Reinforcement Learning for Active Flow Control in Experiments

by   Dixia Fan, et al.

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.


Gym-preCICE: Reinforcement Learning Environments for Active Flow Control

Active flow control (AFC) involves manipulating fluid flow over time to ...

How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning

Deep reinforcement learning (DRL) for fluidic pinball, three individuall...

Comparative analysis of machine learning methods for active flow control

Machine learning frameworks such as Genetic Programming (GP) and Reinfor...

Symmetry reduction for deep reinforcement learning active control of chaotic spatiotemporal dynamics

Deep reinforcement learning (RL) is a data-driven, model-free method cap...

Zermelo's problem: Optimal point-to-point navigation in 2D turbulent flows using Reinforcement Learning

To find the path that minimizes the time to navigate between two given p...

Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning

We present theoretical and numerical results concerning the problem to f...

Reinforcement Learning reveals fundamental limits on the mixing of active particles

The control of far-from-equilibrium physical systems, including active m...

Please sign up or login with your details

Forgot password? Click here to reset