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On the use of deep learning and computational fluid dynamics for the estimation of uniform momentum source components of propellers
dc.contributor.author | Martinez Cuenca, Raul | |
dc.contributor.author | Luis Gómez, Jaume | |
dc.contributor.author | Iserte, Sergio | |
dc.contributor.author | chiva, sergio | |
dc.date.accessioned | 2023-12-21T16:00:40Z | |
dc.date.available | 2023-12-21T16:00:40Z | |
dc.date.issued | 2023-12 | |
dc.identifier.citation | Raúl Martínez-Cuenca, Jaume Luis-Gómez, Sergio Iserte, Sergio Chiva, On the use of deep learning and computational fluid dynamics for the estimation of uniform momentum source components of propellers, iScience, Volume 26, Issue 12, 2023, 108297, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2023.108297. (https://www.sciencedirect.com/science/article/pii/S258900422302374X) | ca_CA |
dc.identifier.issn | 2589-0042 | |
dc.identifier.uri | http://hdl.handle.net/10234/205264 | |
dc.description.abstract | This article proposes a novel method based on Deep Learning for the resolution of uniform momentum source terms in the Reynolds-Averaged Navier-Stokes equations. These source terms can represent several industrial devices (propellers, wind turbines, and so forth) in Computational Fluid Dynamics simulations. Current simulation methods require huge computational power, rely on strong assumptions or need additional information about the device that is being simulated. In this first approach to the new method, a Deep Learning system is trained with hundreds of Computational Fluid Dynamics simulations with uniform momemtum sources so that it can compute the one representing a given propeller from a reduced set of flow velocity measurements near it. Results show an overall relative error below the 5% for momentum sources for uniform sources and a moderate error when describing real propellers. This work will allow to simulate more accurately industrial devices with less computational cost. | ca_CA |
dc.format.extent | 15 p. | ca_CA |
dc.format.mimetype | application/pdf | ca_CA |
dc.language.iso | eng | ca_CA |
dc.publisher | Cell Press | ca_CA |
dc.relation.isPartOf | iScience, vol. 26, núm. 12, (2023) | ca_CA |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | ca_CA |
dc.subject | Artificial intelligence | ca_CA |
dc.subject | Industrial engineering | ca_CA |
dc.title | On the use of deep learning and computational fluid dynamics for the estimation of uniform momentum source components of propellers | ca_CA |
dc.type | info:eu-repo/semantics/article | ca_CA |
dc.identifier.doi | 10.1016/j.isci.2023.108297 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | ca_CA |
dc.relation.publisherVersion | https://www.sciencedirect.com/science/article/pii/S258900422302374X | ca_CA |
dc.type.version | info:eu-repo/semantics/publishedVersion | ca_CA |
project.funder.name | Universitat Jaume I | ca_CA |
project.funder.name | Agencia Estatal de Investigación | ca_CA |
project.funder.name | Generalitat Valenciana | ca_CA |
project.funder.name | European Social Funds | ca_CA |
project.funder.name | Ministerio de Universidades | ca_CA |
oaire.awardNumber | UJI-B2021-70 | ca_CA |
oaire.awardNumber | PID2021-128405OB-I00 | ca_CA |
oaire.awardNumber | APOSTD/2020/026 | ca_CA |
oaire.awardNumber | FPU21/03740 | ca_CA |
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