Analytic target cascading with fuzzy uncertainties based on global sensitivity analysis for overall design of launch vehicle powered by hybrid rocket motor

Abstract

An analytic target cascading (ATC) method with fuzzy uncertainties based on global sensitivity analysis is proposed to solve the optimization problem of typical multilevel multidisciplinary nonlinear system. The overall design of launch vehicle (LV) powered by hybrid rocket motors (HRMs) involves multiply disciplines, and is decomposed into two levels using an ATC framework. The fuzzy theory is applied to describe the uncertain design factors caused by decisions and cognition insufficiency. The rank correlation coefficient method (RCCM) based on feasible optimization solutions and the quadratic response surface method (QRSM) based on Latin hypercube sampling (LHS) are used for global sensitivity analyses of input uncertainty and model uncertainty, respectively. The multi-island genetic algorithm (MIGA) is adopted in all examples, and the known two-phase optimization method is used to verify the LV design results. The results show that global sensitivity analysis can significantly filter the fuzzy uncertain factors which have little influence on the responses. The ATC decomposition is applicable in solving the calculation burden caused by uncertainties. The fuzzy-based design optimization with ATC is more efficient than that with MDF, and gives more reliable and robust results than the deterministic ATC method.