Multidisciplinary design optimization under correlated uncertainties

Uncertainty analysis is a hot research topic in multidisciplinary design optimization for complex mechanical systems. Existing multidisciplinary design optimization works typically assume that uncertainties are uncorrelated of each other. In real-world engineering systems, however, correlations do exist between different uncertainties. The multidisciplinary design optimization methods without considering correlations between uncertainties may cause inaccuracy and thus misleading optimization results. In this article, we make contributions by proposing a new multidisciplinary design optimization approach based on the ellipsoidal set theory to investigate the characteristics of correlated uncertainties and incorporate their effects in the multidisciplinary design optimization through an advanced collaborative optimization method, where the quantitative model of correlated uncertainties is transformed into constrains of subsystems. Both a mathematical example and a case study of an engineering system are provided to illustrate feasibility and validity of the proposed method.