| Citation: | MIAO Junjie, WANG Dong, JIN Xin, et al. Study on performance optimization of single expansion ramp nozzle based on depth neural network[J]. Journal of Aerospace Power, 2025, 40（8）：20230531 doi: 10.13224/j.cnki.jasp.20230531
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In response to the requirements of thrust maximization, torque matching and geometric constraint for single expansion ramp nozzle (SERN) of scramjet due to the integration of aircraft/engines, a novel method based on depth neural network (DNN) for SERN performance optimization was proposed. Based on the data set from numerical simulation, the predicting model for calculating the SERN’s wall pressure distribution was established by DNN, which can be applied to optimize the SERN performance combined with the optimization algorithm, and the sensitivity analysis of nozzle performance on geometric parameters was carried out. The results showed that: the prediction model based on Unet-L3 convolutional neural network for predicting the wall pressure distribution of SERN had rather high accuracy. The single-objective optimization algorithm based on the DNN prediction model and the differential evolution algorithm can rarely optimize the thrust coefficient and thrust vector angle simultaneously. The multi-objective optimization for thrust coefficient and thrust vector angle of SERN can be achieved by using the DNN prediction model and hybrid optimization algorithm. By multi-objective optimization, the reduction of thrust coefficient by
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