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dc.contributor.authorAbadía Heredia, R.
dc.contributor.authorLópez Martín, Manuel
dc.contributor.authorCarro Martínez, Belén 
dc.contributor.authorArribas Sánchez, Juan Ignacio 
dc.contributor.authorPérez, José Miguel
dc.contributor.authorLe Clainche, Soledad
dc.date.accessioned2022-07-20T11:29:38Z
dc.date.available2022-07-20T11:29:38Z
dc.date.issued2022
dc.identifier.citationExpert Systems with Applications, 2022, vol. 187, p. 115910es
dc.identifier.issn0957-4174es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/54123
dc.descriptionProducción Científicaes
dc.description.abstractSolving computational fluid dynamics problems requires using large computational resources. The computa- tional time and memory requirements to solve realistic problems vary from a few hours to several weeks with several processors working in parallel. Motivated by the need of reducing such large amount of resources (improving the industrial applications in which fluid dynamics plays a key role), this article introduces a new predictive Reduced Order Model (ROM) applied to solve fluid dynamics problems. The model is based on physical principles and combines modal decompositions with deep learning architectures. The hybrid ROM, reduces the dimensionality of a database via proper orthogonal decomposition (POD), extracting the dominant features leading the flow dynamics of the problem studied. The number of degrees of freedom are reduced from hundred thousands spatial points describing the database to a few (20–100) POD modes. Firstly, POD divides the spatio-temporal data into spatial modes and temporal coefficients (or temporal modes). Next, the temporal coefficients are integrated in time using convolutional or recurrent neural networks. The temporal evolution of the flow is approximated after combining the spatial modes with the new temporal coefficients computed. The model is tested in two complex problems of fluid dynamics, the three-dimensional wake of a circular cylinder and a synthetic jet. The hybrid ROM uses data from the initial transient stage of numerical simulations to predict the temporally converged solution of the flow with high accuracy. The speed-up factor comparing the time necessary to obtain the predicted solution using the hybrid ROM and the numerical solver is ∼140–348 in the synthetic jet and ∼2897–3818 in the three dimensional cylinder wake. The robustness shown in the results presented and the data-driven nature of this ROM, make it possible to extend its application to other fields (i.e. video and language processing, robotics, finances)es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.classificationReduced order modelses
dc.subject.classificationDeep learning architectureses
dc.subject.classificationPODes
dc.subject.classificationModal decompositionses
dc.subject.classificationNeural networkses
dc.subject.classificationFluid dynamicses
dc.titleA predictive hybrid reduced order model based on proper orthogonal decomposition combined with deep learning architectureses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2021 The Author(s)es
dc.identifier.doi10.1016/j.eswa.2021.115910es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0957417421012653es
dc.identifier.publicationfirstpage115910es
dc.identifier.publicationtitleExpert Systems with Applicationses
dc.identifier.publicationvolume187es
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia, Innovación y Universidades Proyectos de I+D+i ‘‘Retos investigación’’, (grant RTI2018-098958- B-I00)es
dc.description.projectMinisterio de Ciencia e Innovación y el Fondo Europeo de Desarrollo Regionales (FEDER) (grant PID2020-114173RB-I00)es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco33 Ciencias Tecnológicases
dc.subject.unesco12 Matemáticases


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