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We propose a novel active-flow-control (AFC) strategy for bluff bodies tohide their hydrodynamic traces from predators. A group ofwindward-suction-leeward-blowing (WSLB) actuators are adopted to control thewake of a circular cylinder submerged in a uniform flow. An array of velocitysensors are deployed in the near wake to provide feedback signals. Through thedata-driven deep reinforcement learning (DRL), effective control strategies aretrained for the WSLB actuation to mitigate the cylinder's hydrodynamicsignatures, i.e., strong shears and periodically shed vortices. Only a 0.29%deficit in streamwise velocity is detected, which is a 99.5% reduction from theuncontrolled value. The same control strategy is found to be also effectivewhen the cylinder undergoes transverse vortex-induced vibration (VIV). Thefindings from this study can shed some lights on the design and operation ofunderwater structures and robotics to achieve hydrodynamic stealth.

Bluff body uses deep-reinforcement-learning trained active flow control to achieve hydrodynamic stealth