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Mathematics of Computation

Published by the American Mathematical Society since 1960 (published as Mathematical Tables and other Aids to Computation 1943-1959), Mathematics of Computation is devoted to research articles of the highest quality in computational mathematics.

ISSN 1088-6842 (online) ISSN 0025-5718 (print)

The 2020 MCQ for Mathematics of Computation is 1.78.

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Finite element approximation of singular power-law systems
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by Adrian Hirn
Math. Comp. 82 (2013), 1247-1268
DOI: https://doi.org/10.1090/S0025-5718-2013-02668-3
Published electronically: January 18, 2013

Abstract:

Non-Newtonian fluid motions are often modeled by a power-law ansatz. In the present paper, we consider shear thinning singular power-law models which feature an unbounded viscosity in the limit of zero shear rate, and we study the finite element (FE) discretization of the equations of motion. In the case under consideration, numerical instabilities usually arise when the FE equations are solved via Newton’s method. In this paper, we propose a numerical method that enables the stable approximation of singular power-law systems and that is based on a simple regularization of the power-law model. Our proposed method generates a sequence of discrete functions that is computable in practice via Newton’s method and that converges to the exact solution of the power-law system for diminishing mesh size. First, for the regularized model we discuss Newton’s method and we show its stability in the sense that we derive an upper bound for the condition number of the Newton matrix. Then, we prove a priori error estimates that quantify the convergence of the proposed method. Finally, we illustrate numerically that our regularized approximation method surpasses the nonregularized one regarding accuracy and numerical efficiency.
References
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Bibliographic Information
  • Adrian Hirn
  • Affiliation: Institut für Angewandte Mathematik, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 294, 69120 Heidelberg, Germany
  • Email: adrian.hirn@iwr.uni-heidelberg.de
  • Received by editor(s): March 2, 2011
  • Received by editor(s) in revised form: November 4, 2011
  • Published electronically: January 18, 2013
  • Additional Notes: This work was supported by the International Graduate College IGK 710 “Complex Processes: Modeling, Simulation and Optimization” and the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) at the Interdisciplinary Center for Scientific Computing (IWR) of the University of Heidelberg
  • © Copyright 2013 American Mathematical Society
    The copyright for this article reverts to public domain 28 years after publication.
  • Journal: Math. Comp. 82 (2013), 1247-1268
  • MSC (2010): Primary 76A05, 35Q35, 65N30, 65N12, 65N15
  • DOI: https://doi.org/10.1090/S0025-5718-2013-02668-3
  • MathSciNet review: 3042563