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http://hdl.handle.net/10397/89897
Title: | Active flow control using machine learning : a brief review | Authors: | Ren, F Hu, HB Tang, H |
Issue Date: | Apr-2020 | Source: | Journal of hydrodynamics, Ser.B, Apr. 2020, v. 32, no. 2, p. 247-253 | Abstract: | Nowadays the rapidly developing artificial intelligence has become a key solution for problems of diverse disciplines, especially those involving big data. Successes in these areas also attract researchers from the community of fluid mechanics, especially in the field of active flow control (AFC). This article surveys recent successful applications of machine learning in AFC, highlights general ideas, and aims at offering a basic outline for those who are interested in this specific topic. In this short review, we focus on two methodologies, i.e., genetic programming (GP) and deep reinforcement learning (DRL), both having been proven effective, efficient, and robust in certain AFC problems, and outline some future prospects that might shed some light for relevant studies. | Keywords: | Active flow control (AFC) Deep reinforcement learning (DRL) Genetic programming (GP) Machine learning |
Publisher: | Elsevier | Journal: | Journal of hydrodynamics, Ser.B | ISSN: | 1001-6058 | DOI: | 10.1007/s42241-020-0026-0 | Description: | Special Column on the International Symposium on High-Fidelity Computational Methods and Applications 2019 (Guest Editors Hui Xu, Wei Zhang) | Rights: | © China Ship Scientific Research Center 2020 This is a post-peer-review, pre-copyedit version of an article published in Journal of Hydrodynamics. The final authenticated version is available online at: https://doi.org/10.1007/s42241-020-0026-0. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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1529_REN_HU_TANG_JHDmanuscript.pdf | Pre-Published version | 1.92 MB | Adobe PDF | View/Open |
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