Active flow control for three-dimensional cylinders through deep
  reinforcement learning
Pol Suárez Francisco Alcántara-Ávila Arnau Miró Jean Rabault Bernat Font Oriol Lehmkuhl R. Vinuesa
This paper presents for the first time successful results of active flow
control with multiple independently controlled zero-net-mass-flux synthetic
jets. The jets are placed on a three-dimensional cylinder along its span with
the aim of reducing the drag coefficient. The method is based on a
deep-reinforcement-learning framework that couples a
computational-fluid-dynamics solver with an agent using the
proximal-policy-optimization algorithm. We implement a multi-agent
reinforcement-learning framework which offers numerous advantages: it exploits
local invariants, makes the control adaptable to different geometries,
facilitates transfer learning and cross-application of agents and results in
significant training speedup. In this contribution we report significant drag
reduction after applying the DRL-based control in three different
configurations of the problem.