On the suitability of the generalized Pareto to model extreme waves

Teixeira, Rui; Nogal, María; O’Connor, Alan

doi:10.1080/00221686.2017.1402829

Cited by 15 publications

(10 citation statements)

References 39 publications

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“…To cover the DoE, real data presented in Teixeira et al (2018) from the M6 buoy was used to ensure a realistic approximation to the DoE environmental variables. The distributions for each input variable used in the modeling are shown in Table 1.…”

Section: P-gsa Design Of Experimentsmentioning

confidence: 99%

Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

2019

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Characterizing uncertainty in complex systems is steadily growing as a topic of interest. One of the efficient ways to characterize a complex system is achieved by probabilistic sensitivity analysis. In the context of performing it, there are a limited number of methods to quantify the change of the output to its full probabilistic extent. Moreover, in some engineering applications, such as reliability analysis, some established indicators of sensitivity do not fit the best interest of the analysis. This is the case of Kullback-Leibler divergence. Despite applied for probabilistic sensitivity analysis, it has limited interest in certain circumstances. A transformation of this indicator of entropy between two distributions is proposed in the present work. This transformation is used to establish a complementary indicator that is more perceptive, and more efficient for reliability analysis. This new function is applied for the global sensitivity analysis of an offshore wind turbine on a monopile foundation. Results show that, for engineering problems as the one presented, the usage of this transformed indicator produces intuitive results. It allows the efficient identification of relevant states of operation as well as the most influent variables in the design of experiments, resulting in better comprehension of system's behaviour and operational risks.

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Section: P-gsa Design Of Experimentsmentioning

confidence: 99%

Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

2019

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“…Truncation of the loading data set allows extrapolation of loads and cycles and avoids the distortion introduced by low Δ L . Teixeira et al present a comprehensive review on the considerations required when analyzing truncated data for a physical variable. It is also noted that the topic of extrapolation of loads for OWT has been extensively studied in the past, however, mainly for the cases of extreme loading characterization and extrapolation of time‐series of measured loading data …”

Section: Comparative Sn Fatigue Design Proceduresmentioning

confidence: 99%

“…Different reference simulation cases, x , function of 2m/s U bins, are analyzed. The data of Teixeira et al were applied to characterize the relevant loading cases. Values of significant wave height ( H s ), peak period ( T p ), and turbulence intensity ( I ) considered for each binned U are presented in Table .…”

Section: Comparative Sn Fatigue Design Proceduresmentioning

confidence: 99%

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

2019

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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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“…The Peak-Over-Threshold (POT) methodology is applied and loading quantiles of 95% and 99% as threshold (u) value. Extensive considerations about the POT procedure are given in [10]. The choice of these quantiles, previously identified as inappropriate [9], is due the systematic character required for the procedure.…”

Section: 1mentioning

confidence: 99%

“…Characterization of the load spectra tail can be performed through extreme value theory or data truncation, being the second well accepted for wind engineering [3]. The challenge of extrapolation with truncation of data has been widely discussed before [3,9,10]. It is commonly assumed that low SN materials do not require the usage of extrapolation techniques to fit the tail distribution.…”

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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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On the suitability of the generalized Pareto to model extreme waves

Cited by 15 publications

References 39 publications

Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback-Leibler divergence

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

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

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