Analysis of the design of experiments of offshore wind turbine fatigue reliability design with Kriging surfaces

Teixeira, Rui; O’Connor, Alan; Nogal, María; Krishnan, N.; Nichols, Joseph W.

doi:10.1016/j.prostr.2017.07.132

Cited by 22 publications

(20 citation statements)

References 11 publications

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“…The uncertainty is related directly to the load effect, similar to and , and is applied alongside these as shown in Eq. (10).…”

Section: Limit State Equationmentioning

confidence: 99%

“…In addition, they investigated the influence of the computational effort (number of samples and seeds) used to calibrate the surrogate model. With focus on offshore wind turbine fatigue loads Teixeira et al [10] used a Kriging model to analyse the importance of different wind and wave climate parameters. Murcia et al [11] used the uncertainty propagation properties of PCE to analyse the sensitivity of the wind climate on the power output and structural response of an onshore turbine.…”

Section: Introductionmentioning

confidence: 99%

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Surrogate model uncertainty in wind turbine reliability assessment

et al. 2020

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Lowering the cost of wind energy entails the optimization of wind turbine material consumption without compromising structural safety. Traditionally, wind turbines are designed by the partial safety factor method, which is calibrated by probabilistic models and presented in the IEC 61400-1 design standard. This approach significantly reduces the amount of aeroelastic simulations required to assess the fatigue limit state of wind turbines, but it may lead to inconsistent reliability levels across wind farm projects. To avoid this, wind turbines may be designed by probabilistic methods using surrogate models to approximate fatigue load effects. In this approach, it is important to quantify and model all relevant uncertainties including that of the surrogate model itself. Here we quantify this uncertainty according to Eurocode 1990 for polynomial chaos expansion (PCE) and Kriging using wind data from 99 real sites and the 5MW reference turbine designed by NREL. We investigate a wide range of simulation efforts to train the surrogate models. Our results show that Kriging yields a higher accuracy per invested simulation compared to PCE. This improved understanding of utilizing PCE and Kriging in fatigue reliability assessment may significantly benefit decision support in probabilistic design of wind turbines.

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“…The uncertainty is related directly to the load effect, similar to and , and is applied alongside these as shown in Eq. (10).…”

Section: Limit State Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Surrogate model uncertainty in wind turbine reliability assessment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Regarding the statistical characterization of the t damage, Teixeira et al (2017b) showed that in an OWT follows a lognormal distribution. This assumption is of relevance to estimate confidence intervals in the mean using a Kriging model.…”

Section: Fatigue Analysis Of Owt Towersmentioning

confidence: 99%

“…The initial sample size is generated selecting subset points of a Latin Hypercube Sampling scheme in (Θ), with addition of extreme occurrences of Θ. Θ is defined as function of the wind velocity (U) and turbulence intensity (I). Teixeira et al (2017b) showed these to be the most influential variables in the baseline turbine tower design.…”

Section: Confidence Interval Interpolationmentioning

confidence: 99%

On the suitability of the generalized Pareto to model extreme waves

Teixeira

Nogal

O’Connor

2018

Journal of Hydraulic Research

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For complex systems, the applicability of surrogate models has shown the potential to enable accurate assessments using a reduced batch of data and to compile information about large datasets. These behave as black-box functions that replace a series of inputs/outputs. In the present work, a Kriging surrogate is used to predict confidence intervals in an offshore wind turbine tower fatigue design. Uncertainty in fatigue due to loading is highly connected to the mean. One year operational fatigue results is used to validate the results. The Kriging is applied to replicate the yearly states of operation, and successfully predicts intervals of confidence for the long-term fatigue design. Regarding the interest of data analysis, the approach implemented is characterized by its flexibility and capability of approaching any problem that can be characterized by a single variable. Being therefore an interesting tool in decision schemes where large datasets are available or prediction of unknown outputs is required.

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“…Figure 1 presents the uncertainty in the calculation of the mean using 8 repetitions ( = 8) of six different seeds for the cumulative damage calculation. The mean wind speed (U) and the turbulence intensity (I) were selected to compute the results as these are expected to be the most influential variables in terms of fatigue of the tower [8]. It can be seen in Figure 1 that the variability of the mean within the calculations can be relatively high when using 6 seeds.…”

mentioning

confidence: 99%

On the Calculation of Offshore Wind Turbine Load Spectra for Fatigue Design

Teixeira

Nogal

O’Connor

2019

Structural Integrity

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The present work researches on the definition of the load spectra used for offshore wind turbine low SN slope materials' fatigue design. Uncertainty in the sample sized used to scale fatigue life is analyzed for the tower component. Damage density is investigated for different environmental conditions in order to understand the importance of the different regimes of operation. Damage density is identified to be a heterogeneous function of the loading environmental conditions. In some cases, even for low SN slope materials, most of the damage occurs due to high load ranges. To study on the influence of this heterogeneity, different statistical tail fits are used to compare the influence of accurately defining the tail region on a reference design time ( ). Results show that OWT fatigue is highly dependent on the shorter that time used to approximate . This is mainly related to the fact that fatigue design depends not only on scaling stress ranges, but also cycle counts. Effort on the design phase should be applied in the definition of the uncertainty of the load spectra due to the limitation imposed by using low sample sizes to cover the extensive joint distribution of environmental parameters.

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Analysis of the design of experiments of offshore wind turbine fatigue reliability design with Kriging surfaces

Cited by 22 publications

References 11 publications

Surrogate model uncertainty in wind turbine reliability assessment

Surrogate model uncertainty in wind turbine reliability assessment

On the suitability of the generalized Pareto to model extreme waves

On the Calculation of Offshore Wind Turbine Load Spectra for Fatigue Design

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