Monte Carlo Analysis for Significant Parameters Ranking in RLV Flight Evaluation

Monte Carlo simulation is an effectivemethod for evaluating complex systems. Besides estimating the performancelevel of the system through Monte Carlo method, it is more wanted to identifykey factors in system operation so as to improve or redesign the system. When estimating the performance level, in order to obtainsufficientevaluation accuracy while keeping time cost as low as possible, its relation with confidence leveland number of simulation runs is explained according to probability and statistics theory. To identify key factors, a method ranking the significant influencing parameters automaticallyfor complex systemsbased 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 identifyingsignificant influencing parameters in automated manner.The method above is applied toa 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.