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Enhancing accuracy with a convolution filter: What works and why!
Barcelona Supercomputing Centre- Centro Nacional de Supercomputación (BSC-CNS), c / Jordi Girona, 29 - Nexus II, CASE department, 08034- Barcelona, Spain.
Department of Aerospace Engineering, San Diego State University, 5500 Campanile Drive, MC 1308 San Diego, CA 92182, United States.
School of Mathematical Science, University of Electronic Science and Technology of China.
Applied Mathematics & Statistics, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80402, United States.ORCID iD: 0000-0002-6252-8199
2020 (English)In: Computers & Fluids, ISSN 0045-7930, E-ISSN 1879-0747, Vol. 213, p. 104727-104727, article id 104727Article in journal (Refereed) Published
Abstract [en]

In this paper we present a simplified discussion of the Smoothness-Increasing Accuracy-Conserving (SIAC) filter. We demonstrate the importance of appropriately initializing the data in order to be able to extract higher orders of accuracy by comparing the nodal and modal forms of a discontinuous Galerkin approximation applied to a simplified cubic polynomial. Using the modal form, we are able to exactly reproduce the cubic polynomial, whereas this reconstruction does not occur using the nodal form. Furthermore, we tie the ability of the filter to extract extra accuracy to its accurate wave propagation properties.

Place, publisher, year, edition, pages
Elsevier BV , 2020. Vol. 213, p. 104727-104727, article id 104727
Keywords [en]
Convolution filtering, Discontinuous Galerkin, Smoothness-increasing Accuracy-conserving (SIAC) filter, Superconvergence
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-334834DOI: 10.1016/j.compfluid.2020.104727ISI: 000595147400012Scopus ID: 2-s2.0-85092075326OAI: oai:DiVA.org:kth-334834DiVA, id: diva2:1791838
Note

QC 20230828

Available from: 2023-08-27 Created: 2023-08-27 Last updated: 2023-08-28Bibliographically approved

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Ryan, Jennifer K.

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CiteExportLink to record
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