Mechanical APDL Basic Analysis Guide - Ansys

used. Running the distributed sparse solver in out-of-core mode on a shared disk resource (for example,
NAS or SAN disk) is typically not recommended. You can effectively run the distributed sparse solver using
multiple processes with one drive (or a shared disk resource) if:
• The problem size is small enough relative to the physical memory on the system that the system buffer
cache can hold all of the distributed sparse solver (I/O) files and other ANSYS files in memory.
• You have a very fast hard drive configuration that can handle multiple I/O requests simultaneously
(typically found on proprietary UNIX systems). For a shared disk resource on a cluster, a very fast interconnect
is also needed to handle the I/O traffic along with the regular communication of data within
the solver.
• You use the DSPOPTION,,INCORE command to force the distributed sparse solver into an in-core mode.
5.2.2. The Preconditioned Conjugate Gradient (PCG) Solver
The PCG solver starts with element matrix formulation. Instead of factoring the global matrix, the PCG solver
assembles the full global stiffness matrix and calculates the DOF solution by iterating to convergence
(starting with an initial guess solution for all DOFs). The PCG solver uses a proprietary preconditioner that
is material property and element-dependent.
• The PCG solver is usually about 4 to 10 times faster than the JCG solver for structural solid elements
and about 10 times faster then JCG for shell elements. Savings increase with the problem size.
• The PCG solver usually requires approximately twice as much memory as the JCG solver because it retains
two matrices in memory:
– The preconditioner, which is almost the same size as the stiffness matrix
– The symmetric, nonzero part of the stiffness matrix
5.2.2.The Preconditioned Conjugate Gradient (PCG) Solver
You can use Table 5.1: Solver Selection Guidelines (p. 98) as a general guideline for memory usage.
This solver is available only for static or steady-state analyses and transient analyses, or for PCG Lanczos
modal analyses. The PCG solver performs well on most static analyses and certain nonlinear analyses. It is
valid for elements with symmetric, sparse, definite or indefinite matrices. Contact analyses that use penaltybased
or penalty and augmented Lagrangian-based methods work well with the PCG solver as long as
contact does not generate rigid body motions throughout the nonlinear iterations (for example, full loss of
contact). However, Lagrange-formulation contact methods and incompressible u-P formulations cannot be
used by the PCG solver and require the sparse solver.
Because they take fewer iterations to converge, well-conditioned models perform better than ill-conditioned
models when using the PCG solver. Ill-conditioning often occurs in models containing elongated elements
(i.e., elements with high aspect ratios) or contact elements. To determine if your model is ill-conditioned,
view the Jobname.PCS file to see the number of PCG iterations needed to reach a converged solution.
Generally, static or full transient solutions that require more than 1500 PCG iterations are considered to be
ill-conditioned for the PCG solver. When the model is very ill-conditioned (e.g., over 3000 iterations are
needed for convergence) a direct solver may be the best choice unless you need to use an iterative solver
due to memory or disk space limitations.
For ill-conditioned models, the PCGOPT command can sometimes reduce solution times. You can adjust
the level of difficulty (PCGOPT,Lev_Diff) depending on the amount of ill-conditioning in the model. By
default, ANSYS automatically adjusts the level of difficulty for the PCG solver based on the model. However,
sometimes forcing a higher level of difficulty value for ill-conditioned models can reduce the overall solution
time.
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