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Machine Learning Based Developing Flow Control Technique Over Circular Cylinders

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dc.contributor.author Aylı, Ece
dc.contributor.author Koçak, Eyup
dc.contributor.author Türkoğlu, Haşmet
dc.date.accessioned 2024-01-03T13:27:32Z
dc.date.available 2024-01-03T13:27:32Z
dc.date.issued 2023-04
dc.identifier.citation Aylı, E.; Koçak, E.; Türkoğlu, H. (2023). "Machine Learning Based Developing Flow Control Technique Over Circular Cylinders", Journal of Computing and Information Science in Engineering, Vol.23, No.2. tr_TR
dc.identifier.issn 15309827
dc.identifier.uri http://hdl.handle.net/20.500.12416/6844
dc.description.abstract This paper demonstrates the feasibility of blowing and suction for flow control based on the computational fluid dynamics (CFD) simulations at a low Reynolds number flows. The effects of blowing and suction position, and the blowing and suction mass flowrate, and on the flow control are presented in this paper. The optimal conditions for suppressing the wake of the cylinder are investigated by examining the flow separation and the near wake region; analyzing the aerodynamic force (lift and drag) fluctuations using the fast Fourier transform (FFT) to separate the effects of small-scale turbulent structures in the wake region. A method for stochastic analysis using machine learning techniques is proposed. Three different novel machine learning methods were applied to CFD results to predict the variation in drag coefficient due to the vortex shedding. Although, the prediction power of all the methods utilized is in the acceptable accuracy range, the Gaussian process regression (GPR) method is more accurate with an R2(coefficient of determination) > 0.95. The results indicate that by optimizing the blowing and suction parameters like mass flowrate, slot location, and the slot configuration, up to 20% reduction can be achieved in the drag coefficient. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1115/1.4054689 tr_TR
dc.rights info:eu-repo/semantics/closedAccess tr_TR
dc.subject Active Control tr_TR
dc.subject ANN tr_TR
dc.subject Computational Foundations For Engineering Optimization tr_TR
dc.subject Cylinder tr_TR
dc.subject GPR tr_TR
dc.subject Machine Learning For Engineering Applications tr_TR
dc.subject SVM tr_TR
dc.subject Wake tr_TR
dc.title Machine Learning Based Developing Flow Control Technique Over Circular Cylinders tr_TR
dc.type article tr_TR
dc.relation.journal Journal of Computing and Information Science in Engineering tr_TR
dc.contributor.authorID 265836 tr_TR
dc.contributor.authorID 283455 tr_TR
dc.contributor.authorID 12941 tr_TR
dc.identifier.volume 23 tr_TR
dc.identifier.issue 2 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Makine Mühendisliği Bölümü tr_TR


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