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

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7 Citations (Scopus)


We propose a novel active-flow-control strategy for bluff bodies to hide their hydrodynamic traces, i.e., strong shears and periodically shed vortices, 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 is deployed in the near wake to provide feedback signals. Through the data-driven deep reinforcement learning, effective control strategies are trained for the WSLB actuation to mitigate the cylinder's hydrodynamic signatures. 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 also to be effective when the cylinder undergoes transverse vortex-induced vibration. The findings from this study can shed some light on the design and operation of underwater structures and robotics to achieve hydrodynamic stealth.

Original languageEnglish
Article number093602
JournalPhysics of Fluids
Issue number9
Publication statusPublished - 1 Sep 2021

ASJC Scopus subject areas

  • Computational Mechanics
  • Condensed Matter Physics
  • Mechanics of Materials
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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