Reliability-based design optimization of shells with uncertain geometry using adaptive Kriging metamodels
V. Dubourg, J.-M. Bourinet, B. Sudret

TL;DR
This paper presents an adaptive Kriging surrogate modeling approach for reliability-based design optimization of shells with uncertain geometry, effectively balancing computational cost and accuracy in complex engineering problems.
Contribution
It introduces an adaptive Kriging-based RBDO framework that combines surrogate modeling with gradient optimization for uncertain shell design, specifically applied to submarine cylindrical shells.
Findings
Accurately estimates failure probabilities with reduced computational effort.
Enhances design robustness under geometric uncertainties.
Successfully applied to submarine shell buckling optimization.
Abstract
Optimal design under uncertainty has gained much attention in the past ten years due to the ever increasing need for manufacturers to build robust systems at the lowest cost. Reliability-based design optimization (RBDO) allows the analyst to minimize some cost function while ensuring some minimal performances cast as admissible failure probabilities for a set of performance functions. In order to address real-world engineering problems in which the performance is assessed through computational models (e.g., finite element models in structural mechanics) metamodeling techniques have been developed in the past decade. This paper introduces adaptive Kriging surrogate models to solve the RBDO problem. The latter is cast in an augmented space that "sums up" the range of the design space and the aleatory uncertainty in the design parameters and the environmental conditions. The surrogate…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Probabilistic and Robust Engineering Design · Topology Optimization in Engineering
