A parallel adaptive sampling strategy to accelerate the sampling process during the modeling of a Kriging metamodel

Zeng, Wei; Sun, Wen; Song, Hong; Ren, Tao; Sun, Yanping

doi:10.1080/02533839.2019.1660222

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…As an unbiased interpolation method, the KG model can fit multi-dimensional and strong non-linear responses well, and it has a high fitting accuracy and fitting error estimation. 22 The basic form of this model and the details of this modeling process can be found in Zeng et al 23 As the sealing performance calculation of the connector sealing structure is high dimensional and strong non-linear problem, a non-linear KG model was constructed to fit the relationship between design variables and sealing performance indexes for the purpose of improving the calculation efficiency. Therefore, in this paper, the ordinary Sobol method and the KG model were combined to establish a new sensitivity analysis method, which can be applied to improve the sensitivity analysis efficiency when determining connector sealing performance.…”

Section: Sensitivity Analysis Of Sealing Performance Based On the Kg-sobol Methodsmentioning

confidence: 99%

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Zeng

Sun

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part M:

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The deep-water pipeline connector (DPC) is a key piece of equipment for the connection of multiple facilities located in different positions of the subsea production system. Its sealing reliability affects the safety of the production system directly during oil and gas exploration. Although the reliability assessment methods of the DPC have been investigated, previous studies have mostly been carried out under a single mechanical field. To determine the DPC reliability considering thermo-mechanical coupling, this paper establishes random load models of working pressure and temperature by using the Gaussian function, and the stochastic finite element model of the DPC is constructed to obtain the sealing performance under coupling conditions. A Kriging–Sobol sensitivity analysis method is developed to obtain the main variables influencing the sealing performance for the purpose of decreasing the computational cost of the ordinary sensitivity analysis. A reliability assessment approach based on the cross entropy-importance sampling-Kriging method is introduced to analyze the DPC reliability, and a sealing reliability assessment method of DPC under thermo-mechanical coupling conditions is finally formed. An engineering case is taken to verify the effectiveness of the proposed method. The results show that the reliability analysis accuracy of the proposed method is almost agreed with the Monte Carlo method, but the computational cost can be reduced 85.61%, which indicates that the proposed method provides designers with a fast method with an acceptable computational cost to assess the reliability of the DPC connector under coupling conditions.

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Section: Sensitivity Analysis Of Sealing Performance Based On the Kg-sobol Methodsmentioning

confidence: 99%

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Zeng

Sun

Zhao

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part M:

Self Cite

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show abstract

“…This will lead to a strong nonlinear characteristic relationship between the grinding amount and grinding parameters, which results in a decline in the prediction accuracy for the established prediction model, thereby certainly reducing the prediction precision of the final grinding profile. Therefore, the KG model is used to fit the relationship between the grinding area and grinding parameters based on its good ability in nonlinear function estimation in the literature by Yang et al 3 However, KG model is a kind of interpolation model with unbiased estimation, the computational cost is relatively high, particularly the variable dimension greater than or equal to three, the computational cost increases significantly, 18 which after prediction will increase the computation time of completion to nearly 50 min. For the practical grinding operation, the computation time is too long.…”

Section: Problem Statementmentioning

confidence: 99%

A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS

Xie

Chen

Zeng

et al. 2014

Advances in Mechanical Engineering

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Rail grinding profile prediction in different grinding patterns is important to improve the grinding quality for the rail grinding operation site. However, because of high-dimensional and strong nonlinearity between grinding amount and grinding parameters, the prediction error and computational cost is relatively high. As a result, the accuracy and efficiency of conventional methods cannot be guaranteed. In this article, an accurate and efficient rail grinding profile prediction method is proposed, in which an interval segmentation approach is proposed to improve the prediction efficiency based on the geometric characteristic of a rail profile. Then, the accurate area integral approach with cubic NURBS is used as the grinding area calculation approach to improve the prediction accuracy. Finally, the normal length index is introduced to evaluate the prediction accuracy. The accuracy and stability of the proposed method are verified by comparing a conventional approach based on a practical experiment. The results demonstrate that the proposed method can predict the rail grinding profile in any grinding pattern with high accuracy and efficiency. Meanwhile, its prediction stability basically agrees with the conventional approach.

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Anti-fatigue optimization of the twisting force arm of landing gear based on Kriging approximate sequential optimization method

Xie,

Yang,

Sun

et al. 2023

Journal of the Chinese Institute of Engineers

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A parallel adaptive sampling strategy to accelerate the sampling process during the modeling of a Kriging metamodel

Cited by 4 publications

References 19 publications

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

Sealing reliability assessment of deep-water oil and nature gas pipeline connector considering thermo-mechanical coupling

A novel prediction method for rail grinding profile based on an interval segmentation approach and accurate area integral with cubic NURBS

Anti-fatigue optimization of the twisting force arm of landing gear based on Kriging approximate sequential optimization method

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