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AMPView
The frontal direct solver utilizes the element-by-element assembly and factoring technology to
reduce the memory needed for solving large problems and is provided as a backup to the
sparse direct solver. And for extremely large problem with limited memory constraint, the
element-by-element Jacobian Preconditioned Conjugate Gradient (JPCG) solver can be used
since it uses a matrix free iteration method, but is generally slower than the sparse iterative
solver.
Model>System>Iteration Control>Iterative Solver Controls.
Currently, following methods are available for sparse iterative solver:
PCG:
Preconditioned Conjugate Gradient method
BiCGSTAB: BiConjugate Gradients Stabilized method
MLBiCGSTAB: a BiCGSTAB variant based on Multiple Lanczos Starting Vectors
TFQMR:
Transpose-Free QMR (Quasi-Minimal Residual)
BGMRES:
Block General Minimal Residual
These sparse iterative methods use an incomplete LU factorization matrix as the
preconditioner for the iteration method. Since all iterative solver techniques are sensitive to
the condition of the equation being solved, it should always be used with small enough error
control to ensure the solution is correct. The default error_norm for the iterative solver is 1.e-6,
and the incomplete LU factoring skip tolerance is automatically determined based on the ratio
of the minimum to the maximum diagonal of the equations. This setting is found to be a reliable
technique from our empirical testing, and this default setting handles well for most problems
including shells, high order p element and general problems. In general, this error norm can be
lowered to around 1.e-4 for most problems to speed up the solution time. For most problems,
this factor_tol is generally found to in the range of 1e-2 to 1e-8. If necessary, the user can
overwrite the automatic setting so the tolerance will be computed based on the value of
factor_tol times the maximum diagonal term of the equations. Relaxing these tolerances may
speed up the solution, but the iterative method may not converge and could cause the program
to stop after reaching its maximum iteration limits.
Generally, PCG method performs well in most cases, but when it has erratic convergence
behavior either due to poor preconditioning treatment or ill-conditioned equations due to the
multi-physics properties, other methods have proven to be more stable. More detailed
reference about these methods can be found in general numerical methods literature.
Model>System>Solution Control>Time Stepping/Pseudo Time Controls
For all analyses, the user specifies a time step size, the analysis start time and the finish time
to control the solution. In the steady state analysis or static analysis, these time controls are
pseudo time steps in the sense that they are used to control how the specific boundary
conditions or loads are applied. For dynamic or transient analysis, the time controls should be
the real analysis start/stop/increment that control the analysis. The program, by default,
assumes the analysis is static or steady-state and fills in the default values for the user at the
beginning.
Model>System>Solution Control>Time Stepping/Pseudo Time Controls>Analysis Start/Finish
Time
The user specifies the analysis start and finish time here. For restart analysis, the start time will
come from the restart file, and the start time control will automatically change to the values
specified in the restart file.
Model>System>Solution Control>Time Stepping/Pseudo Time Controls>Max Iteration limit per
step, Convergence Error Norm
This will limit the iteration during a equilibrium iteration in a single step. An equilibrium check is
performed to ensure the error norm is under the specified values
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