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We investigate drag reduction mechanisms in flows past two- andthree-dimensional cylinders controlled by surface actuators using deepreinforcement learning. We investigate 2D and 3D flows at Reynolds numbers upto 8,000 and 4,000, respectively. The learning agents are trained in planarflows at various Reynolds numbers, with constraints on the available actuationenergy. The discovered actuation policies exhibit intriguing generalizationcapabilities, enabling open-loop control even for Reynolds numbers beyond theirtraining range. Remarkably, the discovered two-dimensional controls, inducingdelayed separation, are transferable to three-dimensional cylinder flows. Weexamine the trade-offs between drag reduction and energy input while discussingthe associated mechanisms. The present work paves the way for control ofunsteady separated flows via interpretable control strategies discoveredthrough deep reinforcement learning.

Drag Reduction in Flows Past 2D and 3D Circular Cylinders Through Deep Reinforcement Learning