Robust regression metamodelling of complex systems: The case of solid rocket motor performance metamodelling

Abstract

Computer-based simulation models have been widely used to monitor the performance of complex systems. In spite of recent improvements in computers, use of simulation models may still be computationally impractical. On the other hand, metamodels can be used instead as an alternative approach, response surface metamodels (RSMs), particularly, polynomial regression metamodels (PRMs) being the most commonly preferred ones. However, existence of extreme observations in data may cause statistically invalid inferences if some remedial actions are not taken. One of the effective remedies is to use robust regression metamodels (RRMs), which decrease sensitivity of the results to such data. In this study, performances of several RRMs are compared with those of RSMs with respect to several criteria including accuracy, stability, efficiency and robustness using a validation method for the ballistic performance functions of a solid rocket engine. Furthermore, the effect of sampling strategies on the performance of these metamodels is assessed by collecting data using some traditional and computational design of experiment (DOEs). Results indicate that the performances of RRMs are competing with that of RSM, especially, in computational DOEs. Besides, they are more efficient and do not require an expert support with a capable software. © 2012 Springer Berlin Heidelberg.