Robust design optimization in aeronautics using stochastic analysis and evolutionary algorithms

Abstract

Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like computational fluid dynamics (CFD) or fluid–structure interaction (FSI), can be extremely expensive. This study presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter, respectively.

Tolerance optimization by modification of Taguchi’s robust design approach and considering performance levels: Application to the design of a cold-expanded bushing

“…Robust approaches have been applied to coupled problems, like computational fluid dynamics (CFD) or fluidstructure interaction (FSI). 4 They have also been applied to a large class of preliminary design problems, e.g. for space vehicles.…”