  We propose a novel active-flow-control (AFC) strategy for bluff bodies to
hide their hydrodynamic traces from predators. A group of
windward-suction-leeward-blowing (WSLB) actuators are adopted to control the
wake of a circular cylinder submerged in a uniform flow. An array of velocity
sensors are deployed in the near wake to provide feedback signals. Through the
data-driven deep reinforcement learning (DRL), effective control strategies are
trained for the WSLB actuation to mitigate the cylinder's hydrodynamic
signatures, 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 the
uncontrolled value. The same control strategy is found to be also effective
when the cylinder undergoes transverse vortex-induced vibration (VIV). The
findings from this study can shed some lights on the design and operation of
underwater structures and robotics to achieve hydrodynamic stealth.
