Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/4864
Title: | Flow control over a circular cylinder using vortex generators: Particle image velocimetry analysis and machine-learning-based prediction of flow characteristics | Authors: | Okbaz, A. Aksoy, M.H. Kurtulmuş, N. Çolak, A.B. |
Keywords: | Bluff body Flow control Machine learning Particle image velocimetry Turbulence Vortex generators Circular cylinders Data acquisition Data visualization Drag coefficient Flow control Flow structure Flow visualization Marine applications Neural networks Offshore oil well production Tall buildings Velocity measurement Wakes Bluff body Flow around circular cylinder Flow characteristic Image velocimetry Machine-learning Particle image velocimetry Particle image velocimetry analysis Particle images Vortex formation Vortex generators Machine learning artificial neural network cylinder data acquisition drag coefficient flow control fluid dynamics machine learning offshore engineering particle image velocimetry prediction quantitative analysis turbulence vortex flow |
Publisher: | Elsevier Ltd | Abstract: | Controlling 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 Ltd | URI: | https://doi.org/10.1016/j.oceaneng.2023.116055 https://hdl.handle.net/20.500.13091/4864 |
ISSN: | 0029-8018 |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections |
Show full item record
CORE Recommender
SCOPUSTM
Citations
2
checked on Sep 21, 2024
Page view(s)
36
checked on Sep 23, 2024
Google ScholarTM
Check
Altmetric
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.