Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4865
Title: Machine Learning Approach for Flow Fields Over a Circular Cylinder Based on Particle Image Velocimetry Measurements
Authors: Aksoy, M.H.
Goktepeli, I.
Ispir, M.
Cakan, A.
Keywords: ANN
Bluff body
Circular cylinder
Machine learning
PIV
Circular cylinders
Drag coefficient
Flow separation
Flow visualization
Machine learning
Reynolds number
Velocimeters
Velocity measurement
Artificial neural network methods
Bluff body
Image velocimetry
Machine learning approaches
Machine-learning
Particle image velocimetry
Particle image velocimetry measurement
Particle images
Streamwise velocity
Velocity components
Neural networks
Issue Date: 2023
Publisher: Elsevier B.V.
Abstract: Flow around a circular cylinder has been experimentally studied at Reynolds numbers of Re = 4 × 103 and Re = 8 × 103 using Particle Image Velocimetry (PIV). The Artificial Neural Network (ANN) method, a subset of machine learning, has been implemented to capture complex flow patterns and relationships within data to estimate the experimental results. Experimental and estimated results regarding instantaneous contour graphics for streamwise (u) and cross-stream (v) velocity components with velocity vector fields have been compared. Furthermore, drag coefficient (CD) and Strouhal number (St) have been validated. In flow features, instantaneous streamwise velocity components indicated a lower pressure zone in the wake region of the cylinder owing to flow separation. The ANN method has predicted the positions of the minimum velocity clusters with 3.2 % for Re = 8 × 103 as a mean relative error compared to the experimental results. Moreover, the average relative difference of 10.82 % between the experimental and the estimated results for the clusters with the minimum streamwise velocity components has been attained at Re = 4 × 103 and Re = 8 × 103. Regarding the cross-stream velocity components, the mean relative difference values have been observed for the clusters with maximum values of 6.83 % and the clusters with minimum values of 3.66 %. Drag coefficients have also been obtained, and these values are very close as CD = 1.090 and CD = 1.084 for PIV and ANN methods at Re = 8 × 103, respectively. Furthermore, St values of the circular cylinder are St = 0.216 at Re = 4 × 103 and St = 0.211 at Re = 8 × 103, which agree with the results of the literature. © 2023 Elsevier Ltd
URI: https://doi.org/10.1016/j.measurement.2023.113699
https://hdl.handle.net/20.500.13091/4865
ISSN: 0263-2241
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Show full item record



CORE Recommender

SCOPUSTM   
Citations

2
checked on Mar 2, 2024

WEB OF SCIENCETM
Citations

1
checked on Mar 2, 2024

Page view(s)

34
checked on Mar 4, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.