Forced convection heat transfer control for cylinder via closed-loop continuous goal-oriented reinforcement learning

Forced convection heat transfer control offers considerable engineering value. This study focuses on a two-dimensional rapid temperature control problem in a heat exchange system, where a cylindrical heat source is immersed in a narrow cavity. First, a closed-loop continuous deep reinforcement learning (DRL) framework based on the deep deterministic policy gradient (DDPG) algorithm is developed. This framework swiftly achieves the target temperature with a temperature variance of 0.0116, which is only 5.7% of discrete frameworks. Particle tracking technology is used to analyze the evolution of flow and heat transfer under different control strategies. Due to the broader action space for exploration, continuous algorithms inherently excel in addressing delicate control issues. Furthermore, to address the deficiency that traditional DRL-based active flow control (AFC) frameworks require retraining with each goal changes and cost substantial computational resources to develop strategies for varied goals, the goal information is directly embedded into the agent, and the hindsight experience replay (HER) is employed to improve the training stability and sample efficiency. Then, a closed-loop continuous goal-oriented reinforcement learning (GoRL) framework based on the HER-DDPG algorithm is first proposed to perform real-time rapid temperature transition control and address multiple goals without retraining. Generalization tests show the proposed GoRL framework accomplishes multi-goal tasks with a temperature variance of 0.0121, which is only 5.8% of discrete frameworks, and consumes merely 11% of the computational resources compared with frameworks without goal-oriented capability. The GoRL framework greatly enhances the ability of AFC systems to handle multiple targets and time-varying goals.