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Modern aerospace software systems simulations usually contain many (dependent and independent) parameters. Due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components, analysis is complicated and time consuming. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such a comprehensive analysis with a tool to support parametric analysis and envelope assessment: a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations and model-based testcase generation is used to limit the number of cases without sacrificing important interactions in the parameter space. For the automatic analysis of the generated data we use unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. This unique combination of advanced machine learning technology enables a fast and powerful multivariate analysis that supports finding of root causes.
Schumann et al. (Sun,) studied this question.
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