Worst Case and Fuzzy Enhanced Robust Design Optimization Methodology

Abstract

*† The multidisciplinary nature of complex engineering systems combined with the effect of uncertainty presents significant and exciting research challenges. This paper presents a new design methodology for efficient robust design optimization of complex systems involving multidisciplinary and computationally intensive analysis with large number of uncertain design variables. At each candidate design point a two-level first-order orthogonal designs is proposed to estimate worst case variation due to assumed uncertainty in design variables. Multi-objective Genetic Algorithm optimization with fuzzy-pareto-front uses this worst case variation estimation and mean to determine the robust solution. The performance of proposed methodology is investigated in this paper for the conceptual design of multistage solid fueled Space Launch Vehicle for Low Earth Orbit mission with one ton class payload. The results demonstrate the effectiveness of the proposed methodology as the number of required exact analysis is much lower than traditional methodologies like Monte Carlo method.