首页|Deep reinforcement learning for optimizing the thermoacoustic core in a supercritical CO_2 thermoacoustic engine
Deep reinforcement learning for optimizing the thermoacoustic core in a supercritical CO_2 thermoacoustic engine
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NETL
NSTL
Elsevier
Thermoacoustic engines (TAEs) are promising energy conversion technologies due to their absence of moving parts, flexibility, and environmental friendliness. The driver of such an engine is the thermoacoustic core (TAC). In this study, we propose a framework that integrates CFD simulations, a surrogate model based on an artificial neural network (ANN), and deep reinforcement learning (DRL) to optimize the channel shape in the TAC of a supercritical CO_2 TAE. CFD simulations generate a dataset for the surrogate model. The surrogate model demonstrates exceptional generalization capability (R~2 = 0.992) and computational efficiency (within 3.8 ms per prediction), enabling fast reward evaluation during the DRL optimization. The TD3 algorithm is employed to explore the continuous design space. The optimized channel achieves a pressure amplitude of 0.663 Mpa, an 8.51% improvement compared to the original straight channel, which can be attributed to the enhanced heat transfer matching between the hot heat exchanger and the ambient one. This study demonstrates the potential of combining ANN-based surrogate models with DRL for optimizing thermoacoustic devices. The proposed framework is adaptable for optimizing other thermal systems and casts light on integrating artificial intelligence with physical modeling for engineering optimization.
Thermoacoustic engineDeep reinforcement learningSurrogate modelOptimizationArtificial intelligenceSupercritical CO2
Junjiao Yang、Zhan-Chao Hu
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Department of Applied Mechanics and Engineering,School of Aeronautics and Astronautics,Sun Yat-sen University,No.66 Gongchang Road,Shenzhen,518107,Guangdong Province,PR China
NETL
NSTL
Elsevier
