Deep reinforcement learning for active flow control in a turbulent separation bubble
Bernat Font,
Francisco AlcĆ”ntara-Ćvila,
Jean Rabault
et al.
Abstract:The control efficacy of deep reinforcement learning (DRL) compared with classical periodic forcing is assessed for a turbulent separation bubble (TSB) reaching a friction Reynolds number of Reš=750. The TSB is a simplified representation of the separation phenomenon naturally arising in wings, and a successful reduction of the TSB has practical implications in the reduction of the aviation carbon footprint. We use two different grid resolutions so that the DRL training is run on the coarse grid for computatio… Show more
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