Research Article

Parallelization and scalability of a spectral element channel flow solver for incompressible Navier–Stokes equations

  1. C. W. Hamman,
  2. R. M. Kirby*,
  3. M. Berzins

Article first published online: 3 APR 2007

DOI: 10.1002/cpe.1181

Issue

Concurrency and Computation: Practice and Experience

Concurrency and Computation: Practice and Experience

Volume 19, Issue 10, pages 1403–1422, July 2007

Additional Information

How to Cite

Hamman, C. W., Kirby, R. M. and Berzins, M. (2007), Parallelization and scalability of a spectral element channel flow solver for incompressible Navier–Stokes equations. Concurrency Computat.: Pract. Exper., 19: 1403–1422. doi: 10.1002/cpe.1181

Author Information

  1. Scientific Computing and Imaging Institute, University of Utah, 50 South Central Campus Drive, Room 3490, Salt Lake City, UT 84112, U.S.A.

*Scientific Computing and Imaging Institute, School of Computing, University of Utah, 50 South Central Campus Drive, Room 3490, Salt Lake City, UT 84112, U.S.A.

Publication History

  1. Issue published online: 8 JUN 2007
  2. Article first published online: 3 APR 2007
  3. Manuscript Accepted: 29 JAN 2007
  4. Manuscript Revised: 9 NOV 2006
  5. Manuscript Received: 31 MAR 2006

Funded by

  • U.S. Department of Energy. Grant Number: W-7405-ENG-48
  • NSF. Grant Number: CCF-0347791

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Keywords:

  • parallel computing;
  • spectral/hp elements;
  • direct numerical simulation;
  • turbulent channel flow;
  • performance modeling

Abstract

Direct numerical simulation (DNS) of turbulent flows is widely recognized to demand fine spatial meshes, small timesteps, and very long runtimes to properly resolve the flow field. To overcome these limitations, most DNS is performed on supercomputing machines. With the rapid development of terascale (and, eventually, petascale) computing on thousands of processors, it has become imperative to consider the development of DNS algorithms and parallelization methods that are capable of fully exploiting these massively parallel machines. A highly parallelizable algorithm for the simulation of turbulent channel flow that allows for efficient scaling on several thousand processors is presented. A model that accurately predicts the performance of the algorithm is developed and compared with experimental data. The results demonstrate that the proposed numerical algorithm is capable of scaling well on petascale computing machines and thus will allow for the development and analysis of high Reynolds number channel flows. Copyright © 2007 John Wiley & Sons, Ltd.

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