TY - JOUR A2 - Qu, Feng auli, Zhimao AU - Chen, Changdong AU - Pei, hou - ju AU - Kong，基于BP神经网络和遗传算法的飞机NACA进口结构优化SP - 8857821 VL - 2020 AB提出了冲压进风系统的节能优化方法。提出了一种冲压空气性能评估方法，并将其主要结构参数推广到某型飞机上。研究了结构参数对冲压件空气性能的影响，建立了冲压件空气性能数据库。采用BP神经网络和遗传算法相结合的方法进行结构优化，结果表明该方法是有效的。此外，还推导出了NACA冲压进风系统结构的优化结果。结果表明:(1)优化算法有效，质量流量和燃油惩罚预测误差小。在全样本中，质量流量的平均相对误差为1.37%，燃油惩罚的平均相对误差为1.41%。(2)预测偏差分析表明，优化设计与未优化设计差异很小。 The relative error of the mass flow rate is 0.080% while that of the fuel penalty is 0.083%. The accuracy of the proposed optimization method is proven. (3) The mass flow rate after optimization is increased to 2.506 kg/s, and the fuel penalty is decreased by 74.595 Et kg. The BP neural networks and genetic algorithms are studied to optimize the design of the ram air inlet system. It is proven to be a novel approach, and the efficiency can be highly improved. SN - 1687-5966 UR - https://doi.org/10.1155/2020/8857821 DO - 10.1155/2020/8857821 JF - International Journal of Aerospace Engineering PB - Hindawi KW - ER -