Extreme value oriented random field discretization based on an hybrid polynomial chaos expansion — Kriging approach

Dubreuil, Sylvain; Bartoli, Nathalie; Gogu, Christian; Lefèbvre, Thierry; Colomer, Joan Mas

doi:10.1016/j.cma.2018.01.009

Cited by 19 publications

(14 citation statements)

References 35 publications

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“…In connection with random fields, Dubreuil et al [20] developed an adaptive RF representation dedicated to the approximation of extreme value statistics. The proposed approach was based on a discretization of RF by hybrid PCE-Kriging approaches and could provide an accurate presentation of the global extremum for any realization.…”

Section: Literature Reviewmentioning

confidence: 99%

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Hariri-Ardebili

Mahdavi

Abdollahi

et al. 2021

Water

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Quantification of structural vibration characteristics is an essential task prior to perform any dynamic health monitoring and system identification. Anatomy of vibration in concrete arch dams (especially tall dams with un-symmetry shape) is very complicated and requires special techniques to solve the eigenvalue problem. The situation becomes even more complicated if the material distribution is assumed to be heterogeneous within the dam body (as opposed to conventional isotropic homogeneous relationship). This paper proposes a hybrid Random Field (RF)–Polynomial Chaos Expansion (PCE) surrogate model for uncertainty quantification and sensitivity assessment of dams. For different vibration modes, the most sensitive spatial locations within dam body are identified using both Sobol’s indices and correlation rank methods. Results of the proposed hybrid model is further validated using the classical random forest regression method. The outcome of this study can improve the results of system identification and dynamic analysis by properly determining the vibration characteristics.

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Section: Literature Reviewmentioning

confidence: 99%

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Hariri-Ardebili

Mahdavi

Abdollahi

et al. 2021

Water

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“…for a fixed value of z. In the following an original continuous approximation of this random field proposed in [9] that will be further used to explore the design space is introduced.…”

Section: Random Objective Functionmentioning

confidence: 99%

Towards an efficient global multidisciplinary design optimization algorithm

Dubreuil

Bartoli

Gogu

et al. 2020

Struct Multidisc Optim

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This article proposes a new surrogate based Multidisciplinary Design Optimization algorithm.The main idea is to replace each disciplinary solver involved in a non linear Multidisciplinary Analysis by Gaussian Process surrogate models. Although very natural, this approach creates difficulties as the non linearity of the Multidisciplinary Analysis leads to a non Gaussian model of the objective function. However, in order to follow the path of classical Bayesian optimization such as the Efficient Global Optimization algorithm, a dedicated model of the non Gaussian random objective function is proposed. Then, an expected improvement criterion is proposed to enrich the disciplinary Gaussian processes in an iterative procedure, that we call Efficient Global Multidisciplinary Design Optimization (EGMDO). Such an adaptive approach allows to focus the computational budget on areas of the design space relevant only with respect to the optimization problem. The obtained reduction of the number of solvers evaluations is illustrated on a classical MDO test case and on an engineering test case.

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“…The problem was formulated so that the calculation of small probabilities of failure and extreme quantiles were unified. Dubreuil et al (2018) carried out a parametric study of engineering models under uncertainty, using the PC-Kriging approach. The advantage of the approach developed in this article was the reduction in computational cost, which was demonstrated in several numerical examples and also illustrated in the parametric study of an aircraft wing under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

Pavlack

Paixão

Silva

et al. 2021

Structural Health Monitoring

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This study aims to investigate the performance of a data-driven methodology for quantifying damage based on the use of a metamodel obtained from the Polynomial Chaos-Kriging method. The investigation seeks to quantify the severity of the damage, described by a specific type of debonding in a wind turbine blade as a function of a damage index. The damage indexes used are computed using a data-driven vibration-based structural health monitoring methodology. The blade’s debonding damage is introduced artificially, and the blade is excited with an electromechanical actuator that introduces a mechanical impulse causing the impact on the blade. The acceleration responses’ vibrations are measured by accelerometers distributed along the trailing and the wind turbine blade. A metamodel is formerly obtained through the Polynomial Chaos-Kriging method based on the damage indexes, trained with the blade’s healthy condition and four damage conditions, and validated with the other two damage conditions. The Polynomial Chaos-Kriging manifests promising results for capturing the proper trend for the severity of the damage as a function of the damage index. This research complements the damage detection analyses previously performed on the same blade.

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Extreme value oriented random field discretization based on an hybrid polynomial chaos expansion — Kriging approach

Cited by 19 publications

References 35 publications

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

An RF-PCE Hybrid Surrogate Model for Sensitivity Analysis of Dams

Towards an efficient global multidisciplinary design optimization algorithm

Polynomial Chaos-Kriging metamodel for quantification of the debonding area in large wind turbine blades

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