Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning

26 Apr 2020 Hongwei Tang Jean Rabault Alexander Kuhnle Yan Wang Tongguang Wang

This paper focuses on the active flow control of a computational fluid dynamics simulation over a range of Reynolds numbers using deep reinforcement learning (DRL). More precisely, the proximal policy optimization (PPO) method is used to control the mass flow rate of four synthetic jets symmetrically located on the upper and lower sides of a cylinder immersed in a two-dimensional flow domain... (read more)

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