An equilibrated flux a posteriori error estimator for defeaturing problems - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2023

An equilibrated flux a posteriori error estimator for defeaturing problems

Abstract

An a posteriori error estimator based on an equilibrated flux reconstruction is proposed for defeaturing problems in the context of finite element discretizations. Defeaturing consists in the simplification of a geometry by removing features that are considered not relevant for the approximation of the solution of a given PDE. In this work, the focus is on Poisson equation with Neumann boundary conditions on the feature boundary. The estimator accounts both for the so-called defeaturing error and for the numerical error committed by approximating the solution on the defeatured domain. Unlike other estimators that were previously proposed for defeaturing problems, the use of the equilibrated flux reconstruction allows to obtain a sharp bound for the numerical component of the error. Furthermore, it does not require the evaluation of the normal trace of the numerical flux on the feature boundary: this makes the estimator well-suited for finite element discretizations, in which the normal trace of the numerical flux is typically discontinuous across elements. The reliability of the estimator is proven and verified on several numerical examples. Its capability to identify the most relevant features is also shown, in anticipation of a future application to an adaptive strategy.
Fichier principal
Vignette du fichier
Pub_defeaturing.pdf (1.05 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04379236 , version 1 (08-01-2024)

Licence

Attribution

Identifiers

  • HAL Id : hal-04379236 , version 1

Cite

Annalisa Buffa, Ondine Chanon, Denise Grappein, Rafael Vázquez, Martin Vohralík. An equilibrated flux a posteriori error estimator for defeaturing problems. 2024. ⟨hal-04379236⟩
32 View
9 Download

Share

Gmail Facebook X LinkedIn More