Data-parallel line relaxation method for the Navier-Stokes equations

Michael J. Wright, Graham V. Candler, Deepak Bose

Research output: Contribution to journalArticlepeer-review

646 Scopus citations

Abstract

The Gauss-Seidel line relaxation method is modified for the simulation of viscous flows on massively parallel computers. The resulting data-parallel line relaxation method is shown to have good convergence properties for a series of test cases. The new method requires significantly more memory than the previously developed data-parallel relaxation methods, but it reaches a steady-state solution in much less time for all cases tested to date. In addition, the data-parallel line relaxation method shows good convergence properties even on the high-cell-aspect-ratio grids required to simulate high-Reynolds-number flows. The new method is implemented using message passing on the Cray T3E, and the parallel performance of the method on this machine is discussed. The data-parallel line relaxation method combines the fast convergence of the Gauss-Seidel line relaxation method with a high parallel efficiency and thus shows promise for large-scale simulation of viscous flows.

Original languageEnglish (US)
Pages (from-to)1603-1609
Number of pages7
JournalAIAA Journal
Volume36
Issue number9
DOIs
StatePublished - Sep 1998

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