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Tracking Turbulent Coherent Structures by Means of Neural Networks

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Tracking Turbulent Coherent Structures by Means of Neural Networks

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dc.contributor.author Aguilar-Fuertes, José J. es_ES
dc.contributor.author Noguero-Rodríguez, Francisco es_ES
dc.contributor.author Jaen Ruiz, José C. es_ES
dc.contributor.author García-Raffi, L. M. es_ES
dc.contributor.author Hoyas, S. es_ES
dc.date.accessioned 2022-06-08T18:06:06Z
dc.date.available 2022-06-08T18:06:06Z
dc.date.issued 2021-02 es_ES
dc.identifier.uri http://hdl.handle.net/10251/183132
dc.description.abstract [EN] The behaviours of individual flow structures have become a relevant matter of study in turbulent flows as the computational power to allow their study feasible has become available. Especially, high instantaneous Reynolds Stress events have been found to dominate the behaviour of the logarithmic layer. In this work, we present a viability study where two machine learning solutions are proposed to reduce the computational cost of tracking such structures in large domains. The first one is a Multi-Layer Perceptron. The second one uses Long Short-Term Memory (LSTM). Both of the methods are developed with the objective of taking the the structures' geometrical features as inputs from which to predict the structures' geometrical features in future time steps. Some of the tested Multi-Layer Perceptron architectures proved to perform better and achieve higher accuracy than the LSTM architectures tested, providing lower errors on the predictions and achieving higher accuracy in relating the structures in the consecutive time steps. es_ES
dc.description.sponsorship This work was supported by RTI2018-102256-B-I00 of MINECO/FEDER. The computations of the new simulations were made possible by a generous grant of computing time from the Barcelona Supercomputing Centre, reference AECT-2020-2-0005. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Energies es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Turbulence es_ES
dc.subject Turbulent structures es_ES
dc.subject DNS es_ES
dc.subject Machine learning es_ES
dc.subject Neural networks es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.subject.classification INGENIERIA AEROESPACIAL es_ES
dc.title Tracking Turbulent Coherent Structures by Means of Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/en14040984 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-102256-B-I00/ES/TRANSFERENCIA DE CALOR EN FLUJOS DE PARED: CANALES Y CAPAS LIMITES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics es_ES
dc.description.bibliographicCitation Aguilar-Fuertes, JJ.; Noguero-Rodríguez, F.; Jaen Ruiz, JC.; García-Raffi, LM.; Hoyas, S. (2021). Tracking Turbulent Coherent Structures by Means of Neural Networks. Energies. 14(4):1-15. https://doi.org/10.3390/en14040984 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/en14040984 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 14 es_ES
dc.description.issue 4 es_ES
dc.identifier.eissn 1996-1073 es_ES
dc.relation.pasarela S\437886 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES


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