Efficient optimization of reliability-constrained structural design problems including interval uncertainty. (December 2016) - Liu, Yan; Jeong, Han Koo; Collette, Matthew
Highlights: Intervals represent epistemic uncertainty in reliability-constrained optimization. An online surrogate with an adaptive quadratic search algorithm is presented. The method shows how epistemic uncertainty impacts system performance. The surrogate method reduces the computational cost by several orders of magnitude. Abstract: A novel interval uncertainty formulation for exploring the impact of epistemic uncertainty on reliability-constrained design performance is proposed. An adaptive surrogate modeling framework is developed to locate the lowest reliability value within a multi-dimensional interval. This framework is combined with a multi-objective optimizer, where the interval width is considered as an objective. The resulting Pareto front examines how uncertainty reduces performance while maintaining a specified reliability threshold. Two case studies are presented: a cantilever tube under multiple loads and a composite stiffened panel. The proposed framework demonstrates its ability to resolve the Pareto front in an efficient manner.