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Dynamic Mode Decomposition of Random Pressure Fields over Bluff Bodies

Journal Article · · Journal of Engineering Mechanics
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. Here, 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.
Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0012704
OSTI ID:
1835112
Report Number(s):
BNL--222382-2021-JAAM
Journal Information:
Journal of Engineering Mechanics, Journal Name: Journal of Engineering Mechanics Journal Issue: 4 Vol. 147; ISSN 0733-9399
Publisher:
American Society of Civil Engineers (ASCE)Copyright Statement
Country of Publication:
United States
Language:
English

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