Okbaz, A.Aksoy, M.H.Kurtulmuş, N.Çolak, A.B.2023-12-092023-12-0920230029-8018https://doi.org/10.1016/j.oceaneng.2023.116055https://hdl.handle.net/20.500.13091/4864Controlling 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 α = 60° & β = 60°, 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. © 2023 Elsevier Ltdeninfo:eu-repo/semantics/closedAccessBluff bodyFlow controlMachine learningParticle image velocimetryTurbulenceVortex generatorsCircular cylindersData acquisitionData visualizationDrag coefficientFlow controlFlow structureFlow visualizationMarine applicationsNeural networksOffshore oil well productionTall buildingsVelocity measurementWakesBluff bodyFlow around circular cylinderFlow characteristicImage velocimetryMachine-learningParticle image velocimetryParticle image velocimetry analysisParticle imagesVortex formationVortex generatorsMachine learningartificial neural networkcylinderdata acquisitiondrag coefficientflow controlfluid dynamicsmachine learningoffshore engineeringparticle image velocimetrypredictionquantitative analysisturbulencevortex flowArticle10.1016/j.oceaneng.2023.1160552-s2.0-85175001254