Adaptively Tuned High-Order Methods for iLES

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UDC.coleccionInvestigación
UDC.conferenceTitleEuropean Congress on Computational Methods in Applied Sciences and Engineering, ECCOMAS 2024
UDC.departamentoMatemáticas
UDC.endPage12
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)
UDC.institutoCentroCITEEC - Centro de Innovación Tecnolóxica en Edificación e Enxeñaría Civil
UDC.startPage1
dc.contributor.authorTsoutsanis, Panagiotis
dc.contributor.authorFu, Lin
dc.contributor.authorNogueira, Xesús
dc.date.accessioned2026-07-13T17:40:17Z
dc.date.available2026-07-13T17:40:17Z
dc.date.issued2024
dc.descriptionPaper presented at the 9th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS 2024), 3-7 June 2024, Lisboa, Portugal.
dc.description.abstract[Abstract]: Implicit Large Eddy Simulation (iLES) is popular for modeling high-Reynolds-number turbulent flows due to its simplicity and efficiency. High-resolution numerical schemes in iLES mimic subgrid-scale models by using numerical dissipation/dispersion errors. Balancing these errors is crucial to avoid unphysical energy build-up or excessive diffusion, especially in compressible flows with discontinuities and smooth features. This work introduces an adaptive dissipation/dispersion adjustment (ADDA) algorithm for the CWENOZ scheme in a finite- volume framework for unstructured meshes, tested in subsonic, transonic, and supersonic regimes. The ADDA algorithm enhances robustness and scale-resolving capabilities, yielding efficient and physically meaningful results, and is available in the open-source UCNS3D CFD solver.
dc.description.sponsorshipP.T. acknowledges the computing time on ARCHER2 through UK Turbulence Consortium by the EPSRC grant [EP/X035484/1] and the support by the EPSRC grant [EP/W037092/1]. L.F. acknowledges the fund from the Research Grants Council (RGC) of the Government of Hong Kong Special Administrative Region (HKSAR) with RGC/ECS Project (No. 26200222), RGC/GRF Project (No. 16201023) and RGC/STG Project (No. STG2/E-605/23-N), the fund from Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515011798), and the fund from Guangdong Province Science and Technology Plan Project (No. 2023A0505030005). X.N. acknowledges the support provided by the [Grant PID2021-125447OB-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by “ERDF A way of making Europe”, the funds by [Grant TED2021-129805B-I00] funded by MCIN/AEI/ 10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR” and the funding provided by the Xunta de Galicia [Grant #ED431C 2022/06].
dc.description.sponsorshipXunta de Galicia; ED431C 2022/06
dc.description.sponsorshipUnited Kingdom. Engineering and Physical Sciences Research Council; EP/X035484/1
dc.description.sponsorshipUnited Kingdom. Engineering and Physical Sciences Research Council; EP/W037092/1
dc.description.sponsorshipChina-Hong Kong Special Administrative Region. Research Grants Council; 26200222
dc.description.sponsorshipChina-Hong Kong Special Administrative Region. Research Grants Council; 16201023
dc.description.sponsorshipChina-Hong Kong Special Administrative Region. Research Grants Council; STG2/E-605/23-N
dc.description.sponsorshipChina. Guangdong Basic and Applied Basic Research Foundation; 2024A1515011798
dc.description.sponsorshipChina. Guangdong Province Science and Technology Plan; 2023A0505030005
dc.identifier.citationP. Tsoutsanis, X. NOGUEIRA and L. FU, Adaptively Tuned High-Order Methods for iLES, in: Advances in Turbulence Modeling using Nonlocal Derivatives, Implicit LES and Deep Learning, 2024. ECCOMAS 2024. URL https://www.scipedia.com/public/Tsoutsanis_et_al_2024a. DOI: 10.23967/eccomas.2024.040
dc.identifier.doi10.23967/eccomas.2024.040
dc.identifier.issn2696-6999
dc.identifier.urihttps://hdl.handle.net/2183/48868
dc.language.isoeng
dc.publisherScipedia S.L.
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129805B-I00/ES/NUEVOS METODOS PARA EL DISEÑO OPTIMO DE TURBINAS DE CORRIENTES MARINAS
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-125447OB-I00/ES/MODELOS NUMERICOS DE ALTA PRECISION PARA EL DESARROLLO DE UNA NUEVA GENERACION DE PARQUES OFFSHORE DE ENERGIA RENOVABLE
dc.relation.urihttps://doi.org/10.23967/eccomas.2024.040
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 Internationalen
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subjectCompressible flow
dc.subjectCWENO schemes
dc.subjectFinite-Volume method
dc.subjectTurbulence
dc.subjectCFD
dc.subjectiLES
dc.subjectHigh-Order Finite-Volume
dc.subjectCWENOZ
dc.titleAdaptively Tuned High-Order Methods for iLES
dc.typeconference output
dspace.entity.typePublication
relation.isAuthorOfPublication8063e598-1ae3-462e-8840-785c4333adfa
relation.isAuthorOfPublication.latestForDiscovery8063e598-1ae3-462e-8840-785c4333adfa

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