关键词: 高超声速拦截器, 气动性能, 代理模型, 贝叶斯神经网络, 工程经验

Abstract: The widely used high-precision analytical models make the computational cost of interceptor aerodynamic shape design increasing.Therefore,the method of rapid performance evaluation based on surrogate models has received extensive attention in recent years.When the interceptor is affected by jet disturbance factors,the parameter mapping relationship is complicated,the data acquisition cost is high,and the prediction accuracy of the surrogate model is low.For this reason,a surrogate model construction method that integrates engineering experience and data was proposed.The engineering experience accumulated by designers was mathematically characterized,a loss function was constructed by combining engineering data,and an aerodynamic performance prediction surrogate model was established for a hypersonic interceptor in the near space.The experimental results showed that the proposed surrogate model construction algorithm had higher prediction accuracy and better generalization capability than the traditional methods,which provided a new idea for aerodynamic performance evaluation model construction.

Key words: hypersonic interceptor, aerodynamic performance, surrogate model, Bayesian neural network, engineering experience