Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators — Application to extreme loads on wind turbines

Abdallah, Imad; Lataniotis, Christos; Sudret, Bruno

doi:10.1016/j.probengmech.2018.10.001

Cited by 51 publications

(26 citation statements)

References 36 publications

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“…First, one only needs to build Kriging model of each fidelity level independently, and it does not require to model the crosscorrelation between LF and HF samples, thus it is simpler and computationally cheaper than the classic Co‐Kriging model. Second, it is more flexible as different correlation kernel can be utilized in Kriging model of different fidelity level . However, when dealing with high‐dimensional problems, the samples needed to train the LF Kriging model increase greatly.…”

Section: Methodsmentioning

confidence: 99%

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

Cheng

Lü

2019

Numerical Meth Engineering

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Summary In this article, hierarchical surrogate model combined with dimensionality reduction technique is investigated for uncertainty propagation of high‐dimensional problems. In the proposed method, a low‐fidelity sparse polynomial chaos expansion model is first constructed to capture the global trend of model response and exploit a low‐dimensional active subspace (AS). Then a high‐fidelity (HF) stochastic Kriging model is built on the reduced space by mapping the original high‐dimensional input onto the identified AS. The effective dimensionality of the AS is estimated by maximum likelihood estimation technique. Finally, an accurate HF surrogate model is obtained for uncertainty propagation of high‐dimensional stochastic problems. The proposed method is validated by two challenging high‐dimensional stochastic examples, and the results demonstrate that our method is effective for high‐dimensional uncertainty propagation.

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Section: Methodsmentioning

confidence: 99%

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

Cheng

Lü

2019

Numerical Meth Engineering

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“…where r(x * ) =θ c ) = [R(x * − x (1) |θ c ), ..., R(x * − x (Nc) |θ c )] T is the cross-correlation vector between the point x * and each of the points of X c . In this study, we use a nested combination of surrogate models to define a HK surrogate [23]. Specifically, a PCE surrogate of the LF simulator is used as a trend for the HF simulator.…”

Section: R a F Tmentioning

confidence: 99%

Seismic Fragility Analysis based on Artificial Ground Motions and Surrogate Modeling of Validated Structural Simulators

Abbiati

Broccardo

Abdallah

et al. 2020

Preprint

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This study introduces a computational framework for efficient and accurate seismic fragility analysis based on a combination of artificial ground motion modeling, polynomial-chaos-based global sensitivity analysis, and hierarchical kriging surrogate modeling. The framework follows the philosophy of the Performance-Based Earthquake Engineering PEER approach, where the fragility analysis is decoupled from hazard analysis. This study addresses three criticalities that are present in the current practice. Namely, reduced size of hazard-consistent size-specific ensembles of seismic records, validation of structural simulators against large-scale experiments, high computational cost for accurate fragility estimates. The effectiveness of the proposed framework is demonstrated for the Rio Torto Bridge, recently tested using hybrid simulation within the RETRO project.

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“…However, surrogate modeling is increasingly also proposed for basic structural analysis due to the large number of environmental states that need to be checked for certification according to design standards (e.g., International Electrotechnical Commission, 2009). For example, Toft et al (2016a) used a response surface based on Taylor expansions, and Gaussian process regression was used by Huchet et al (2019) and Teixeira et al (2019) for fatigue design and by Abdallah et al (2019) for ultimate limit state (ULS) design. Though there are some challenges regarding the number of samples required to build an accurate model, this can be alleviated by efficient design of experiment and/or adaptive methods.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

Stieng

Muskulus

2020

Wind Energ. Sci.

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Abstract. The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in the development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a general methodology that implements recent developments in gradient-based design optimization, in particular the use of analytical gradients, within the context of reliability-based design optimization methods. Gradient-based optimization is typically more efficient and has more well-defined convergence properties than gradient-free methods, making this the preferred paradigm for reliability-based optimization where possible. By an assumed factorization of the uncertain response into a design-independent, probabilistic part and a design-dependent but completely deterministic part, it is possible to computationally decouple the reliability analysis from the design optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic response, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic part can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and constraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal support structure designs and furthermore show that in practice only a limited amount of additional computational effort is required compared to deterministic design optimization. While there are some limitations in the applied cases, and some further refinement might be necessary for applications to high-fidelity design scenarios, the demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore wind turbine support structures is feasible.

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Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators — Application to extreme loads on wind turbines

Cited by 51 publications

References 36 publications

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

Hierarchical surrogate model with dimensionality reduction technique for high‐dimensional uncertainty propagation

Seismic Fragility Analysis based on Artificial Ground Motions and Surrogate Modeling of Validated Structural Simulators

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling

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