A Decomposition Approach to Uncertainty Analysis of Multidisciplinary Systems

Abstract

To support eective decision making, engineers should comprehend and manage various uncertainties throughout the design process. Unfortunately, in today’s modern systems, uncertainty analysis can become cumbersome and computationally intractable for one individual or group to manage. This is particularly true for systems comprising multiple components described by a large number of models. In many cases, these models may be developed by dierent groups and even run on dierent computational platforms. This paper proposes an approach for decomposing and distributing the uncertainty analysis task amongst the various components comprising a system. The mathematical approach draws on concepts and algorithms from multidisciplinary analysis and optimization, density estimation, and sequential Monte Carlo methods. The distributed multidisciplinary uncertainty analysis approach is provably convergent and is compared to a traditional allat-once Monte Carlo uncertainty analysis approach. The proposed method is illustrated on a mathematical example and two aerospace system applications|a beam loading example and a gas turbine design example.

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