Probabilistic sensitivity analysis for the frame structure of missiles

Abstract

In this study, the high-performance and high-reliability of missiles were achieved under the background of the probabilistic analysis for frame structure of missiles. On the basis of deep investigation of the artificial neural network method and mathematical model of multiorder moment method, artificial neural network based higher order moment approach was proposed for the probabilistic analysis of complex structure. As illustrated in frame structure probabilistic analysis with multiphysics fields based on artificial neural network based higher order moment, the reliability and failure probability of frame structure were studied. Furthermore, it was shown that the radial concentrated force P and the outer radius of the frame R2 are the most important factors, and the inside radius of the frame R1 and the ultimate tensile strength of materials σ b also have an important influence on the frame structure. Through comparison with Monte Carlo simulation, second-order fourth-moment approach and the artificial neural network based first-order second-moment approach, it was demonstrated that artificial neural network based higher order moment reshapes the possibility of complex aero-missile probabilistic analysis and improves computing efficiency while preserving the accuracy. Artificial neural network based higher order moment offers a useful insight for missile reliability design and optimization with multi-discipline. The efforts of this study also enrich the theory and method of mechanical reliability design.