Multidisciplinary Design Optimization Under Uncertainty: An Information Model Approach

Motivated by needs of concurrent multi-disciplinary design of a multi-purpose vehicle, a modeling and methodological approach to handling tradeoffs is presented. Each component has uncertain elements and a random performance which is influenced by the performance of other components. The components may require different knowledge bases and models with different mathematical structures, time and size scales, calling for higher-level coordination. The theory of reproducing kernel Hilbert spaces provides the mathematical foundation for the approach. Performance is modeled as a random function of uncertainties that are considered as independent variables. Higher-level design decisions, the result of tradeoffs between alternative component designs, are based on information models of component performance functions. The models make use of second-order statistics of the performances and an algebra of their reducedorder representations. Multicriteria optimization methods are used to determine preferred overall designs.