Typically, the following conditions must be met for the PCG Lanczos eigensolver to be most efficient:
- The model would be a good candidate for using the PCG solver in a similar static or full transient analysis.
- The number of requested modes is less than a hundred.
- The beginning frequency input on the MODOPT command is zero (or near zero).
The PCG Lanczos eigensolver (like all iterative solvers) is most efficient when the solution converges
quickly. So if the model would not converge quickly in a similar static or full transient analysis, it is ex-
pected that the PCG Lanczos eigensolver will also not converge quickly and thus be less efficient. The
PCG Lanczos eigensolver is most efficient when finding the lowest natural frequencies of the system.
(For detailed information on measuring PCG Lanczos solver efficiency, see PCG Lanczos Solver Perform-
ance Output in the Performance Guide.) As the number of requested modes begins to approach one
hundred, or when requesting only higher modes, the Block Lanczos eigensolver becomes a better
choice.
Other factors such as the size of the problem and the hardware being used can affect the eigensolver
selection strategy. For example, when solving problems where the Block Lanczos eigensolver runs in
an out-of-core mode on a system with very slow hard drive speed (that is, bad I/O performance) the
PCG Lanczos eigensolver may be the better choice as it does significantly less I/O, assuming a Lev_Diff
value of 1 through 4 is used (PCGOPT command). Another example would be solving a model with 15
million degrees of freedom. In this case, the Block Lanczos eigensolver would need approximately 300
GB of hard drive space. If computer resources are limited, the PCG Lanczos eigensolver may be the only
viable choice for solving this problem since the PCG Lanczos eigensolver does much less I/O than the
Block Lanczos eigensolver. The PCG Lanczos eigensolver has been successfully used on problems ex-
ceeding 100 million degrees of freedom.
3.8.3. Supernode (SNODE) Method
The Supernode (SNODE) solver is used to solve large, symmetric eigenvalue problems for many modes
(up to 10,000 and beyond) in one solution.Typically, the reason for seeking many modes is to perform
a subsequent mode-superposition or PSD analysis to solve for the response in a higher frequency range.
A supernode is a group of nodes from a group of elements. The supernodes for the model are generated
automatically by the program. This method first calculates eigenmodes for each supernode in the range
of 0.0 to FREQE*RangeFact (where RangeFact is specified by the SNOPTION command and defaults to
2.0), and then uses the supernode eigenmodes to calculate the global eigenmodes of the model in the
range of FREQB to FREQE (where FREQB and FREQE are specified by the MODOPT command).
Typically, this method offers faster solution times than Block Lanczos or PCG Lanczos if the number of
modes requested is more than 200.The accuracy of the Supernode solution can be controlled by the
SNOPTION command. For more information, see Supernode Method in the Mechanical APDL Theory
Reference.
The lumped mass matrix option (LUMPM,ON) is not allowed when using the Supernode mode-extraction
method. The consistent mass matrix option will be used regardless of the LUMPM setting.
When to Choose Supernode
Typically, the following conditions must be met for the Supernode eigensolver to be most efficient:
- The model is a good candidate for using the sparse solver in a similar static or full transient analysis (that
is, the model is dominated with beams/shells or has a thin structure).
Release 15.0 - © SAS IP, Inc. All rights reserved. - Contains proprietary and confidential informationComparing Mode-Extraction Methods