Monte Carlo simulation (MCS) has been widely used in the development of aerospace vehicles. The rapid growth of computer power has allowed it to be applied in particular to the evaluation of systems before flight, where its capability of directly evaluating nonlinear systems incorporating various uncertain inputs is a key advantage. To improve a system’s design, it is crucial to detect those uncertain inputs that have a significant influence on unsatisfactory MCS results. This paper presents a new approach for detecting such input parameters quickly. It combines two statistical tests: Kuiper’s test and the test. The advantage of the approach is that it uses only input and output data from MCS simulation evaluations: knowledge of the system model or further simulations are unnecessary. Because its results can be obtained quickly, the method contributes to the efficient development of flight vehicles. The approach also can be applied to MCS results that have a relatively small number of unsatisfactory results, so it is effective not only during the early stages of design where failures are more likely, but also for successive design iterations where failures are fewer.
Quick Detection of Influential Inputs in Monte Carlo Simulation
Journal of Aerospace Information Systems ; 19 , 2 ; 113-123
2021-11-09
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Identification of Influential Uncertainties in Monte Carlo Analysis
Online Contents | 2002
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