Flow control over a circular cylinder using vortex generators: Particle image velocimetry analysis and machine-learning-based prediction of flow characteristics

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dc.authoridColak, Andac Batur/0000-0001-9297-8134
dc.authoridOKBAZ, ABDULKERIM/0000-0002-8866-6047
dc.authoridAksoy, Muharrem Hilmi/0000-0002-6509-8112
dc.contributor.authorOkbaz, Abdulkerim
dc.contributor.authorAksoy, Muharrem Hilmi
dc.contributor.authorKurtulmus, Nazim
dc.contributor.authorColak, Andac Batur
dc.date.accessioned2025-01-06T17:36:42Z
dc.date.available2025-01-06T17:36:42Z
dc.date.issued2023
dc.description.abstractControlling the flow around circular cylinders is crucial to mitigate vortex-induced vibrations and prevent structural damage in a range of applications, such as marine and offshore engineering, tall buildings, long-span bridges, transport ships, and heat exchangers. In this study, we aimed to control the turbulent flow structure around a circular cylinder by placing vortex generators (VGs). We examined the flow structure using particle image velocimetry (PIV). This enabled quantitative data acquisition, intuitive flow visualization, and drag coefficient determination from PIV data. We developed artificial neural network (ANN) models that successfully predict both mean and instantaneous flow characteristics for different scenarios. Our findings show that using VGs elongated the wake and increased vortex formation lengths while reducing velocity fluctuations and the drag coefficient. A minimum drag coefficient of 0.718 was achieved with VGs oriented at alpha = 60 degrees & beta = 60 degrees, reducing the drag by 35.3% compared to the bare cylinder. The drag coefficient exhibited a substantial inverse correlation with both wake and vortex formation lengths. This study is significant for controlling flow structures, providing detailed insights into the near-wake region, and highlighting the potential applications of machine learning in fluid dynamics.
dc.description.sponsorshipScientific and Technological Research Council of Turkiye [1059B192201266]
dc.description.sponsorshipAbdulkerim Okbaz expresses gratitude to the TUBITAK-2219 International Postdoctoral Research Fellowship Program, supported by The Scientific and Technological Research Council of Turkiye, for funding a segment of this study carried out at the Georgia Institute of Technology (2022, Grant# 1059B192201266).
dc.identifier.doi10.1016/j.oceaneng.2023.116055
dc.identifier.issn0029-8018
dc.identifier.issn1873-5258
dc.identifier.scopus2-s2.0-85175001254
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.oceaneng.2023.116055
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1971
dc.identifier.volume288
dc.identifier.wosWOS:001106997800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherPergamon-Elsevier Science Ltd
dc.relation.ispartofOcean Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectBluff body
dc.subjectFlow control
dc.subjectTurbulence
dc.subjectMachine learning
dc.subjectParticle image velocimetry
dc.subjectVortex generators
dc.titleFlow control over a circular cylinder using vortex generators: Particle image velocimetry analysis and machine-learning-based prediction of flow characteristics
dc.typeArticle

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