Worst case propagated uncertainty of multidisciplinary systems in robust design optimization

While simulation based design tools continue to be advanced at unprecedented rates, little attention has been paid to how these tools interact with other advanced design tools and how that interaction influences the multidisciplinary system analysis and design processes. In this research an investigation of how uncertainty propagates through a multidisciplinary system analysis subject to the bias errors associated with the disciplinary design tools and the precision errors in the inputs is undertaken. A rigorous derivation for estimating the worst case propagated uncertainty in multidisciplinary systems is developed and validated using Monte Carlo simulation in application to a small analytic problem and an Autonomous HoverCraft (AHC) problem. The method of worst case estimation of uncertainty is then integrated into a robust optimization framework. In robust optimization, both the objective function and the constraints consist of two parts, the original or conventional functions and an estimate of the variation of the functions. In robust optimization the engineer must trade off an increase in the objective function value for a decrease in variation. The robust optimization approach is tested in application to the AHC problem and the corresponding results are discussed.