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dc.contributor.authorRanocha, Hendrik
dc.contributor.authorDalcin, Lisandro
dc.contributor.authorParsani, Matteo
dc.contributor.authorKetcheson, David I.
dc.date.accessioned2022-01-19T07:51:59Z
dc.date.available2021-04-19T09:08:00Z
dc.date.available2022-01-19T07:51:59Z
dc.date.issued2021-11-22
dc.date.submitted2021-04-14
dc.identifier.citationRanocha, H., Dalcin, L., Parsani, M., & Ketcheson, D. I. (2021). Optimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics. Communications on Applied Mathematics and Computation. doi:10.1007/s42967-021-00159-w
dc.identifier.issn2096-6385
dc.identifier.issn2661-8893
dc.identifier.doi10.1007/s42967-021-00159-w
dc.identifier.urihttp://hdl.handle.net/10754/668844
dc.description.abstractAbstractWe develop error-control based time integration algorithms for compressible fluid dynamics (CFD) applications and show that they are efficient and robust in both the accuracy-limited and stability-limited regime. Focusing on discontinuous spectral element semidiscretizations, we design new controllers for existing methods and for some new embedded Runge-Kutta pairs. We demonstrate the importance of choosing adequate controller parameters and provide a means to obtain these in practice. We compare a wide range of error-control-based methods, along with the common approach in which step size control is based on the Courant-Friedrichs-Lewy (CFL) number. The optimized methods give improved performance and naturally adopt a step size close to the maximum stable CFL number at loose tolerances, while additionally providing control of the temporal error at tighter tolerances. The numerical examples include challenging industrial CFD applications.
dc.description.sponsorshipResearch reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). We are thankful for the computing resources of the Supercomputing Laboratory and the Extreme Computing Research Center at KAUST. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2044-390685587, Mathematics Münster: Dynamics-Geometry-Structure. Special thanks are extended to the McLaren F1 racing Team for providing data, CAD geometries, and setup of the Imperial Front Wing test c
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://link.springer.com/10.1007/s42967-021-00159-w
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleOptimized Runge-Kutta Methods with Automatic Step Size Control for Compressible Computational Fluid Dynamics
dc.typeArticle
dc.contributor.departmentExtreme Computing Research Center
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.identifier.journalCommunications on Applied Mathematics and Computation
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionApplied Mathematics Münster, University of Münster, Münster, Germany.
dc.identifier.arxivid2104.06836
kaust.personDalcin, Lisandro
kaust.personParsani, Matteo
kaust.personKetcheson, David I.
dc.date.accepted2021-07-23
refterms.dateFOA2021-04-19T09:08:41Z
kaust.acknowledged.supportUnitExtreme Computing Research Center at KAUST
kaust.acknowledged.supportUnitSupercomputing Laboratory


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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