Finite element approximation of singular powerlaw systems
Author:
Adrian Hirn
Journal:
Math. Comp. 82 (2013), 12471268
MSC (2010):
Primary 76A05, 35Q35, 65N30, 65N12, 65N15
Published electronically:
January 18, 2013
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Abstract: NonNewtonian fluid motions are often modeled by a powerlaw ansatz. In the present paper, we consider shear thinning singular powerlaw 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 powerlaw systems and that is based on a simple regularization of the powerlaw 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 powerlaw 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.
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Additional Information
Adrian Hirn
Affiliation:
Institut für Angewandte Mathematik, RuprechtKarlsUniversität Heidelberg, Im Neuenheimer Feld 294, 69120 Heidelberg, Germany
Email:
adrian.hirn@iwr.uniheidelberg.de
DOI:
http://dx.doi.org/10.1090/S002557182013026683
PII:
S 00255718(2013)026683
Keywords:
Powerlaw fluid,
finite element method,
error analysis
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
Article copyright:
© Copyright 2013 American Mathematical Society
The copyright for this article reverts to public domain 28 years after publication.
