Towards effective robust design of hierarchical industrial models

Complex industrial models are of hierarchical nature. To compute those models non-intrusive local/global coupling using legacy codes have been introduced in the past. The proposal aims ay developing an efficient non-intrusive coupled global-local approach allowing achieving a robust global design with respect to possible local modifications or uncertainties. Fort thisaA hybrid surrogate model is constructed, combining kriging for the global design variables and Polynomial Chaos Expansion to represent local uncertainties. To enhance efficiency, a two-level multifidelity surrogate model is also employed to accelerate robust optimization. Overall, the proposed approach fully exploits the non-intrusive local/global coupling algorithm to generate low-cost multifidelity datasets accounting for local uncertainties. This surrogate framework allows for cost-effective uncertainty quantification and robust optimization, which is implemented using the NSGA-II algorithm to efficiently explore the design space. The methodology is illustrated on 2D examples sensitive to local perturbations, highlighting its potential for non-intrusive hybrid robust design applications.