Environmental contours using copulas

Montes-Iturrizaga, R.; Heredia‐Zavoni, Ernesto

doi:10.1016/j.apor.2015.05.007

Cited by 76 publications

(24 citation statements)

References 19 publications

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“…Also the estimated copula parameters are not only within the theoretical copula's range (see [21]) but seem to compare reasonably well with the ones used in copulas application to metocean data, e.g. [11], [45] or [46]. However, these works only include symmetric models, which leaves space for the present technique to be tested, possibly leading to fitting improvements in the datasets related to the mentioned works.…”

Section: Discussion and Applications To Wind Engineeringmentioning

confidence: 59%

“…Thus, several spectral parameters modelling and reliability problems studied under the use of simple Archimedean or elliptical copulas (e.g. Antão and Soares, 2016; Montes-Iturrizaga and Heredia-Zavoni, 2015, 2016; Redón-Conde and Heredia-Zavoni, 2015) can improve their accuracy with the use of the same copula with extra-parameters. In the next section, the extra-parametrization of copulas is introduced and compared with the results obtained in the symmetric models (Table 7).…”

Section: Copula Models Applied To the Significant Wave Height And Thementioning

confidence: 99%

“…Therefore, a corollary from this observation is that when using copula models applied to the significant wave height and a characteristic wave period, the extra-parametrization is a reasonable way to try to improve the results, obtained from symmetric copulas, which are still the main copulas applied in the ocean modelling literature (e.g. Jane et al, 2016; Montes-Iturrizaga and Heredia-Zavoni, 2015; Redón-Conde and Heredia-Zavoni, 2015).…”

Section: Tail Dependence Analysismentioning

confidence: 99%

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Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

et al. 2018

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Joint statistical models for long-term wave climate are a key aspect of offshore wind engineering design. However, to find a joint model for sea-state characteristics is often difficult due to the complex nature of the wave climate and the physical constraints of seastate phenomena. The available records of wave heights and periods are often very asymmetric in their nature. This paper presents a copula-based approach to obtain the joint cumulative distribution function of significant wave heights and the mean up-crossing periods. This study is based on 124 months hindcast data concerning Horns Rev 3 offshore wind farm. The extra-parametrization technique of symmetric copulas is implemented to account for the asymmetry present in the data. The analysis of the total sea, the wind-sea and primary swell components is performed separately. The results show that the extraparametrization technique with pairwise copulas consistently provided a better goodnessof-fit when compared to symmetric copulas. Moreover, it is demonstrated that the separation of the total sea into its components does not always improve the extraparametrized copula's performance. Furthermore, this paper also discusses copula application to offshore wind engineering.

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Section: Discussion and Applications To Wind Engineeringmentioning

confidence: 59%

Section: Copula Models Applied To the Significant Wave Height And Thementioning

confidence: 99%

Section: Tail Dependence Analysismentioning

confidence: 99%

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Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

et al. 2018

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“…Other approached for environmental contours exist, see e.g. [10,11,12,13,14,15,16,17] or the recent review in [18]. In this paper various approaches to environmental contours will be used, and the results from an approximate long-term analysis using such contours will be compared to an approximate long-term analysis based on a statistical response emulator.…”

Section: Introductionmentioning

confidence: 99%

Comparing different contour methods with response-based methods for extreme ship response analysis

et al. 2020

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Environmental contours are often applied in probabilistic structural reliability analysis to identify extreme environmental conditions that may give rise to extreme loads and responses. They facilitate approximate long term analysis of critical structural responses in situations where computationally heavy and time-consuming response calculations makes full long-term analysis infeasible. The environmental contour method identifies extreme environmental conditions that are expected to give rise to extreme structural response of marine structures. The extreme responses can then be estimated by performing response calculations for environmental conditions along the contours. Response-based analysis is an alternative, where extreme value analysis is performed on the actual response rather than on the environmental conditions. For complex structures, this is often not practical due to computationally heavy response calculations. However, by establishing statistical emulators of the response, using machine learning techniques, one may obtain long time-series of the structural response and use this to estimate extreme responses. In this paper, various contour methods will be compared to response-based estimation of extreme vertical bending moment for a tanker. A response emulator based on Gaussian processes regression with adaptive sampling has been established based on response calculations from a hydrodynamic model. Long timeseries of sea-state parameters such as significant wave height and wave period are used to construct Nyear environmental contours and the extreme N-year response is estimated from numerical calculations for identified sea states. At the same time, the response emulator is applied on the time series to provide long time-series of structural response, in this case vertical bending moment of a tanker. Extreme value analysis is then performed directly on the responses to estimate the N-year extreme response. The results from either method will then be compared, and it is possible to evaluate the accuracy of the environmental contour method in estimating the response. Moreover, different contour methods will be compared.

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“…In particular, the direct sampling contours will by definition always enclose a convex set, whereas the IFORM contours will tend to follow the scatter of the data more closely. Other approached for environmental contours exist, see, eg, previous studies . The most common applications of environmental contours are restricted to two‐dimensional problems and with only linear variables, although generalizations to higher dimensions are, in principle, straightforward .…”

Section: Introductionmentioning

confidence: 99%

Environmental contours for circular‐linear variables based on the direct sampling method

2019

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Environmental contours are often used in the design of engineering structures to identify extreme environmental conditions that may give rise to extreme loads and responses. The perhaps most common application of environmental contours is for wave climate variables such as significant wave height and wave period. However, for the design of wind energy installations, the joint distribution of wind speed and wind direction may be equally important. In this case, joint modelling of linear (wind speed) and circular (wind direction) variables are needed, and methods for establishing environmental contours for circular-linear variables will be required.In this paper, different ways of establishing environmental contours for circular-linear variables will be presented and applied to a joint distribution model for wind speed and wind direction.In particular, the direct sampling approach to environmental contours will be modified to the case where one of the variables is cyclic. In addition, contours based on exceedance planes in polar coordinates will be established, and circular-linear contours will also be calculated based on the inverse FORM (I-FORM) approach.

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Environmental contours using copulas

Cited by 76 publications

References 19 publications

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Asymmetric copula–based distribution models for met-ocean data in offshore wind engineering applications

Comparing different contour methods with response-based methods for extreme ship response analysis

Environmental contours for circular‐linear variables based on the direct sampling method

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