A novel dual-stage adaptive Kriging method for profust reliability analysis

Feng, Kaixuan; Lü, Zhenzhou; Wang, Lu

doi:10.1016/j.jcp.2020.109701

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…As shown in Figure 1, the EAK modeling is mainly divided into two stages: in Stage I , the initial Kriging model with a small number of sample points is first constructed and the initial state of the Markov chain is determined; considering the MCMC algorithm, the samples in failure domain are identified; by performing the K -means cluster analysis (Yun et al ., 2020; Feng et al ., 2020) for the samples in failure domain, the IS samples are generated by centering on the obtained K center-of-mass points, respectively. In Stage II , regarding the IS samples in the reduced sampling pool as candidate samples, updating the active Kriging model until the convergence criterion is satisfied.…”

Section: Hierarchical Collaborative Enhanced Active Krigingmentioning

confidence: 99%

Fatigue reliability framework using enhanced active Kriging-based hierarchical collaborative strategy

Zhang

Song

Bai

2023

IJSI

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PurposeThe purpose of this study is to improve the computational efficiency and accuracy of fatigue reliability analysis.Design/methodology/approachBy absorbing the advantages of Markov chain and active Kriging model into the hierarchical collaborative strategy, an enhanced active Kriging-based hierarchical collaborative model (DCEAK) is proposed.FindingsThe analysis results show that the proposed DCEAK method holds high accuracy and efficiency in dealing with fatigue reliability analysis with high nonlinearity and small failure probability.Research limitations/implicationsThe effectiveness of the presented method in more complex reliability analysis problems (i.e. noisy problems, high-dimensional issues etc.) should be further validated.Practical implicationsThe current efforts can provide a feasible way to analyze the reliability performance and identify the sensitive variables in aeroengine mechanisms.Originality/valueTo improve the computational efficiency and accuracy of fatigue reliability analysis, an enhanced active DCEAK is proposed and the corresponding fatigue reliability framework is established for the first time.

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Section: Hierarchical Collaborative Enhanced Active Krigingmentioning

confidence: 99%

Fatigue reliability framework using enhanced active Kriging-based hierarchical collaborative strategy

Zhang

Song

Bai

2023

IJSI

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“…Generally, the most time-consuming work in reliability analysis or RBDO is the evaluation of the performance function, and the total number of the performance function evaluation is usually regarded as the total computational cost in such problems (Cheng et al. , 2006; Feng et al. , 2020; Wang et al.…”

Section: Algorithm Outlinementioning

confidence: 99%

“…and the third part related to the deterministic constraints P q l¼1 PD l $I ½h l ðd Þ. Generally, the most time-consuming work in reliability analysis or RBDO is the evaluation of the performance function, and the total number of the performance function evaluation is usually regarded as the total computational cost in such problems (Cheng et al, 2006;Feng et al, 2020;Wang et al, 2017;Yun et al, 2019). Thus, the computational cost in estimating Fðd Þ only exists in the evaluation of its second part…”

Section: Algorithm Outlinementioning

confidence: 99%

An innovative reliability-based design optimization method by combination of dual-stage adaptive kriging and genetic algorithm

Feng

Lü

2022

MMMS

Self Cite

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PurposeThis study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.Design/methodology/approachIn the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.FindingsCompared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.Originality/valueSince the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

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“…The Kriging predictor can realize the exploration of design space at a lower computational cost and has better robustness when estimating parameters. 14,15 Therefore, it has been widely used in engineering design and optimization. In addition, some extensions such as co-Kriging, 16,17 gradientenhanced Kriging, 18 and nonstationary Kriging 19 have been researched and achieved better approximation accuracy.…”

Section: Introductionmentioning

confidence: 99%

A dynamic sample augmentation Kriging metamodeling method for industrial residue hydrogenation process

Wang

Liu

et al. 2022

Journal of Chemometrics

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A novel dual-stage adaptive Kriging method for profust reliability analysis

Cited by 14 publications

References 28 publications

Fatigue reliability framework using enhanced active Kriging-based hierarchical collaborative strategy

Fatigue reliability framework using enhanced active Kriging-based hierarchical collaborative strategy

An innovative reliability-based design optimization method by combination of dual-stage adaptive kriging and genetic algorithm

A dynamic sample augmentation Kriging metamodeling method for industrial residue hydrogenation process

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