Abstract
Fluctuating surface pressures on a bluff body exposed to a boundary layer flow generally are characterized as a spatiotemporally varying random field. In this paper, a dynamic mode decomposition (DMD) was applied to extract dominant features embedded in these random pressure fields. Utilizing an unsupervised machine learning algorithm, spatial modes and their temporal variations were grouped into different clusters at scales, e.g., macro, meso, and micro. A proper orthogonal decomposition (POD) of the experimental data was carried out to observe commonalities and distinctive perspectives each decomposition offers. A comprehensive examination of the DMD/POD for their convergence criteria, data sufficiency, and modal components analysis was conducted. The physical interpretation of the spatiotemporal pressure field based on these decomposition schemes was discussed. At different scales, the DMD modes can capture the evolution of aerodynamic features, e.g., convection of vortices (or vortex tubes) and other structures. The distribution of energy among these three broad scales also reflects an energy cascade in pressure fluctuations akin to turbulence.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online (https://xihaier.github.io/) in accordance with funder data retention policies.
Acknowledgments
This work was supported in part by the National Science Foundation (NSF) under Grant No. 1562244. This support is gratefully acknowledged. The authors also gratefully acknowledge the use of the Tokyo Polytechnic University aerodynamic database.
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Published In
Journal of Engineering Mechanics
Volume 147 • Issue 4 • April 2021
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© 2021 American Society of Civil Engineers.
History
Received: Jul 12, 2020
Accepted: Nov 11, 2020
Published online: Jan 20, 2021
Published in print: Apr 1, 2021
Discussion open until: Jun 20, 2021
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