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.
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