Monte Carlo Analysis for Significant Parameters Ranking in RLV Flight Evaluation

Monte Carlo simulation is an effective method for evaluating complex systems. Besides estimating the performance level of the system through Monte Carlo method, it is more wanted to identify key factors in system operation so as to improve or redesign the system. When estimating the performance level, in order to obtain sufficient evaluation accuracy while keeping time cost as low as possible, its relation with confidence level and number of simulation runs is explained according to probability and statistics theory. To identify key factors, a method ranking the significant influencing parameters automatically for complex systems based on naive Bayes classifier (NBC) and kernel density estimator (KDE) is developed. NBC used for classification makes the method valid for all kinds of linear and nonlinear complex systems, and KDE contributes greatly to identifying significant influencing parameters in automated manner. The method above is applied to a reusable launch vehicle (RLV) flight evaluation. Through the evaluation, bias of atmosphere density is identified as the most significant parameter which relies on the flight control mode in the terminal flight phase. (C) 2015 The Authors. Published by Elsevier Ltd.