A posteriori error estimation for the Stokes problem: Anisotropic and isotropic discretizations

• Main Authors: Creusé, Emmanuel, Kunert, Gerd, Nicaise, Serge
• Language: English
• Published: Technische Universität Chemnitz 2003
• Subjects: info:eu-repo/classification/ddc/510; ddc:510; Stokes problem; anisotropic solution; error estimator; stretched elements
• Online Access: http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200300057; https://monarch.qucosa.de/id/qucosa%3A17956; https://monarch.qucosa.de/api/qucosa%3A17956/attachment/ATT-0/; https://monarch.qucosa.de/api/qucosa%3A17956/attachment/ATT-1/; https://monarch.qucosa.de/api/qucosa%3A17956/attachment/ATT-2/; https://monarch.qucosa.de/api/qucosa%3A17956/attachment/ATT-3/

The paper presents a posteriori error estimators for the stationary Stokes problem. We consider anisotropic finite element discretizations (i.e. elements with very large aspect ratio) where conventional, isotropic error estimators fail. Our analysis covers two- and three-dimensional domains, conforming and nonconforming discretizations as well as different elements. This large variety of settings requires different approaches and results in different estimators. Furthermore many examples of finite element pairs that are covered by the analysis are presented. Lower and upper error bounds form the main result with minimal assumptions on the elements. The lower error bound is uniform with respect to the mesh anisotropy with the exception of nonconforming 3D discretizations made of pentahedra or hexahedra. The upper error bound depends on a proper alignment of the anisotropy of the mesh which is a common feature of anisotropic error estimation. In the special case of isotropic meshes, the results simplify, and upper and lower error bounds hold unconditionally. Some of the corresponding results seem to be novel (in particular for 3D domains), and cover element pairs of practical importance. The numerical experiments confirm the theoretical predictions and show the usefulness of the anisotropic error estimators.