Sensing the turbulent large-scale motions with their wall signature

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Show simple item record Güemes Jiménez, Alejandro Discetti, Stefano Ianiro, Andrea 2020-12-15T09:19:08Z 2020-12-15T09:19:08Z 2019-12-01
dc.identifier.bibliographicCitation Güemesa, A., Discetti, S. y Ianiro, A. (2019). Sensing the turbulent large-scale motions with their wall signature. Physics of Fluids, 31(12), 125112.
dc.identifier.issn 1070-6631
dc.description.abstract This study assesses the capability of extended proper orthogonal decomposition (EPOD) and convolutional neural networks (CNNs) to reconstruct large-scale and very-large-scale motions (LSMs and VLSMs respectively) employing wall-shear-stress measurements in wall-bounded turbulent flows. Both techniques are used to reconstruct the instantaneous LSM evolution in the flow field as a combination of proper orthogonal decomposition (POD) modes, employing a limited set of instantaneous wall-shear-stress measurements. Due to the dominance of nonlinear effects, only CNNs provide satisfying results. Being able to account for nonlinearities in the flow, CNNs are shown to perform significantly better than EPOD in terms of both instantaneous flow-field estimation and turbulent-statistics reconstruction. CNNs are able to provide a more effective reconstruction performance employing more POD modes at larger distances from the wall and employing lower wall-measurement resolutions. Furthermore, the capability of tackling nonlinear features of CNNs results in estimation capabilities that are weakly dependent on the distance from the wall.
dc.description.sponsorship This work has been partially supported by Grant No. DPI2016-79401-R funded by the Spanish State Research Agency (SRA) and the European Regional Development Fund (ERDF). A.G. acknowledges Dr. A. Sánchez for insightful discussions about CNN architecture. The authors acknowledge Dr. R. Vinuesa for insightful comments and discussions.
dc.format.extent 20
dc.language.iso eng
dc.publisher AIP Publishing
dc.rights © 2019 AIP Publishing.
dc.title Sensing the turbulent large-scale motions with their wall signature
dc.type article
dc.subject.eciencia Aeronáutica
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. DPI2016-79401-R
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 125112
dc.identifier.publicationissue 12
dc.identifier.publicationtitle Physics of Fluids
dc.identifier.publicationvolume 31
dc.identifier.uxxi AR/0000024402
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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