PERFORMANCE OPTIMIZATION OF MULTIDISCIPLINARY MECHANICAL SYSTEMS SUBJECT TO UNCERTAINTIES

This paper presents a methodology to solve a new class of stochastic o ptimization problems for multidisciplinary systems (multidisciplinary stochastic optimization or MSG) wherein the objective is to maximize s ystem mechanical performance (e.g. aerodynamic efficiency) while satis fying reliability-based constraints (e.g. structural safety). Multidis ciplinary problems require a different solution approach than those so lved in earlier research in reliability-based structural optimization (single discipline) wherein the goal is usually to minimize weight (or cost) for a structural configuration subject to a limiting probabilit y of failure or to minimize probability of failure subject to a limiti ng weight (or cost). For the problems solved herein, the objective is to maximize performance over the range of operating conditions, while satisfying constraints that ensure safe and reliable operation. Becaus e the objective is performance based and because the constraints are r eliability based, the random variables used in the objective must mode l variability in operating conditions, while the random variables used in the constraints must model uncertainty in extreme values (to ensur e safety). Thus, the problem must be formulated to treat these two dif ferent types of variables at the same time, including the case when th e same physical quantity (e.g. a particular load) appears in both the objective function and the constraints. In addition, the problem must be formulated to treat multiple load cases, which can again require mo deling the same physical quantity with different random variables. Det erministic multidisciplinary optimization (MDO) problems have advanced to the stage where they are now commonly formulated with multiple loa d cases and multiple disciplines governing the objective and constrain ts. This advancement has enabled MDO to solve more realistic problems of much more practical interest. The formulation used herein solves st ochastic optimization problems that are posed in this same way, enabli ng similar practical benefits but, in addition, producing optimum desi gns that are more robust than the deterministic optimum designs (since uncertainties are accounted for during the optimization process). The methodology has been implemented in the form of a baseline MSO shell that executes on both a massively parallel computer and a network of w orkstations, The MSO shell is demonstrated herein by a stochastic shap e optimization of an axial compressor blade involving fully coupled ae rostructural analysis. (C) 1997 Elsevier Science Ltd.