Reliability-based design optimization using kriging surrogates and subset simulation
TLDR
The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate.Abstract:
The aim of the present paper is to develop a strategy for solving reliability-based design optimization (RBDO) problems that remains applicable when the performance models are expensive to evaluate. Starting with the premise that simulation-based approaches are not affordable for such problems, and that the most-probable-failure-point-based approaches do not permit to quantify the error on the estimation of the failure probability, an approach based on both metamodels and advanced simulation techniques is explored. The kriging metamodeling technique is chosen in order to surrogate the performance functions because it allows one to genuinely quantify the surrogate error. The surrogate error onto the limit-state surfaces is propagated to the failure probabilities estimates in order to provide an empirical error measure. This error is then sequentially reduced by means of a population-based adaptive refinement technique until the kriging surrogates are accurate enough for reliability analysis. This original refinement strategy makes it possible to add several observations in the design of experiments at the same time. Reliability and reliability sensitivity analyses are performed by means of the subset simulation technique for the sake of numerical efficiency. The adaptive surrogate-based strategy for reliability estimation is finally involved into a classical gradient-based optimization algorithm in order to solve the RBDO problem. The kriging surrogates are built in a so-called augmented reliability space thus making them reusable from one nested RBDO iteration to the other. The strategy is compared to other approaches available in the literature on three academic examples in the field of structural mechanics.read more
Citations
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An Efficient Simulation-Based Search Method for Reliability-Based Robust Design Optimization of Mechanical Components
TL;DR: A simulation-based search method based on correlation coefficients between design variables and responses that enables to efficiently and effectively find reliable and robust designs under uncertainty compared to the deterministic case is proposed.
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Subset simulation method including fitness-based seed selection for reliability analysis
TL;DR: Results showed that the proposed approaches could find proper failure sets better than the original SS method, especially in problems with several failure domains.
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An Enhanced Analytical Target Cascading and Kriging Model Combined Approach for Multidisciplinary Design Optimization
TL;DR: In this paper, a quadratic exterior penalty function (QEPF) based analytical target cascading (ATC) approach is proposed, where QEPF is adopted as the coordination strategy.
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Stochastic Reliability Measurement and Design Optimization of an Inventory Management System
TL;DR: A method of quantifying the reliability of an inventory management system and a novel, reliability-based robust design optimization model has been developed to optimally allocate and schedule time while considering uncertainty associated with inventory movement.
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Intelligent initial point selection for MPP search in reliability-based design optimization
TL;DR: Comparative study with two existing initial point strategies for MPP search shows that the proposed initial point significantly improves efficiency of MPPSearch in any PMA algorithm with various types of performance functions and input distributions.
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