Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
Author:
Affiliation:
1. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao St., Nanjing 210016, China
2. Department of Mathematics, University of Oslo, 0316 Oslo, Norway
3. Blue Prism AI Labs, London WC2B 6NH, United Kingdom
Funder
National Key Research and Development Program
Funding of Nanjing Institute of Technology
National Numerical Wind Tunnel Project
Natural Science Foundation of Jiangsu Province
Natural Science Foundation of China
Priority Academic Program Development of Jiangsu Higher Education Institutions
Publisher
AIP Publishing
Subject
Condensed Matter Physics,Fluid Flow and Transfer Processes,Mechanics of Materials,Computational Mechanics,Mechanical Engineering
Link
http://aip.scitation.org/doi/pdf/10.1063/5.0006492
Reference74 articles.
1. Flow Control
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3. The control of flow separation: Study of optimal open loop parameters
4. Active Flow Control Virtual Maneuvering System Applied to Conventional Airfoil
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