A data-parallel line relaxation method for the Navier-Stokes equations

Michael J. Wright, Graham V Candler, Deepak Bose

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations


The Gauss-Seidel Line Relaxation (GSLR) method of MacCormack is modified for the simulation of viscous flows on massively parallel computers. The resulting Data-Parallel Line Relaxation (DPLR) 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 DPLR 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 DPLR method combines the fast convergence of the GSLR method with a high parallel efficiency, and thus shows promise for large-scale simulation of viscous flows.

Original languageEnglish (US)
Number of pages10
StatePublished - Jan 1 1997
Event13th Computational Fluid Dynamics Conference, 1997 - Snowmass Village, United States
Duration: Jun 29 1997Jul 2 1997


Other13th Computational Fluid Dynamics Conference, 1997
CountryUnited States
CitySnowmass Village

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