A New Approach to Multidisciplinary System Analysis under Aleatory Uncertainty

This paper develops an efficient probabilistic approach for uncertainty propagation in multidisciplinary system analysis (MDA) under aleatory uncertainty (i.e., natural or physical variability). A decoupled approach is used in this paper to un-nest the multidisciplinary system analysis from the probabilistic analysis to achieve computational efficiency. This paper proposes a moment-matching based approach to estimate the first four moments of the coupling variables. The proposed moment-matching approach uses Taylor series expansion, tensor product quadrature, and univariate dimension reduction methods to estimate the moments of the coupling variables. Once the uncertainty in the coupling variables is quantified, the system level uncertainty propagation analysis is similar to single discipline problems. The proposed methods are equally applicable to both sampling and analytical approximation-based reliability analysis methods. A mathematical problem is used to illustrate the proposed methods. The accuracy of the proposed decoupled approach is verified by Monte Carlo simulation using a multi-discipline feasible (MDF) approach.