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dc.contributor.authorIserte, Sergio
dc.contributor.authorMacias, Aina
dc.contributor.authorMartínez-Cuenca, Raúl
dc.contributor.authorchiva, sergio
dc.contributor.authorParedes, Roberto
dc.contributor.authorQuintana-Orti, Enrique S.
dc.date.accessioned2022-06-22T12:39:00Z
dc.date.available2022-06-22T12:39:00Z
dc.date.issued2022-06-15
dc.identifier.citationIserte, S., Macías, A., Martínez-Cuenca, R., Chiva, S., Paredes, R., & Quintana-Ortí, E. S. (2022). Accelerating urban scale simulations leveraging local spatial 3D structure. Journal of Computational Science, 101741.ca_CA
dc.identifier.issn1877-7503
dc.identifier.urihttp://hdl.handle.net/10234/198102
dc.description.abstractThis paper presents a hybrid methodology for accelerating Computational Fluid Dynamics (CFD) simulations intertwining inferences from deep neural networks (DNN). The strategy leverages the local spatial data of the velocity field to leverage three-dimensional convolutional kernels within DNN. The hybrid workflow is composed of two-step cycles where CFD solvers calculations are utilized to feed predictive models, whose inferences, in turn, accelerate the simulation of the fluid evolution compared with traditional CFD. This approach has proved to reduce 30% time-to-solution in an urban scale study case, which leads to generating massive datasets at a fraction of the cost.ca_CA
dc.description.sponsorShipFunding for open access charge: CRUE-Universitat Jaume I
dc.format.extent11 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.relation.isPartOfJournal of Computational Science, 62 (2022) 101741ca_CA
dc.rights1877-7503/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY licenseca_CA
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/ca_CA
dc.subjectcomputational fluid dynamicsca_CA
dc.subjectstructured meshca_CA
dc.subjectconvolutional neural networkca_CA
dc.subjecthigh performance computingca_CA
dc.subjectdata-driven simulationsca_CA
dc.titleAccelerating urban scale simulations leveraging local spatial 3D structureca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.jocs.2022.101741
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA
project.funder.nameGeneralitat Valencianaca_CA
oaire.awardNumberAPOSTD/2020/026ca_CA


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1877-7503/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license
Excepto si se señala otra cosa, la licencia del ítem se describe como: 1877-7503/© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license