A least square extrapolation method for the a posteriori error estimate of the incompressible Navier Stokes problem

M. Garbey, W. Shyy

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

A posteriori error estimators are fundamental tools for providing confidence in the numerical computation of PDEs. To date, the main theories of a posteriori estimators have been developed largely in the finite element framework, for either linear elliptic operators or non-linear PDEs in the absence of disparate length scales. On the other hand, there is a strong interest in using grid refinement combined with Richardson extrapolation to produce CFD solutions with improved accuracy and, therefore, a posteriori error estimates. But in practice, the effective order of a numerical method often depends on space location and is not uniform, rendering the Richardson extrapolation method unreliable. We have recently introduced (Garbey, 13th International Conference on Domain Decomposition, Barcelona, 2002; 379-386; Garbey and Shyy, J. Comput. Phys. 2003; 186:1-23) a new method which estimates the order of convergence of a computation as the solution of a least square minimization problem on the residual. This method, called least square extrapolation, introduces a framework facilitating multi-level extrapolation, improves accuracy and provides a posteriori error estimate. This method can accommodate different grid arrangements. The goal of this paper is to investigate the power and limits of this method via incompressible Navier Stokes flow computations.

Original languageEnglish (US)
Pages (from-to)43-59
Number of pages17
JournalInternational Journal for Numerical Methods in Fluids
Volume48
Issue number1
DOIs
StatePublished - May 10 2005

Keywords

  • A posteriori error estimate
  • Least square method
  • Partial differential equations
  • Richardson extrapolation

ASJC Scopus subject areas

  • Computational Mechanics
  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Applied Mathematics

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