Deep reinforcement learning control unlocks enhanced heat transfer in turbulent convection
Zisong Zhou, Xiaojue Zhu
Abstract
Turbulent convection governs heat transport in both natural and industrial settings, yet optimizing it under extreme conditions remains a significant challenge. Traditional control strategies, such as predefined temperature modulation, struggle to achieve substantial enhancement. Here, we introduce a deep reinforcement learning (DRL) framework that autonomously discovers optimal control policies to maximize heat transfer in turbulent Rayleigh-Bénard convection. By dynamically adjusting wall temperature fluctuations, the DRL agent achieves a heat transfer enhancement of up to 38.5%, exceeding the 20 to 25% limit of conventional methods. The learned strategy reveals a nonlinear state-action relationship, inducing a fully modulated boundary layer regime. Furthermore, we distill the DRL insights into a simplified bang-bang control model, which retains comparable performance (up to 40.0% enhancement) and, crucially, generalizes to unseen, higher Rayleigh number cases without additional training. Our results demonstrate the power of machine learning in turbulence control and reveal a framework with potential for intelligent heat transfer optimization in real-world applications.