Engineering Optimization · 2008 · 94 citations · 28 references
EngineeringDesignSystems EngineeringDesign OptimizationOptimizationUncertainty QuantificationMethod Of MomentsOptimization Under UncertaintyOptimization-based DesignReliability ModelingReliability-based DesignDesign Under UncertaintyReliability OptimizationReliability-driven Design Optimization
Reliability‑based design optimization (RBDO) optimizes engineering systems under uncertainty but requires intensive reliability analysis; although the fourth‑moment method is computationally cheaper, RBDO studies have largely relied on first‑order methods, leaving moment‑based RBDO underexplored. The study aims to address challenges of integrating the moment method into RBDO by employing a kriging metamodel with an active constraint strategy. The authors use a kriging metamodel combined with an active constraint strategy to overcome the implementation difficulties of the moment method in RBDO. Numerical experiments on three examples demonstrate that the proposed approach is both efficient and accurate.
Reliability-based design optimization (RBDO) has been used for optimizing engineering systems with uncertainties in design variables and system parameters. RBDO involves reliability analysis, which requires a large amount of computational effort, so it is important to select an efficient method for reliability analysis. Of the many methods for reliability analysis, a moment method, which is called the fourth moment method, is known to be less expensive for moderate size problems and requires neither iteration nor the computation of derivatives. Despite these advantages, previous research on RBDO has been mainly based on the first-order reliability method and relatively little attention has been paid to moment-based RBDO. This article considers difficulties in implementing the moment method into RBDO; they are solved using a kriging metamodel with an active constraint strategy. Three numerical examples are tested and the results show that the proposed method is efficient and accurate.
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