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Scalable implicit flow solver for realistic wing simulations with flow control

  • Michel Rasquin
  • , Cameron Smith
  • , Kedar Chitale
  • , E. Seegyoung Seol
  • , Benjamin A. Matthews
  • , Jeffrey L. Martin
  • , Onkar Sahni
  • , Raymond M. Loy
  • , Mark S. Shephard
  • , Kenneth E. Jansen
  • Argonne National Laboratory
  • Rensselaer Polytechnic Institute
  • University of Colorado Boulder

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

Massively parallel computation provides an enormous capacity to perform simulations on a timescale that can change the paradigm of how scientists, engineers, and other practitioners use simulations to address discovery and design. This work considers an active flow control application on a realistic and complex wing design that could be leveraged by a scalable, fully implicit, unstructured flow solver and access to high-performance computing resources. The article describes the active flow control application; then summarizes the main features in the implementation of a massively parallel turbulent flow solver, PHASTA; and finally demonstrates the methods strong scalability at extreme scale. Scaling studies performed with unstructured meshes of 11 and 92 billion elements on the Argonne Leadership Computing Facility's Blue Gene/Q Mira machine with up to 786,432 cores and 3,145,728 MPI processes.

Original languageEnglish
Article number6970999
Pages (from-to)13-21
Number of pages9
JournalComputing in Science and Engineering
Volume16
Issue number6
DOIs
StatePublished - Nov 1 2014
Externally publishedYes

Keywords

  • finite element methods
  • high-performance computing
  • HPC
  • leadership computing
  • numerical analysis
  • parallel algorithms
  • partial differential equations
  • scientific computing

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