Active flow control for three-dimensional cylinders through deep reinforcement learning
This addresses drag reduction in fluid dynamics applications, representing an incremental advance with a novel method for a known bottleneck.
The paper tackled active flow control on a 3D cylinder using deep reinforcement learning with synthetic jets, achieving significant drag reduction in three configurations.
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.