconditioning, refer to Preconditioning. Off-diagonal Jacobian terms are ignored. It essentially activates the matrix-free Jacobian-vector products, and the preconditioning matrix.
JFNK means there is no preconditioning during the Krylov solve. No Jacobian will be assembled. It essentially activates the matrix-free Jacobian-vector products and no preconditioning matrix.
LINEAR will use PETSc control parameter -snes_type ksponly to set the type of SNES for solving the linear system. Note that it only works when you have an exact Jacobian because it is not activating matrix-free calculations.
NEWTON means PETSc will use the Jacobian provided by kernels (typically not exact) to do the Krylov solve. If the Jacobian is not exact, Newton update in Eq. (4) will not reduce the residual effectively and typically results into an unconverged Newton iteration.
FD means the Jacobian is assembled via a finite differencing method. This is costly and should used only for testing purpose.

Preconditioning

Krylov methods need preconditioning to be efficient (or even effective!).
Even though the Jacobian is never formed, JFNK methods still require preconditioning.
MOOSE's automatic (without user intervention) preconditioning is fairly minimal.
Many options exist for implementing improved preconditioning in MOOSE.

Preconditioned JFNK

Using right preconditioning, solve

var element = document.getElementById("moose-equation-8334200a-746c-4f24-acb1-cecbbada6e13");katex.render("\\boldsymbol{R}'(\\boldsymbol{u}^{i-1}) \\boldsymbol{M}^{-1} (\\boldsymbol{M} \\delta \\boldsymbol{u}^{i}) = -\\boldsymbol{R}(\\boldsymbol{u}^{i-1})", element, {displayMode:true,throwOnError:false});

$var element = document.getElementById("moose-equation-5651d14c-429f-4c83-953f-61c32e95c6ba");katex.render("\\boldsymbol{M}", element, {displayMode:false,throwOnError:false});$ symbolically represents the preconditioning matrix or process
Inside GMRES, we only apply the action of $var element = document.getElementById("moose-equation-3a31e5ec-67d9-4a00-8d73-d37654addea9");katex.render("\\boldsymbol{M}^{-1}", element, {displayMode:false,throwOnError:false});$ on a vector
Right preconditioned matrix free version

var element = document.getElementById("moose-equation-d0c21b97-6026-45a5-93af-47f2f59e0ec8");katex.render("\\boldsymbol{R}' (\\boldsymbol{u}) \\boldsymbol{M}^{-1}\\boldsymbol{v} \\approx \\frac{\\boldsymbol{R}(\\boldsymbol{u} + \\epsilon \\boldsymbol{M}^{-1}\\boldsymbol{v}) - \\boldsymbol{R}(\\boldsymbol{u})}{\\epsilon}", element, {displayMode:true,throwOnError:false});

PETSc Options

PETSc parameters can either be set on the command line or by using the "petsc_options", "petsc_options_iname", and "petsc_options_value" parameters. Several PETSc parameters that users could frequently use are listed below:

Common stand-alone petsc options

`petsc_options`	Description
`-snes_ksp_ew`	Variable linear solve tolerance – useful for transient solves
`-help`	Show PETSc options during the solve

Common petsc options and values

`petsc_options_iname`	`petsc_options_value`	Description
`-pc_type`	`ilu`	Default for serial
	`bjacobi`	Default for parallel with `-sub_pc_type ilu`
	`asm`	Additive Schwartz with `-sub_pc_type ilu`
	`lu`	Full LU, serial only!
	`gamg`	PETSc Geometric AMG Preconditioner
	`hypre`	Hypre, usually used with `boomeramg`
`-sub_pc_type`	`ilu, lu, hypre`	Can be used with bjacobi or asm
`-pc_hypre_type`	`boomeramg`	Algebraic multigrid
`-pc_hypre_boomeramg` (cont.)		"Information Threshold" for AMG process
`_strong_threshold`	`0.0 - 1.0`	(0.7 is auto set for 3D)
`-ksp_gmres_restart`	`#`	Number of Krylov vectors to store

Input Parameters

time0System time
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:System time
verboseFalseSet to true to print additional information
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set to true to print additional information

Optional Parameters

accept_on_max_fixed_point_iterationFalseTrue to treat reaching the maximum number of fixed point iterations as converged.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to treat reaching the maximum number of fixed point iterations as converged.
auto_advanceFalseWhether to automatically advance sub-applications regardless of whether their solve converges, for transient executioners only.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to automatically advance sub-applications regardless of whether their solve converges, for transient executioners only.
custom_abs_tol1e-50The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on postprocessor defined by the custom_pp residual.
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on postprocessor defined by the custom_pp residual.
custom_ppPostprocessor for custom fixed point convergence check.
C++ Type:PostprocessorName
Unit:(no unit assumed)
Controllable:No
Description:Postprocessor for custom fixed point convergence check.
custom_rel_tol1e-08The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the postprocessor defined by custom_pp residual.
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the postprocessor defined by custom_pp residual.
direct_pp_valueFalseTrue to use direct postprocessor value (scaled by value on first iteration). False (default) to use difference in postprocessor value between fixed point iterations.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:True to use direct postprocessor value (scaled by value on first iteration). False (default) to use difference in postprocessor value between fixed point iterations.
disable_fixed_point_residual_norm_checkFalseDisable the residual norm evaluation thus the three parameters fixed_point_rel_tol, fixed_point_abs_tol and fixed_point_force_norms.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Disable the residual norm evaluation thus the three parameters fixed_point_rel_tol, fixed_point_abs_tol and fixed_point_force_norms.
fixed_point_abs_tol1e-50The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The absolute nonlinear residual to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
fixed_point_algorithmpicardThe fixed point algorithm to converge the sequence of problems.
Default:picard
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:picard, secant, steffensen
Controllable:No
Description:The fixed point algorithm to converge the sequence of problems.
fixed_point_force_normsFalseForce the evaluation of both the TIMESTEP_BEGIN and TIMESTEP_END norms regardless of the existence of active MultiApps with those execute_on flags, default: false.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Force the evaluation of both the TIMESTEP_BEGIN and TIMESTEP_END norms regardless of the existence of active MultiApps with those execute_on flags, default: false.
fixed_point_max_its1Specifies the maximum number of fixed point iterations.
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Specifies the maximum number of fixed point iterations.
fixed_point_min_its1Specifies the minimum number of fixed point iterations.
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Specifies the minimum number of fixed point iterations.
fixed_point_rel_tol1e-08The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The relative nonlinear residual drop to shoot for during fixed point iterations. This check is performed based on the main app's nonlinear residual.
relaxation_factor1Fraction of newly computed value to keep.Set between 0 and 2.
Default:1
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Fraction of newly computed value to keep.Set between 0 and 2.
transformed_postprocessorsList of main app postprocessors to transform during fixed point iterations
C++ Type:std::vector<PostprocessorName>
Unit:(no unit assumed)
Controllable:No
Description:List of main app postprocessors to transform during fixed point iterations
transformed_variablesList of main app variables to transform during fixed point iterations
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:List of main app variables to transform during fixed point iterations

Fixed Point Iterations Parameters

automatic_scalingFalseWhether to use automatic scaling for the variables.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to use automatic scaling for the variables.
compute_scaling_onceTrueWhether the scaling factors should only be computed once at the beginning of the simulation through an extra Jacobian evaluation. If this is set to false, then the scaling factors will be computed during an extra Jacobian evaluation at the beginning of every time step.
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether the scaling factors should only be computed once at the beginning of the simulation through an extra Jacobian evaluation. If this is set to false, then the scaling factors will be computed during an extra Jacobian evaluation at the beginning of every time step.
ignore_variables_for_autoscalingList of variables that do not participate in autoscaling.
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:List of variables that do not participate in autoscaling.
off_diagonals_in_auto_scalingFalseWhether to consider off-diagonals when determining automatic scaling factors.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to consider off-diagonals when determining automatic scaling factors.
resid_vs_jac_scaling_param0A parameter that indicates the weighting of the residual vs the Jacobian in determining variable scaling parameters. A value of 1 indicates pure residual-based scaling. A value of 0 indicates pure Jacobian-based scaling
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:A parameter that indicates the weighting of the residual vs the Jacobian in determining variable scaling parameters. A value of 1 indicates pure residual-based scaling. A value of 0 indicates pure Jacobian-based scaling
scaling_group_variablesName of variables that are grouped together for determining scale factors. (Multiple groups can be provided, separated by semicolon)
C++ Type:std::vector<std::vector<std::string>>
Unit:(no unit assumed)
Controllable:No
Description:Name of variables that are grouped together for determining scale factors. (Multiple groups can be provided, separated by semicolon)

Solver Variable Scaling Parameters

contact_line_search_allowed_lambda_cuts2The number of times lambda is allowed to be cut in half in the contact line search. We recommend this number be roughly bounded by 0 <= allowed_lambda_cuts <= 3
Default:2
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The number of times lambda is allowed to be cut in half in the contact line search. We recommend this number be roughly bounded by 0 <= allowed_lambda_cuts <= 3
contact_line_search_ltolThe linear relative tolerance to be used while the contact state is changing between non-linear iterations. We recommend that this tolerance be looser than the standard linear tolerance
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:The linear relative tolerance to be used while the contact state is changing between non-linear iterations. We recommend that this tolerance be looser than the standard linear tolerance
line_searchdefaultSpecifies the line search type (Note: none = basic)
Default:default
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:basic, bt, contact, cp, default, l2, none, project, shell
Controllable:No
Description:Specifies the line search type (Note: none = basic)
line_search_packagepetscThe solver package to use to conduct the line-search
Default:petsc
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:petsc, moose
Controllable:No
Description:The solver package to use to conduct the line-search

Solver Line Search Parameters

control_tagsAdds user-defined labels for accessing object parameters via control logic.
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Adds user-defined labels for accessing object parameters via control logic.
enableTrueSet the enabled status of the MooseObject.
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Set the enabled status of the MooseObject.
outputsVector of output names where you would like to restrict the output of variables(s) associated with this object
C++ Type:std::vector<OutputName>
Unit:(no unit assumed)
Controllable:No
Description:Vector of output names where you would like to restrict the output of variables(s) associated with this object
skip_exception_checkFalseSpecifies whether or not to skip exception check
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Specifies whether or not to skip exception check

Advanced Parameters

l_abs_tol1e-50Linear Absolute Tolerance
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Linear Absolute Tolerance
l_max_its10000Max Linear Iterations
Default:10000
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Linear Iterations
l_tol1e-05Linear Relative Tolerance
Default:1e-05
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Linear Relative Tolerance
reuse_preconditionerFalseIf true reuse the previously calculated preconditioner for the linearized system across multiple solves spanning nonlinear iterations and time steps. The preconditioner resets as controlled by reuse_preconditioner_max_linear_its
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:If true reuse the previously calculated preconditioner for the linearized system across multiple solves spanning nonlinear iterations and time steps. The preconditioner resets as controlled by reuse_preconditioner_max_linear_its
reuse_preconditioner_max_linear_its25Reuse the previously calculated preconditioner for the linear system until the number of linear iterations exceeds this number
Default:25
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Reuse the previously calculated preconditioner for the linear system until the number of linear iterations exceeds this number

Linear Solver Parameters

max_xfem_update4294967295Maximum number of times to update XFEM crack topology in a step due to evolving cracks
Default:4294967295
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Maximum number of times to update XFEM crack topology in a step due to evolving cracks
update_xfem_at_timestep_beginFalseShould XFEM update the mesh at the beginning of the timestep
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Should XFEM update the mesh at the beginning of the timestep

Xfem Fixed Point Iterations Parameters

mffd_typewpSpecifies the finite differencing type for Jacobian-free solve types. Note that the default is wp (for Walker and Pernice).
Default:wp
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:wp, ds
Controllable:No
Description:Specifies the finite differencing type for Jacobian-free solve types. Note that the default is wp (for Walker and Pernice).
petsc_optionsSingleton PETSc options
C++ Type:MultiMooseEnum
Unit:(no unit assumed)
Options:-dm_moose_print_embedding, -dm_view, -ksp_converged_reason, -ksp_gmres_modifiedgramschmidt, -ksp_monitor, -ksp_monitor_snes_lg-snes_ksp_ew, -ksp_snes_ew, -snes_converged_reason, -snes_ksp, -snes_ksp_ew, -snes_linesearch_monitor, -snes_mf, -snes_mf_operator, -snes_monitor, -snes_test_display, -snes_view
Controllable:No
Description:Singleton PETSc options
petsc_options_inameNames of PETSc name/value pairs
C++ Type:MultiMooseEnum
Unit:(no unit assumed)
Options:-ksp_atol, -ksp_gmres_restart, -ksp_max_it, -ksp_pc_side, -ksp_rtol, -ksp_type, -mat_fd_coloring_err, -mat_fd_type, -mat_mffd_type, -pc_asm_overlap, -pc_factor_levels, -pc_factor_mat_ordering_type, -pc_hypre_boomeramg_grid_sweeps_all, -pc_hypre_boomeramg_max_iter, -pc_hypre_boomeramg_strong_threshold, -pc_hypre_type, -pc_type, -snes_atol, -snes_linesearch_type, -snes_ls, -snes_max_it, -snes_rtol, -snes_divergence_tolerance, -snes_type, -sub_ksp_type, -sub_pc_type
Controllable:No
Description:Names of PETSc name/value pairs
petsc_options_valueValues of PETSc name/value pairs (must correspond with "petsc_options_iname"
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Values of PETSc name/value pairs (must correspond with "petsc_options_iname"

Petsc Parameters

n_max_nonlinear_pingpong100The maximum number of times the nonlinear residual can ping pong before requesting halting the current evaluation and requesting timestep cut
Default:100
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The maximum number of times the nonlinear residual can ping pong before requesting halting the current evaluation and requesting timestep cut
nl_abs_div_tol1e+50Nonlinear Absolute Divergence Tolerance. A negative value disables this check.
Default:1e+50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute Divergence Tolerance. A negative value disables this check.
nl_abs_step_tol0Nonlinear Absolute step Tolerance
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute step Tolerance
nl_abs_tol1e-50Nonlinear Absolute Tolerance
Default:1e-50
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Absolute Tolerance
nl_div_tol1e+10Nonlinear Relative Divergence Tolerance. A negative value disables this check.
Default:1e+10
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative Divergence Tolerance. A negative value disables this check.
nl_forced_its0The Number of Forced Nonlinear Iterations
Default:0
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The Number of Forced Nonlinear Iterations
nl_max_funcs10000Max Nonlinear solver function evaluations
Default:10000
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Nonlinear solver function evaluations
nl_max_its50Max Nonlinear Iterations
Default:50
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:Max Nonlinear Iterations
nl_rel_step_tol0Nonlinear Relative step Tolerance
Default:0
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative step Tolerance
nl_rel_tol1e-08Nonlinear Relative Tolerance
Default:1e-08
C++ Type:double
Unit:(no unit assumed)
Controllable:No
Description:Nonlinear Relative Tolerance
num_grids1The number of grids to use for a grid sequencing algorithm. This includes the final grid, so num_grids = 1 indicates just one solve in a time-step
Default:1
C++ Type:unsigned int
Unit:(no unit assumed)
Controllable:No
Description:The number of grids to use for a grid sequencing algorithm. This includes the final grid, so num_grids = 1 indicates just one solve in a time-step
residual_and_jacobian_togetherFalseWhether to compute the residual and Jacobian together.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Whether to compute the residual and Jacobian together.
snesmf_reuse_baseTrueSpecifies whether or not to reuse the base vector for matrix-free calculation
Default:True
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Specifies whether or not to reuse the base vector for matrix-free calculation
solve_typePJFNK: Preconditioned Jacobian-Free Newton Krylov JFNK: Jacobian-Free Newton Krylov NEWTON: Full Newton Solve FD: Use finite differences to compute Jacobian LINEAR: Solving a linear problem
C++ Type:MooseEnum
Unit:(no unit assumed)
Options:PJFNK, JFNK, NEWTON, FD, LINEAR
Controllable:No
Description:PJFNK: Preconditioned Jacobian-Free Newton Krylov JFNK: Jacobian-Free Newton Krylov NEWTON: Full Newton Solve FD: Use finite differences to compute Jacobian LINEAR: Solving a linear problem
splittingTop-level splitting defining a hierarchical decomposition into subsystems to help the solver.
C++ Type:std::vector<std::string>
Unit:(no unit assumed)
Controllable:No
Description:Top-level splitting defining a hierarchical decomposition into subsystems to help the solver.
use_pre_SMO_residualFalseCompute the pre-SMO residual norm and use it in the relative convergence check. The pre-SMO residual is computed at the begining of the time step before solution-modifying objects are executed. Solution-modifying objects include preset BCs, constraints, predictors, etc.
Default:False
C++ Type:bool
Unit:(no unit assumed)
Controllable:No
Description:Compute the pre-SMO residual norm and use it in the relative convergence check. The pre-SMO residual is computed at the begining of the time step before solution-modifying objects are executed. Solution-modifying objects include preset BCs, constraints, predictors, etc.

Nonlinear Solver Parameters

Restart Parameters

Overview
Solve Type
Preconditioning
PETSc Options
Input Parameters