A Data-Driven Approach for Generating Vortex-Shedding Regime Maps for an Oscillating Cylinder

Matthew Cann, Ryley McConkey, Fue-Sang Lien (), William Melek and Eugene Yee
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Matthew Cann: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Ryley McConkey: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Fue-Sang Lien: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
William Melek: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
Eugene Yee: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Energies, 2023, vol. 16, issue 11, 1-30

Abstract: This study presents a data-driven approach for generating vortex-shedding maps, which are vital for predicting flow structures in vortex-induced vibration (VIV) wind energy extraction devices, while addressing the computational and complexity limitations of traditional methods. The approach employs unsupervised clustering techniques on subsequences extracted using the matrix profile method from local flow measurements in the wake of an oscillating circular cylinder generated from 2-dimensional computational fluid dynamics simulations of VIV. The proposed clustering methods were validated by reproducing a benchmark map produced at a low Reynolds number (Re = 4000) and then extended to a higher Reynolds number (Re = 10,000) to gain insights into the complex flow regimes. The multi-step clustering methods used density-based and k -Means clustering for the pre-clustering stage and agglomerative clustering using dynamic time warping (DTW) as the similarity measure for final clustering. The clustering methods achieved exceptional performance at high-Reynolds-number flow, with scores in the silhouette index (0.4822 and 0.4694) and Dunn index (0.3156 and 0.2858) demonstrating the accuracy and versatility of the hybrid clustering methods. This data-driven approach enables the generation of more accurate and feasible maps for vortex-shedding applications, which could improve the design and optimization of VIV wind energy harvesting systems.

Keywords: vortex shedding; machine learning; unsupervised clustering; time series clustering; vortex-induced vibrations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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