Title: Multidisciplinary design optimisation under uncertainty: an information model approach

Authors: James A. Reneke; Margaret M. Wiecek; Georges M. Fadel; Sundeep Samson

Addresses: Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA ' Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA ' Department of Mechanical Engineering, Clemson University, Clemson, SC 29634, USA ' Department of Mathematical Sciences, Clemson University, Clemson, SC 29634, USA

Abstract: The design of a multi-purpose vehicle capable of performing diverse missions in diverse environmental conditions requires a multi-disciplinary approach. Uncertain missions implied by a multi-purpose design goal and uncertain environmental conditions resulting from multiple operating theatres involve tradeoffs in vehicle performance. An information model approach to handling tradeoffs is presented. The design process is conceptualized as proceeding in stages. At each stage, the design problem is decomposed from the top down into design levels and interacting components having uncertain elements on each level. The components may require different knowledge bases and models with different mathematical structures, time and size scales, calling for higher-level coordination. Component performances are modelled as random functions of uncertainties considered as deterministic variables. Information models are developed making use of second-order statistics of the random performance functions and an algebra of their reduced-order representations. Decision-making proceeds from the bottom up. Higher-level design decisions, the result of tradeoffs between alternative component designs, are based on the information models of the component performance functions. Preferred overall designs are determined within a finite set of feasible designs by means of multi-criteria optimisation methods without using mathematical programming. The methodology is illustrated by a simplified two-component vehicle design problem.

Keywords: multidisciplinary optimisation; engineering design; decomposition; information models; reproducing kernel Hilbert spaces; separable random fields; second-order statistics; reduced-order representations; design optimisation; uncertainty; modelling; vehicle design; multi-purpose vehicles; vehicle performance; component design; design process.

DOI: 10.1504/IJVD.2013.050848

International Journal of Vehicle Design, 2013 Vol.61 No.1/2/3/4, pp.249 - 284

Available online: 05 Dec 2012 *

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