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dc.contributor.authorParsani, Matteo
dc.contributor.authorBoukharfane, Radouan
dc.contributor.authorNolasco, Irving Reyna
dc.contributor.authorDel Rey Fernández, David C.
dc.contributor.authorZampini, Stefano
dc.contributor.authorHadri, Bilel
dc.contributor.authorDalcin, Lisandro
dc.date.accessioned2020-09-28T11:36:04Z
dc.date.available2020-09-28T11:36:04Z
dc.date.issued2020-09-22
dc.date.submitted2020-02-08
dc.identifier.citationParsani, M., Boukharfane, R., Nolasco, I. R., Del Rey Fernández, D. C., Zampini, S., Hadri, B., & Dalcin, L. (2020). High-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver. Journal of Computational Physics, 109844. doi:10.1016/j.jcp.2020.109844
dc.identifier.issn0021-9991
dc.identifier.doi10.1016/j.jcp.2020.109844
dc.identifier.urihttp://hdl.handle.net/10754/665342
dc.description.abstractThis work reports on the performances of a fully-discrete hp-adaptive entropy stable discontinuous collocated Galerkin method for the compressible Naiver–Stokes equations. The resulting code framework is denoted by SSDC, the first S for entropy, the second for stable, and DC for discontinuous collocated. The method is endowed with the summation-by-parts property, allows for arbitrary spatial and temporal order, and is implemented in an unstructured high performance solver. The considered class of fully-discrete algorithms are systematically designed with mimetic and structure preserving properties that allow the transfer of continuous proofs to the fully discrete setting. Our goal is to provide numerical evidence of the adequacy and maturity of these high-order methods as potential base schemes for the next generation of unstructured computational fluid dynamics tools. We provide a series of test cases of increased difficulty, ranging from non-smooth to turbulent flows, in order to evaluate the numerical performance of the algorithms. Results on weak and strong scaling of the distributed memory implementation demonstrate that the parallel SSDC solver can scale efficiently over 100,000 processes.
dc.description.sponsorshipThe research reported in this paper was funded by King Abdullah University of Science and Technology. We are thankful to the Supercomputing Laboratory and the Extreme Computing Research Center at King Abdullah University of Science and Technology for their computing resources. Special thanks are extended to the McLaren F1 racing Team for providing experimental data and CAD geometries for the delta wing test case.
dc.publisherElsevier BV
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0021999120306185
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Computational Physics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Computational Physics, [, , (2020-09-22)] DOI: 10.1016/j.jcp.2020.109844 . © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleHigh-order accurate entropy-stable discontinuous collocated Galerkin methods with the summation-by-parts property for compressible CFD frameworks: Scalable SSDC algorithms and flow solver
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentKing Abdullah University of Science and Technology (KAUST), Computer Electrical and Mathematical Science and Engineering Division (CEMSE), Extreme Computing Research Center (ECRC), Thuwal, Saudi Arabia.
dc.contributor.departmentSupercomputing, Computational Scientists
dc.identifier.journalJournal of Computational Physics
dc.rights.embargodate2022-09-22
dc.eprint.versionPost-print
dc.contributor.institutionNational Institute of Aerospace, Hampton, VA, United States.
dc.identifier.pages109844
kaust.personParsani, Matteo
kaust.personBoukharfane, Radouan
kaust.personNolasco, Irving Reyna
kaust.personZampini, Stefano
kaust.personHadri, Bilel
kaust.personDalcin, Lisandro
dc.date.accepted2020-09-09
refterms.dateFOA2020-09-28T11:36:58Z


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