Implicit Solvers
Implicit Solvers with Parallel Version Compatibility Table
| Option | Radioss SMP | Radioss MPP SPMD | |
|---|---|---|---|
| Implicit Resolution | /IMPL/SOLVER/1 | PCG | PCG |
| /IMPL/SOLVER/2 | MUMPS | MUMPS | |
| Buckling Modes | /IMPL/BUCKL/1 | N/A | MUMPS |
| /IMPL/BUCKL/2 | N/A | MUMPS | |
| Eigen Modes | /EIG (Starter) | N/A | MUMPS |
- PCG:
- Iterative Preconditioned Conjugate Gradient
- MUMPS:
- Massively Parallel Multi-Frontal Solver
Linear Solvers
Direct, Iterative and Mixed
Linear solver will be used in both Linear and Nonlinear Analyses, so it is very important to choose an appropriate solver for your application.
The PCG (Preconditioned Conjugate Gradient) iterative solver has been available from the first version of Radioss Implicit followed by MUMPS solvers (default solver).
If you are not sure as to which solver to use for a particular application, it is recommended to try a MUMPS solver first. For large simulations, such as Full-Vehicle Analysis, where memory might be an issue, the PCG method with higher quality preconditioner (this is set using /IMPL/PREPAT/n, for example: n=2) could be used instead.
For a Nonlinear Analysis, it is worth comparing the two methods on your model by running a simple Linear Analysis before launching the actual analysis.
Nonlinear Solvers
Modified Newton and Quasi-Newton Methods
Once again, the choice of solver depends on the type of analysis. Generally, the Quasi-Newton method is more suitable for an analysis with a high degree of nonlinearity, but it requires more memory and costs more per iteration.