Structural optimization for a jaw using iterative Kriging metamodels

Bang, Il-Kwon; Han, Dong-Seop; Han, Ge; Lee, Kwon-Hee

doi:10.1007/s12206-008-0704-2

Cited by 17 publications

(7 citation statements)

References 12 publications

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“…The zero-order method, due to its independency from using derivatives of the problem variables, is the first candidate for the optimization subroutine. It can be efficiently applied to most engineering problems (Zhang, Zhu, and Zhao 2010;Bang et al 2008). The shape or material design optimization problem can generally be formulated as a constrained minimization problem as following (Zhang et al 2012):…”

Section: Optimization Methodsmentioning

confidence: 99%

Optimal Design of Cymbal Stack Transducer in a Piezoelectric Linear Actuator by Finite Element Method

Wen

Zhang

et al. 2015

Energy Harvesting and Systems

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The optimal design of a piezoelectric linear actuator using parametric optimum method-based finite element method (FEM) was presented. First, the FEM model of the cymbal stack transducer was generated with its initial configuration. The structural parameters were chosen as the design variables and the displacement on the top surface of the transducer taken as the objective function. Second, the zero-order optimization method was chosen as the basic tool of the structural updating. The structural optimization scheme of the cymbal stack transducer was carried out based on ANSYS parametric design language (APDL). Finally, an example of dynamic response analysis was performed on the cymbal stack transducer to verify the structural optimization scheme. The results show that the displacement on the top surface is increased by 32.9% compared with the case of initial configuration.

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Section: Optimization Methodsmentioning

confidence: 99%

Optimal Design of Cymbal Stack Transducer in a Piezoelectric Linear Actuator by Finite Element Method

Wen

Zhang

et al. 2015

Energy Harvesting and Systems

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“…Most methods for efficient reliability analysis and optimisation follow a framework composed of an initial sampling strategy, a rule for selecting new points and a stopping criterion. To that effect, different initial sampling strategies are presented in [12][13][14][15][16]. The approach proposed in this paper uses Latin hypercube sampling (LHS) by McKay et al [17], which is widely used in surrogate modelling.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Gaussian process emulators for efficient reliability analysis

Hristov¹,

DiazDelaO²,

Farooq³

et al. 2019

Applied Mathematical Modelling

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ReuseThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) licence. This licence only allows you to download this work and share it with others as long as you credit the authors, but you can't change the article in any way or use it commercially. More information and the full terms of the licence here: https://creativecommons.org/licenses/ TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. AbstractThis paper presents an approximation method for performing efficient reliability analysis with complex computer models. The computational cost of industrial-scale models causes problems when performing sampling-based reliability analysis. This is due to the fact that the failure modes of the system typically occupy a small region of the performance space and thus require relatively large sample sizes to accurately estimate 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 as an integral part of the algorithm to efficiently populate those regions of the surrogate which are likely to lead to the performance function exceeding a predefined critical threshold. The emulator itself is used to inform decisions about efficiently using the original code to augment its predictions. The iterative nature of the method ensures that an arbitrarily accurate approximation of the failure region is developed at a reasonable computational cost. The presented method is applied to an industrial model of a biodiesel filter.

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“…Equations (6)-(7) can be calculated using sampling methods such as MCS with large computational cost. The Kriging surrogate model [12,13] can be employed to reduce the cost for uncertainty propagation. With training observations, the response and predicted mean square error for any given new point x can be expressed aŝ…”

Section: Mixed Uncertainty Propagation Using Evidence Theorymentioning

confidence: 99%

Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory

Xie

2016

Mathematical Problems in Engineering

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Quantification of Margins and Uncertainties (QMU) is a decision-support methodology for complex technical decisions centering on performance thresholds and associated margins for engineering systems. Uncertainty propagation is a key element in QMU process for structure reliability analysis at the presence of both aleatory uncertainty and epistemic uncertainty. In order to reduce the computational cost of Monte Carlo method, a mixed uncertainty propagation approach is proposed by integrated Kriging surrogate model under the framework of evidence theory for QMU analysis in this paper. The approach is demonstrated by a numerical example to show the effectiveness of the mixed uncertainty propagation method.

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Structural optimization for a jaw using iterative Kriging metamodels

Cited by 17 publications

References 12 publications

Optimal Design of Cymbal Stack Transducer in a Piezoelectric Linear Actuator by Finite Element Method

Optimal Design of Cymbal Stack Transducer in a Piezoelectric Linear Actuator by Finite Element Method

Adaptive Gaussian process emulators for efficient reliability analysis

Quantification of Margins and Uncertainties Approach for Structure Analysis Based on Evidence Theory

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