Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis

Hao, Peng; Feng, Shaojun; Zhang, Ke; Li, Zheng; Wang, Bo; Li, Gang

doi:10.1007/s00158-018-1988-1

Cited by 51 publications

(6 citation statements)

References 55 publications

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“…As already mentioned, in this paper we aim to solve problems in which φ is a black box computationally demanding function, and J is nonconvex. We assume gradient information is unknown, although such information, if available, could be employed as in Hao et al (2018). EGO algorithms are able to handle these difficulties and were successfully applied in different fields (Couckuyt et al, 2010;Li and Heap, 2011;Bae et al, 2012;Duvigneau and Chandrashekar, 2012;Gengembre et al, 2012;Kanazaki et al, 2015;Chaudhuri et al, 2015;Haftka et al, 2016;Ur Rehman and Langelaar, 2017).…”

Section: Problem Statementmentioning

confidence: 99%

“…For example, Wang et al (2014) included qualitative factors on the SK metamodel by constructing correlation functions that are valid across levels of qualitative factors. Several authors incorporated gradient estimators into the metamodel, which tends to significantly improve surface prediction (Chen et al, 2013;Qu and Fu, 2014;Ulaganathan et al, 2014;Hao et al, 2018). Chen and Kim (2014) evaluated sampling strategies while taking into account possible bias in SK simulation response estimates.…”

Section: Introductionmentioning

confidence: 99%

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Monte Carlo integration with adaptive variance selection for improved stochastic efficient global optimization

Carraro

López

Miguel

et al. 2019

Struct Multidisc Optim

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In this paper, the minimization of computational cost on evaluating multi-dimensional integrals is explored. More specifically, a method based on an adaptive scheme for error variance selection in Monte Carlo integration (MCI) is presented. It uses a stochastic Efficient Global Optimization (sEGO) framework to guide the optimization search. The MCI is employed to approximate the integrals, because it provides the variance of the error in the integration. In the proposed approach, the variance of the integration error is included into a Stochastic Kriging framework by setting a target variance in the MCI. We show that the variance of the error of the MCI may be controlled by the designer and that its value strongly influences the computational cost and the exploration ability of the optimization process. Hence, we propose an adaptive scheme for automatic selection of the target variance during the sEGO search. The robustness and efficiency of the proposed adaptive approach were evaluated on global optimization stochastic benchmark functions as well as on a tuned mass damper design problem. The results showed that the proposed adaptive approach consistently outperformed the constant approach and a multi-start op-timization method. Moreover, the use of MCI enabled the method application in problems with high number of stochastic dimensions. On the other hand, the main limitation of the method is inherited from sEGO coupled with the Kriging metamodel: the efficiency of the approach is reduced when the number of design variables increases.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monte Carlo integration with adaptive variance selection for improved stochastic efficient global optimization

Carraro

López

Miguel

et al. 2019

Struct Multidisc Optim

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show abstract

“…Hughes et al (2005) and Cottrell et al (2009) firstly introduced the concept of IGA by using the spline basis functions [such as non-uniform rational B-splines (NURBSs)] constructing the exact geometric models as interpolation functions in CAE analysis. Up to now, this approach has also gained widespread reception from the scientific community and many applications have been verified, for example, structural optimization (Cho and Ha, 2009;Qian, 2010;Ding et al, 2016;Ding et al, 2018c;Lian et al, 2017;Lian et al, 2016;Hao et al, 2018a;Hao et al, 2019;Hao et al, 2018b), plate and composite structures (Thai et al, 2014;Yu et al, 2018;Thai et al, 2015;Nguyen-Xuan et al, 2014;Chang et al, 2016;Yin et al, 2015;Thanh et al, 2019b;Phung-Van et al, 2019;Thanh et al, 2019a;Thanh et al, 2018;Phung-Van et al, 2018;Thai et al, 2018b;Thai et al, 2018a;Tran et al, 2017;Thai et al, 2016), isogeometric boundary methods (Simpson et al, 2013;Simpson et al, 2012;Peng et al, 2017;Scott et al, 2013), stochastic analysis (Ding et al, 2019a;Ding et al, 2018b;Ding et al, 2019b;Ding et al, 2019c), other splines based methods (Atroshchenko et al, 2018;Nguyen-Thanh et al, 2011;Gu et al, 2018a;Gu et al, 2018b), and especially the severa...…”

Section: Cae Modelmentioning

confidence: 99%

Isogeometric independent coefficients method for fast reanalysis of structural modifications

Ding

2019

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Purpose This paper aims to provide designers/engineers, in engineering structural design and analysis, approaches to freely and accurately modify structures (geometric and/or material), and then quickly provide real-time capability to obtain the numerical solutions of the modified structures (designs). Design/methodology/approach The authors propose an isogeometric independent coefficients (IGA-IC) method for a fast reanalysis of structures with geometric and material modifications. Firstly, the authors seamlessly integrate computer-aided design (CAD) and computer-aided engineering (CAE) by capitalizing upon isogeometric analysis (IGA). Hence, the authors can easily modify the structural geometry only by changing the control point positions without tedious transformations between CAE and CAD models; and modify material characters simply based on knots vectors. Besides, more accurate solutions can be obtained because of the high order degree of the spline functions that are used as interpolation functions. Secondly, the authors advance the proposed independent coefficients method within IGA for fast numerical simulation of the modified designs, thereby significantly reducing the enormous time spent in repeatedly numerical evaluations. Findings This proposed scheme is efficient and accurate for modifying the structural geometry by simply changing the control point positions, and material characters by knots vectors. The enormous time spent in repeated full numerical simulations for reanalysis is significantly reduced. Hence, enabling quickly modifying structural geometry and material, and analyzing the modified model for practicality in design stages. Originality/value The authors herein advance and propose the IGA-IC scheme. Where, it provides designers to fasten and simple designs and modify structures (both geometric and material). It then can quickly in real-time obtain numerical solutions of the modified structures. It is a powerful tool in practical engineering design and analysis process for local modification. While this method is an approximation method designed for local modifications, it generally cannot provide an exact numerical solution and its effectiveness for large modification deserves further study.

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“…Indeed, surrogate models can reduce significantly the computational burden needed to solve advanced physical problems, concerned with design of earthquake-resistant systems [25], nonlinear analysis of carbon nanotubes [26], reliability of large-scale structures [27,28], fluid mechanics in turbulent flows [29,30]. Despite being seldom and only recently employed in the specific field of metamaterial design [31][32][33], surrogate optimization techniques have been successfully applied in a variety of advanced applications, especially related to optimal material design problems involving -for instance -matrix and precipitate crystal structures [34], stiffened shells [35], hierarchical stiffened plates and shells [36][37][38], carbon fiber reinforced polymer laminates [39], hysteretic multilayer nanocomposites [40], variable-stiffness composite panels [41].…”

Section: Introductionmentioning

confidence: 99%

Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization

Bacigalupo

Gnecco

Lepidi

et al. 2021

Computer Methods in Applied Mechanics and Engineering

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Adaptive gradient-enhanced kriging model for variable-stiffness composite panels using Isogeometric analysis

Cited by 51 publications

References 55 publications

Monte Carlo integration with adaptive variance selection for improved stochastic efficient global optimization

Monte Carlo integration with adaptive variance selection for improved stochastic efficient global optimization

Isogeometric independent coefficients method for fast reanalysis of structural modifications

Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization

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