Intelligent decision-making approach for rapid optimization of double-wall cooling structures under varying cooling demands

Double-wall structures are widely employed for cooling turbine blades. When manufacturing materials and application conditions vary, the design of double-wall structures should be adapted to meet different cooling demands. However, because of inevitable repetitive iterations, conventional evolutionary algorithms exhibit low efficiency in optimizing double-wall structures when cooling demands change. This paper presents a reinforcement learning-based method to optimize double-wall cooling structures facing various cooling demands rapidly. A decision network established a forward mapping from cooling demands to optimized structural designs and was trained using historical decision-making experiences generated from numerical simulations. The historical experiences were quantified uniformly based on the cooling performance gains or losses resulting from changes in geometric parameters. The physical understanding that cooling performance evaluations differ with varying cooling demands was incorporated into the training dataset. The performance of the trained decision network was validated by varying the cooling optimization objectives. Results indicate that the decision network can achieve optimal double-wall cooling units that meet given objectives with a single decision. The decision process took 5 ms, nearly 90 times faster than traditional genetic algorithms. Moreover, a partitioning, multi-cycle application strategy for the decision network was employed to optimize double-wall cooling plates under non-uniform inlet temperature distributions, resulting in a 13.7 K reduction in overall average temperature and improved radial temperature uniformity.