A DRL agent uses far-field acoustic measurements from a hydrophone array as its sole feedback to drive synthetic jets on a cylinder, achieving up to 9.5% noise reduction and 23.8% drag reduction at Re=100.
Adjoint-based machine learning for active flow control,
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Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets
A DRL agent uses far-field acoustic measurements from a hydrophone array as its sole feedback to drive synthetic jets on a cylinder, achieving up to 9.5% noise reduction and 23.8% drag reduction at Re=100.