Uncertainty Analysis for Multidisciplinary Systems Based on Convex Models

Abstract

The convex model theory is implemented to handle the uncertain parameters of multidisciplinary systems. Two types of convex models are considered simultaneously to account for the uncertainties: the ellipsoid convex model and the uniform bound convex model (or interval variables). In order to improve computing efficiency, the first-order Taylor approximation combined with global sensitivity equations (GSE) is used to estimate the intervals of the end performance of multidisciplinary systems. The sensitivity information needed in this method is often a byproduct for many gradient-based optimization algorithms, so this approach can be easily integrated with a non-deterministic optimization framework to perform the robust design for multidisciplinary systems. The method is validated using the Monte Carlo simulation in application to an electronic packaging problem. Results show that it can give a good approximation for the uncertainty intervals of the coupling variables with a small amount of calculation when the uncertainty level is low.

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