A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: Constraint boundary sampling

Lee, T. H.; Jung, Ju-Seong

doi:10.1016/j.compstruc.2007.05.023

Cited by 145 publications

(58 citation statements)

References 7 publications

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“…Previous studies have shown that the accuracy of metamodels for RBDO largely depends on the selection of design points (Lee and Jung 2008;Zhao et al 2009;Chen et al 2014). Consequently, several sampling strategies have been proposed in literature to improve the efficiency and accuracy of metamodel-based RBDO.…”

Section: Metamodel-based Rbdomentioning

confidence: 99%

Optimization of Vehicle Structures under Uncertainties

Shetty¹

2017

Linköping Studies in Science and Technology. Dissertations

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Cover: Schematic illustration of robustness analysis. The histograms indicate the variations in input parameter ‫ݔ‬ and the output parameter ݂ respectively.The front cover picture is reproduced by courtesy of Volvo Car Corporation and illustrates a side impact simulation.Printed by: LiU-Tryck, Linköping, Sweden, 2017 ISBN 978-91-7685-630-7 ISSN 0345-7524 Distributed by: Linköping University Department of Management and Engineering SE-581 83 Linköping, Sweden © 2017 Sandeep ShettyNo part of this publication may be reproduced, stored in a retrieval system, or be transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without prior permission of the author. Advancements in simulation tools and computer power have made it possible to incorporate simulation-based structural optimization in the automotive product development process. However, deterministic optimization without considering uncertainties such as variations in material properties, geometry or loading conditions might result in unreliable optimum designs.In this thesis, the capability of some established approaches to perform design optimization under uncertainties is assessed, and new improved methods are developed. In particular, vehicle structural problems which involve computationally expensive Finite Element (FE) simulations, are addressed.The first paper focuses on the evaluation of robustness, given some variation in input parameters, the capabilities of three well-known metamodels are evaluated. In the second paper, a comparative study of deterministic, reliability-based and robust design optimization approaches is performed. It is found that the overall accuracy of the single-stage (global) metamodels, which are used in the above study, is acceptable for deterministic optimization. However, the accuracy of performance variation prediction (local sensitivity) must be improved. In the third paper, a decoupled reliability-based design optimization (RBDO) approach is presented. In this approach, metamodels are employed for the deterministic optimization only while the uncertainty analysis is performed using FE simulations in order to ensure its accuracy.In the fifth paper, two new sequential sampling strategies are introduced that aim to improve the accuracy of the metamodels efficiently in critical regions. The capabilities of the methods presented are illustrated using analytical examples and a vehicle structural application.It is important to accurately represent physical variations in material properties since these might exert a major influence on the results. In previous work these variations have been treated in a simplified manner and the consequences of these simplifications have been poorly understood. In the fourth paper, the accuracy of several simple methods in representing the real material variation has been studied. It is shown that a scaling of the nominal stress-strain curve based on the Rm scatter is the best choice of the evaluated choices, when limited material data is avail...

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Section: Metamodel-based Rbdomentioning

confidence: 99%

Optimization of Vehicle Structures under Uncertainties

Shetty¹

2017

Linköping Studies in Science and Technology. Dissertations

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“…These issue are addressed in the present article.The other major part of all adaptive algorithms is the stopping condition. This ranges from the use of reliability indices [7,8] through error in the estimation of the failure probability [5,13,15,16,17] and forms of measure of the discrepancy between the GPE predictions and code observations [4,6,9,18] to thresholds on the learning function [3,12,14]. Most frameworks use some form of statistic related to the surrogate, which, depending on the use and complexity of the problem, could prove insufficiently robust.…”

Section: Introductionmentioning

confidence: 99%

“…In general, most approaches follow a framework which is composed of a sampling rule, utility function, stopping criterion and any other specific details. The authors of [6] use constraint boundary sampling to select improvement points. A combination between a MCMC sampling and k-means clustering is used to select new data points in [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Emulation-Based Reliability Analysis

Hristov¹,

DiazDelaO²,

Kubiak³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Abstract. This paper presents an approximation method for performing reliability analysis with high fidelity computer codes at a reasonable computational cost. Complex models are common in science and engineering due to their ability to substitute costly and some times infeasible practical experiments. These models, however, suffer from high computational cost. This causes problems when performing sampling -based reliability analysis, since the failure modes of the system typically occupy a small region of the input space and thus relatively large sample sizes are required for the accurate estimation of their characteristics. The sequential sampling method proposed in this article, combines Gaussian process-based optimisation and Subset Simulation. Gaussian process emulators construct a statistical approximation to the output of the original code, which is both affordable to use and has its own measure of predictive uncertainty. Subset Simulation is used to efficiently populate those regions of the initial approximation which are likely to lead to the performance function exceeding a predefined critical threshold. Among all samples, the ones that are likely to contribute most to increasing the quality of the surrogate in the vicinity of the failure regions are selected, using Bayesian optimisation methods. The iterative nature of the method ensures that an arbitrarily accurate approximation of the failure region in performance space is developed at a reasonable computational cost. The presented method is applied to a number of benchmark problems.

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“…To overcome this issue, iterative kriging surrogate models have been proposed to reduce the number of evaluations [1]- [3]. Infill Sampling Criterion (ISC) was used with the aim of improving the quality of the surrogate model, and searching for the solution of the optimization problem.…”

Section: Introduction Eliability-based Design Optimizationmentioning

confidence: 99%

Iterative Kriging-Based Methods for Expensive Black-Box Models

et al. 2018

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Reliability-Based Design Optimization (RBDO) in electromagnetic field problems requires the calculation of probability of failure leading to a huge computational cost in the case of expensive models. Three different RBDO approaches using kriging surrogate model are proposed to overcome this difficulty by introducing an approximation of the objective function and constraints. These methods use different infill sampling criteria (ISC) to add samples in the process of optimization or/and in the reliability analysis. Several enrichment criteria and strategies are compared in terms of number of evaluations and accuracy of the solution.Index Terms-Infill sampling criteria, kriging model, reliability analysis, reliability-based design optimization.

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A sampling technique enhancing accuracy and efficiency of metamodel-based RBDO: Constraint boundary sampling

Cited by 145 publications

References 7 publications

Optimization of Vehicle Structures under Uncertainties

Optimization of Vehicle Structures under Uncertainties

Adaptive Emulation-Based Reliability Analysis

Iterative Kriging-Based Methods for Expensive Black-Box Models

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