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BIT Numerical Mathematics
Erschienen in: BIT Numerical Mathematics 2/2020

06.01.2020

Orthogonality constrained gradient reconstruction for superconvergent linear functionals

verfasst von: Roberto Porcù, Maurizio M. Chiaramonte

Erschienen in: BIT Numerical Mathematics | Ausgabe 2/2020

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Abstract

The post-processing of the solution of variational problems discretized with Galerkin finite element methods is particularly useful for the computation of quantities of interest. Such quantities are generally expressed as linear functionals of the solution and the error of their approximation is bounded by the error of the solution itself. Several a posteriori recovery procedures have been developed over the years to improve the accuracy of post-processed results. Nonetheless such recovery methods usually deteriorate the convergence properties of linear functionals of the solution and, as a consequence, of the quantities of interest as well. The paper develops an enhanced gradient recovery scheme able to both preserve the good qualities of the recovered gradient and increase the accuracy and the convergence rates of linear functionals of the solution.
Vorheriger Artikel Jumping with variably scaled discontinuous kernels (VSDKs)
Nächster Artikel Local projection stabilization with discontinuous Galerkin method in time applied to convection dominated problems in time-dependent domains

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Metadaten
Titel
Orthogonality constrained gradient reconstruction for superconvergent linear functionals
verfasst von
Roberto Porcù
Maurizio M. Chiaramonte
Publikationsdatum
06.01.2020
Verlag
Springer Netherlands
Erschienen in
BIT Numerical Mathematics / Ausgabe 2/2020
Print ISSN: 0006-3835
Elektronische ISSN: 1572-9125
DOI
https://doi.org/10.1007/s10543-019-00775-2

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