Intelligent optimization method for real-time decision-making in laminated cooling configurations through reinforcement learning

Abstract

Optimizing conjugate heat transfer in laminated configurations is pivotal for efficiently cooling turbine blades. The traditional approaches have limitations in achieving rapid optimization when design conditions change. The emergence of reinforcement learning offers an opportunity to address this issue. However, there is currently a notable scarcity of research on the application of reinforcement learning in the optimization of laminated cooling configurations. This paper presents a novel method using the Deep Q-Network (DQN) model for real-time optimization of laminated cooling configurations. A dataset comprising 246 sets of historical design data was generated through conjugate simulations and augmented with the incorporation of physical information. After undergoing off-policy reinforcement learning, the intelligent model was employed to address optimization problems associated with temperature levels and coolant usage. Results indicate that the intelligent model achieves accurate and real-time optimization for laminated cooling configuration, with an average time of 3 ms. The ability to identify the correct optimization direction is attributed to the formulation of reward and penalty mechanisms in reinforcement learning. Real-time optimization is enabled by establishing a forward computational path from design conditions to optimization results. The findings of this study are easily transferrable and contribute to cost savings in design.