A decision-making design approach to optimize the hole arrangement of effusion cooling plates under non-uniform heat load conditions

Abstract

The harsh operating conditions of aero-engines contain non-uniform temperatures and variable hotspots, which require adaptive cooling techniques to accommodate the heat loads on the external surfaces of metal. Among the existing technologies, effusion cooling is an important way to fulfill such quests due to its flexibility in controlling coolant locally. As an effusively cooled component generally contains hundreds of holes, hole arrangement optimization is a high-dimensional problem that is difficult to solve using existing evolutionary algorithms. Recently matured deep reinforcement learning algorithms showed success in many research fields by incorporating data modeling with sequential optimization, which demonstrated the ability to convert high dimensional optimization problems into very low dimensional iterative optimization problems. The present study proposed a decision-making design approach to optimize the hole arrangement of a series of effusion cooling plates. Hole arrangements were transformed into Markov decision chains of opening or closing each hole. The decision chains were solved by a reinforcement learning framework integrated with a deep learning-based environment simulator. Results indicated that the proposed approach could yield an elevation of cooling performance by 20% as compared with the results from genetic algorithms. Moreover, an analytical correlation was extracted from the clear decision boundaries of reinforcement learning. Upon further validation, the computational cost to apply the correlation to other non-uniform heat loads was within seconds, while the cooling effect could reach the same optimization level as reinforcement learning. The idea proposed in this study could be applied to the arrangement optimization of multiple cooling technologies, such as film/effusion cooling, impingement cooling, and pin–fin array cooling.