Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94010
Title: | Bluff body uses deep-reinforcement-learning trained active flow control to achieve hydrodynamic stealth | Authors: | Ren, F Wang, C Tang, H |
Issue Date: | Sep-2021 | Source: | Physics of fluids, Sept 2021, v. 33, no. 9, 093602 | Abstract: | We propose a novel active-flow-control strategy for bluff bodies to hide their hydrodynamic traces, i.e., strong shears and periodically shed vortices, from predators. A group of windward-suction-leeward-blowing (WSLB) actuators are adopted to control the wake of a circular cylinder submerged in a uniform flow. An array of velocity sensors is deployed in the near wake to provide feedback signals. Through the data-driven deep reinforcement learning, effective control strategies are trained for the WSLB actuation to mitigate the cylinder's hydrodynamic signatures. Only a 0.29% deficit in streamwise velocity is detected, which is a 99.5% reduction from the uncontrolled value. The same control strategy is found also to be effective when the cylinder undergoes transverse vortex-induced vibration. The findings from this study can shed some light on the design and operation of underwater structures and robotics to achieve hydrodynamic stealth. | Publisher: | American Institute of Physics | Journal: | Physics of fluids | ISSN: | 1070-6631 | EISSN: | 1089-7666 | DOI: | 10.1063/5.0060690 | Rights: | © 2021 Author(s). This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Feng Ren (任峰), Chenglei Wang (王成磊), and Hui Tang (唐辉), "Bluff body uses deep-reinforcement-learning trained active flow control to achieve hydrodynamic stealth", Physics of Fluids 33, 093602 (2021) and may be found at https://doi.org/10.1063/5.0060690. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
PoF-renfeng-stealth.pdf | Pre-Published version | 2.95 MB | Adobe PDF | View/Open |
Page views
45
Last Week
1
1
Last month
Citations as of May 5, 2024
Downloads
97
Citations as of May 5, 2024
SCOPUSTM
Citations
30
Citations as of May 3, 2024
WEB OF SCIENCETM
Citations
23
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.