Stress-cycle fatigue design with Kriging applied to offshore wind turbines

Teixeira, Rui; Nogal, María; O’Connor, Alan; Nichols, Joseph W.; Dumas, Antoine

doi:10.1016/j.ijfatigue.2019.04.012

Cited by 30 publications

(13 citation statements)

References 34 publications

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“…In other words, surrogate models mimic the behaviour of the simulation model as closely as possible while remaining computationally cheaper to evaluate. Various surrogate modelling approaches have been recently proposed in the literature for OWTs on monopile foundations, including Gaussian Process (GP) regression (8)(9)(10)(11) and for onshore wind turbines using Polynomial Chaos Expansion (12,13). These models significantly improve the computational efficiency of FLS assessment, and therefore also enable structural reliability assessment and the development of probabilistic risk models for OWTs to predict the potential financial impact of OWT failures.…”

Section: Dlcmentioning

confidence: 99%

Impact of climate-change scenarios on offshore wind turbine structural performance

Wilkie

Galasso

2020

Renewable and Sustainable Energy Reviews

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Section: Dlcmentioning

confidence: 99%

Impact of climate-change scenarios on offshore wind turbine structural performance

Wilkie

Galasso

2020

Renewable and Sustainable Energy Reviews

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“…As highlighted previously, in recent years, research on wind turbine SN fatigue loading has addressed detailed aspects that introduce uncertainty in the SN fatigue assessment, and less focus has been given to the probability theory assumptions related to the loading spectra characterization, such as effect of the turbulence length scales on the loading spectra, atmospheric stability, hydrodynamic theories, or the (design effort) cost of the design procedure …”

Section: Owt Sn Fatigue Designmentioning

confidence: 99%

Analysis of long‐term loading characterization for stress‐cycle fatigue design

2019

Self Cite

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The present paper discusses the application of stress‐cycle (SN) fatigue design procedures for offshore wind turbines, applied to the tower and substructure, and emphasizing the long‐term loading characterization. Three main methodologies are compared to assess SN fatigue long‐term design, direct scale‐up of loads and cycles to the design lifetime, probability fit for the loading distribution, and truncation and fit of the tail region. Different characteristic SN slopes are researched. The comprehensive comparison developed shows that limited interest exists in increasing the complexity of the loading assessment for SN fatigue design. In particular, the notion of representative sample has key influence on the fatigue calculations. Probability analysis techniques are not an adequate substitute of a representative sample. If definition of a representative sample for design is unpractical due to its large cost, direct scale‐up of cycles from a time t to a larger time T can be applied with uncertainty assessment. Bootstrapping of load ranges is implemented to tackle the limitations imposed by approaching a design time much larger than the evaluated time. It takes advantage of the fact that SN fatigue design is a statistical problem of mean value. Bootstrapping proved to be an efficient estimator of uncertainty with relatively constant confidence bounds, of particular interest to perform SN fatigue design with small samples. New insights on its application for fatigue design are presented. Due to its simple application and nonparametric character, it can be applied in combination with other techniques, such as extrapolation of loads and cycles.

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“…For reliability analysis of stochastic structures, surrogate models such as: the response surface method [ 18 , 19 , 20 ]; support vector machine (SVM) [ 21 , 22 , 23 ]; kriging model [ 24 , 25 , 26 ]; artificial neural network (ANN) [ 16 ], etc., have all been employed in various industries. One efficient method with good robustness used for the approximation of nonlinear functions and small samples is, least squares support vector regression (LS-SVR), which is a widely used application in structural reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

A New Approach for Fatigue Reliability Analysis of Thin-Walled Structures with DC-ILSSVR

Dai

et al. 2021

Materials

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Fatigue analysis is of great significance for thin-walled structures in the spacecraft industry to ensure their service reliability during operation. Due to the complex loadings of thin-walled structures under thermal–structural–acoustic coupling conditions, the calculation cost of finite element (FE) simulations is relatively expensive. To improve the computational efficiency of dynamic reliability analysis on thin-walled structures to within acceptable accuracy, a novel probabilistic approach named DC-ILSSVR was developed, in which the rotation matrix optimization (RMO) method was used to initially search for the model parameters of least squares support vector regression (LS-SVR). The distributed collaborative (DC) strategy was then introduced to enhance the efficiency of a component suffering from multiple failure modes. Moreover, a numerical example with respect to thin-walled structures was used to validate the proposed method. The results showed that RMO performed on LS-SVR model parameters provided competitive prediction accuracy, and hence the reliability analysis efficiency of thin-walled pipe was significantly improved.

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Stress-cycle fatigue design with Kriging applied to offshore wind turbines

Cited by 30 publications

References 34 publications

Impact of climate-change scenarios on offshore wind turbine structural performance

Impact of climate-change scenarios on offshore wind turbine structural performance

Analysis of long‐term loading characterization for stress‐cycle fatigue design

A New Approach for Fatigue Reliability Analysis of Thin-Walled Structures with DC-ILSSVR

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