Analytical and Computational Aspects of Collaborative Optimization for Multidisciplinary Design

Abstract

Analytical features of multidisciplinary optimization (MDO) problem formulations have signi cant practical consequences for the abilityof nonlinear programmingalgorithmsto solve the resulting computationaloptimization problems reliably and ef ciently. We explore this important but frequently overlooked fact using the notion of disciplinary autonomy. Disciplinary autonomy is a desirable goal in formulating and solving MDO problems; however, the resulting system optimization problems are frequently dif cult to solve. We illustrate the implications of MDO problem formulation for the tractability of the resulting design optimization problem by examining a representative class of MDO problem formulations known as collaborative optimization. We also discuss an alternative problem formulation, distributed analysis optimization, that yields a more tractable computational optimization problem. Nomenclature= system objective function g i = design constraints for D i l i = design variables local to D i min = minimize s = design variables shared by disciplines s.t. = subject to

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