UNSYMMETRIC CONJUGATE-GRADIENT METHODS AND SPARSE DIRECT METHODS IN FINITE-ELEMENT FLOW SIMULATION

被引:27
作者
HOWARD, D
CONNOLLEY, WM
ROLLETT, JS
机构
[1] Numerical Analysis Group, Oxford University Computing Laboratory, Oxford, OXI 3QD
关键词
Bi‐conjugate gradient; Boussinesq approximation Newton method; Conjugate gradient; Conjugate gradient squared; Finite elements; Flow simulation; Frontal solver; Galerkin method; Navier–Stokes; Petrov–Galerkin; Picard iteration; Preconditioning; Vector processor;
D O I
10.1002/fld.1650100806
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
A series of numerical experiments on the Cray XMP/48 and on the Cray 2 investigate the robustness and economy of direct and unsymmetric conjugate gradient (CG) type methods for the solution of matrix systems arising from a 3D FEM discretization of fluid flow problems. Computations on a Boussinesq flow model problem with either ILU preconditioned or unpreconditioned unsymmetric CG methods are presented. Such experiments seem to indicate that the unpreconditioned BICG method is robust for moderately non‐linear incompressible Navier–Stokes FEM discretizations and that the ILU preconditioned BICG method is very robust and more economic than an unsymmetric frontal solver when the generous memory of the Cray 2 is exploited to store both the matrix and its preconditioner. We cover some of the programming aspects of direct and iterative methods on a supercomputer and find that direct methods have advantages: the crucial CPU‐consuming area of code is compact but overwhelming, and its percentage of total CPU usage is independent of the spectral properties of the matrix involved. An optimal implementation of the unsymmetric CG method is more difficult because its work is related to the spectral distribution of the matrix considered and because there is no single portion of the code that overwhelmingly dominates the CPU usuage. Copyright © 1990 John Wiley & Sons, Ltd
引用
收藏
页码:925 / 945
页数:21
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