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dc.contributor.authorPaksoy, Akın
dc.contributor.authorAradağ, Selin
dc.date.accessioned2019-06-26T08:07:03Z
dc.date.available2019-06-26T08:07:03Z
dc.date.issued2015
dc.identifier.citationPaksoy, A., & Aradag, S. (2015). ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isi Bilimi ve Teknigi Dergisi/Journal of Thermal Science & Technology, 35(2).en_US
dc.identifier.issn1300-3615
dc.identifier.urihttp://hdl.handle.net/20.500.11851/1515
dc.description.abstractIn this study, computational fluid dynamics (CFD) analyses of the two-dimensional, time-dependent lid-driven cavity flows, for Reynolds numbers ranging from 100 to 10000, are performed by using an in-house developed CFD code. The unsteady behavior of the flow is triggered using a sinusoidal lid velocity profile. The flow structure is further investigated with the application of a reduced order modeling technique, Proper Orthogonal Decomposition (POD), and the structures present in the flow, are separated according to their frequency (energy) content. POD results show that when the stream function formation is used as a data ensemble, about 99% of the total energy content can be modeled by considering only the most energetic first four POD modes; whereas, this value remains at a range between 90 - 95% for the x-direction velocity data ensemble. What is more, an Artificial Neural Network (ANN) based approach is developed to predict mode amplitudes for flows with different Reynolds numbers. Once enough information is obtained with the help of CFD of few flow cases, the ANN integrated approach presented herein helps to predict what is happening in the flow for different flow cases without requiring further CFD simulations, which are not practical in real-time flow control applications.en_US
dc.description.sponsorshipThis research is financially supported by Turkish Academy of Sciences Distinguished Young Scientists Awards Programme. (TUBA-GEBIP).
dc.language.isoengen_US
dc.publisherTurkish Soc Thermal Sciences Technologyen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputational Fluid Dynamicsen_US
dc.subjectTime-dependent behavioren_US
dc.subjectCavity flowen_US
dc.subjectProper Orthogonal Decompositionen_US
dc.subjectFlow controlen_US
dc.subjectArtificial Neural Networksen_US
dc.titleArtificial Neural Network Based Prediction Of Time-Dependent Behavior For Lid-Driven Cavity Flowsen_US
dc.typearticleen_US
dc.relation.journalJournal Of Thermal Science And Technologyen_US
dc.relation.journalIsı Bilimi ve Tekniği Dergisitr_TR
dc.contributor.departmentTOBB ETU, Faculty of Engineering, Department of Mechanical Engineeringen_US
dc.contributor.departmentTOBB ETÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümütr_TR
dc.identifier.volume35
dc.identifier.issue2
dc.identifier.startpage1
dc.identifier.endpage18
dc.contributor.orcidhttp://orcid.org/0000-0002-2034-0008
dc.identifier.wosWOS:000367758900001
dc.identifier.scopus2-s2.0-84963568218
dc.contributor.tobbetuauthorAradağ, Selin
dc.contributor.wosresearcherIDI-8876-2012
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıtr_TR
dc.identifier.TRDizinhttp://www.trdizin.gov.tr/publication/paper/detail/TVRneE1qRTNOdz09


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