Deep reinforcement transfer learning of active control for bluff body flows at high Reynolds number

Abstract

We demonstrate how to accelerate the computationally taxing process of deep reinforcement learning (DRL) in numerical simulations for active control of bluff body flows at high Reynolds number ( $Re$ ) using transfer learning. We consider the canonical flow past a circular cylinder whose wake is controlled by two small rotating cylinders. We first pre-train the DRL agent using data from inexpensive simulations at low $Re$ , and subsequently we train the agent with small data from the simulation at high $Re$ (up to $Re=1.4\times 10^5$ ). We apply transfer learning (TL) to three different tasks, the results of which show that TL can greatly reduce the training episodes, while the control method selected by TL is more stable compared with training DRL from scratch. We analyse for the first time the wake flow at $Re=1.4\times 10^5$ in detail and discover that the hydrodynamic forces on the two rotating control cylinders are not symmetric.

Deep reinforcement learning for active flow control in a turbulent separation bubble

“…where the main advances, tendencies, and types of problems addressed by DRL for AFC are discussed. Some of the most typical cases studied are drag reduction on a cylinder, both in 2D [22][23][24][25][26] and 3D [27][28][29]; convective heat reduction in Rayleigh-Bénard convection problems [30,31]; reduction of the skin-friction coefficient in turbulent channels [32,33]; and turbulence modeling [34][35][36]. Currently, the community is working towards expanding the use of DRL to higher complexity and more realistic cases.…”