This research study explores the applicability of Deep Reinforcement Learning (DRL) for thermal control based on Computational Fluid Dynamics. To accomplish that, the forced convection on a hot plate prone to a pulsating cooling jet with variable velocity has been investigated. We begin with evaluating the efficiency and viability of a vanilla Deep Q-Network (DQN) method for thermal control. Subsequently, a comprehensive comparison between different variants of DRL is conducted. Soft Double and Duel DQN achieved better thermal control performance among all the variants due to their efficient learning and action prioritization capabilities. Results demonstrate that the soft Double DQN outperforms the hard Double DQN. Moreover, soft Double and Duel can maintain the temperature in the desired threshold for more than 98% of the control cycle. These findings demonstrate the promising potential of DRL in effectively addressing thermal control systems.

翻译：本研究探索了基于计算流体动力学（CFD）的深度强化学习（DRL）在热控制中的适用性。为此，研究了受可变速度脉冲冷却射流作用的热板上的强制对流换热问题。首先，评估了原始深度Q网络（DQN）方法在热控制中的效率和可行性。随后，对不同变体的DRL方法进行了全面比较。结果表明，软双DQN和竞争DQN因其高效的学习能力和动作优先级排序机制，在所有变体中表现出更优的热控制性能。实验结果证实，软双DQN优于硬双DQN。此外，软双DQN和竞争DQN能够在超过98%的控制周期内将温度维持在期望阈值范围内。这些发现证明了深度强化学习在有效处理热控制系统中的巨大潜力。