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dc.contributor.author | Yousif, Mustafa Z. | es_ES |
dc.contributor.author | Yu, Linqui | es_ES |
dc.contributor.author | Hoyas, S. | es_ES |
dc.contributor.author | Vinuesa, Ricardo | es_ES |
dc.contributor.author | Lim, Hee-Chang | es_ES |
dc.date.accessioned | 2024-10-16T18:02:40Z | |
dc.date.available | 2024-10-16T18:02:40Z | |
dc.date.issued | 2023-02-13 | es_ES |
dc.identifier.issn | 2045-2322 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/210402 | |
dc.description.abstract | [EN] Turbulence is a complex phenomenon that has a chaotic nature with multiple spatio-temporal scales, making predictions of turbulent flows a challenging topic. Nowadays, an abundance of high-fidelity databases can be generated by experimental measurements and numerical simulations, but obtaining such accurate data in full-scale applications is currently not possible. This motivates utilising deep learning on subsets of the available data to reduce the required cost of reconstructing the full flow in such full-scale applications. Here, we develop a generative-adversarial-network (GAN)-based model to reconstruct the three-dimensional velocity fields from flow data represented by a cross-plane of unpaired two-dimensional velocity observations. The model could successfully reconstruct the flow fields with accurate flow structures, statistics and spectra. The results indicate that our model can be successfully utilised for reconstructing three-dimensional flows from two-dimensional experimental measurements. Consequently, a remarkable reduction in the complexity of the experimental setup and the storage cost can be achieved. | es_ES |
dc.description.sponsorship | This work was supported by Human Resources Program in Energy Technology' of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (no. 20214000000140). In addition, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2019R1I1A3A01058576). This work was also supported by the National Supercomputing Center with supercomputing resources including technical support (KSC-2021-CRE-0244). R.V. acknowledges the financial support from the ERC Grant No. "2021-CoG-101043998, DEEPCONTROL". SH was funded by Contract No. PID2021-128676OB-I00 of Ministerio de Ciencia, innovacion y Universidades/ FEDER. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Nature Publishing Group | es_ES |
dc.relation.ispartof | Scientific Reports | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Turbulence | es_ES |
dc.subject | Deep learning | es_ES |
dc.subject | Generative Adversarial Networks (GANs) | es_ES |
dc.subject | Velocity fields | es_ES |
dc.subject.classification | INGENIERIA AEROESPACIAL | es_ES |
dc.title | A deep Learning approach for reconstructing 3D turbulent flows from 2D observation data | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1038/s41598-023-29525-9 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128676OB-I00/ES/REVELANDO LA TURBULENCIA DE PARED/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/HE/101043998/EU/Discovering novel control strategies for turbulent wings through deep reinforcement learning/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/KETEP//20214000000140/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/NRF//2019R1I1A3A01058576/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Yousif, MZ.; Yu, L.; Hoyas, S.; Vinuesa, R.; Lim, H. (2023). A deep Learning approach for reconstructing 3D turbulent flows from 2D observation data. Scientific Reports. 13(1). https://doi.org/10.1038/s41598-023-29525-9 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1038/s41598-023-29525-9 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 13 | es_ES |
dc.description.issue | 1 | es_ES |
dc.identifier.pmid | 36781944 | es_ES |
dc.identifier.pmcid | PMC9925827 | es_ES |
dc.relation.pasarela | S\506422 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | National Research Foundation of Korea | es_ES |
dc.contributor.funder | Korea Institute of Energy Technology Evaluation and Planning | es_ES |