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Linking ghost penalty and aggregated unfitted methods

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In this work, we analyse the links between ghost penalty stabilisation and aggregation-based discrete extension operators for the numerical approximation of elliptic partial differential equations on unfitted meshes. We explore the behaviour of ghost penalty methods in the limit as the penalty parameter goes to infinity, which returns a strong version of these methods. We observe that these methods suffer locking in that limit. On the contrary, aggregated finite element spaces are locking-free because they can be expressed as an extension operator from well-posed to ill-posed degrees of freedom. Next, we propose novel ghost penalty methods that penalise the distance between the solution and its aggregation-based discrete extension. These methods are locking-free and converge to aggregated finite element methods in the infinite penalty parameter limit. We include an exhaustive set of numerical experiments in which we compare weak (ghost penalty) and strong (aggregated finite elements) schemes in terms of error quantities, condition numbers and sensitivity with respect to penalty coefficients on different geometries, intersection locations and mesh topologies.
Original languageEnglish
Article number114232
Pages (from-to)1-23
Number of pages23
JournalComputer Methods in Applied Mechanics and Engineering
Volume388
Early online date9 Nov 2021
DOIs
Publication statusPublished - 1 Jan 2022
Externally publishedYes

Bibliographical note

© 2021 Elsevier B.V.

Funding

This research was partially funded by the Australian Government through the Australian Research Council (project number DP210103092), the European Commission under the FET-HPC ExaQUte project (Grant agreement ID: 800898) within the Horizon 2020 Framework Programme and the projects RTI2018-096898-B-I00 and ERC2018-092843 from the “FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación”. F. Verdugo acknowledges support from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa Programme for Centers of Excellence in R&D (CEX2018-000797-S)”. F. Verdugo acknowledges support from the Secretaria d'Universitats i Recerca of the Catalan Government, Spain in the framework of the Beatriu Pinós Program (Grant Id.: 2016 BP 00145). This work was also supported by computational resources provided by the Australian Government through NCI under the National Computational Merit Allocation Scheme. This research was partially funded by the Australian Government through the Australian Research Council (project number DP210103092), the European Commission under the FET-HPC ExaQUte project (Grant agreement ID: 800898 ) within the Horizon 2020 Framework Programme and the projects RTI2018-096898-B-I00 and ERC2018-092843 from the “ FEDER/Ministerio de Ciencia e Innovación – Agencia Estatal de Investigación ”. F. Verdugo acknowledges support from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa Programme for Centers of Excellence in R&D ( CEX2018-000797-S )”. F. Verdugo acknowledges support from the Secretaria d’Universitats i Recerca of the Catalan Government, Spain in the framework of the Beatriu Pinós Program (Grant Id.: 2016 BP 00145 ). This work was also supported by computational resources provided by the Australian Government through NCI under the National Computational Merit Allocation Scheme.

FundersFunder number
Secretaria d'Universitats i Recerca of the Catalan Government, Spain
Secretaria d’Universitats i Recerca of the Catalan Government, Spain2016 BP 00145
National Computational Infrastructure
Horizon 2020 Framework ProgrammeRTI2018-096898-B-I00
Australian Government
European Commission800898
Australian Research CouncilDP210103092
Ministerio de Economía y CompetitividadCEX2018-000797-S
Ministerio de Ciencia e Innovación
European Regional Development Fund
Agencia Estatal de Investigación

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      SDG 16 Peace, Justice and Strong Institutions

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