Reinforcement Learning for Active Flow Control in Experiments
arXiv:2003.03419
Abstract
We demonstrate experimentally the feasibility of applying reinforcement learning (RL) in flow control problems by automatically discovering active control strategies without any prior knowledge of the flow physics. We consider the turbulent flow past a circular cylinder with the aim of reducing the cylinder drag force or maximizing the power gain efficiency by properly selecting the rotational speed of two small diameter cylinders, parallel to and located downstream of the larger cylinder. Given properly designed rewards and noise reduction techniques, after tens of towing experiments, the RL agent could discover the optimal control strategy, comparable to the optimal static control. While RL has been found to be effective in recent computer flow simulation studies, this is the first time that its effectiveness is demonstrated experimentally, paving the way for exploring new optimal active flow control strategies in complex fluid mechanics applications.
The first two authors contributed equally to this work
References in corpus (4)
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
- Artificial Neural Networks trained through Deep Reinforcement Learning discover control strategies for active flow control
- Flow Navigation by Smart Microswimmers via Reinforcement Learning
- Lily Pad: Towards Real-time Interactive Computational Fluid Dynamics
Cited by in corpus (3)
- DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
- Deep Reinforcement Learning for Active Flow Control around a Circular Cylinder Using Unsteady-mode Plasma Actuators
- Fluid forces and vortex patterns of an oscillating cylinder pair in still water with both side-by-side and tandem configurations