Joint met-ocean description for design and operations of marine structures

Bitner‐Gregersen, Elzbieta M.

doi:10.1016/j.apor.2015.01.007

Cited by 98 publications

(39 citation statements)

References 18 publications

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“…Examples of this approach are given by Madsen et al [6], Winterstein et al [7], Winterstein and Engebretsen [8], and Winterstein et al [9]. Of particular relevance to this paper are the joint models developed by Bitner-Gregersen [10] for the same data sets as used in this study.…”

Section: Introductionmentioning

confidence: 91%

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Ewans

Jameson²

2015

Applied Ocean Research

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Section: Introductionmentioning

confidence: 91%

“…Joint probability models for these three data sets are given by Bitner-Gregersen [10], following the RAA. In this paper the data sets have been analysed directly, analogous to the DSA.…”

Section: Introductionmentioning

confidence: 99%

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Ewans

Jameson²

2015

Applied Ocean Research

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“…In many marine engineering applications there will be several environmental parameters that need to be taken into account jointly (Bitner-Gregersen 2015) and estimates of multivariate extremes associated with long return periods will be needed. Obviously, this would complicate the picture even more, and there are even different ways of defining what is meant by a multivariate extreme.…”

Section: Discussionmentioning

confidence: 99%

Uncertainties in extreme value modelling of wave data in a climate change perspective

Vanem

2015

J. Ocean Eng. Mar. Energy

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The extreme values of wave climate data are of great interest in a number of different applications, including the design and operation of ships and offshore structures, marine energy generation, aquaculture and coastal installations. Typically, the return values of certain met-ocean parameters such as significant wave height are of particular importance. In a climate change perspective, projections of such return values to a future climate are of great importance for risk management and adaptation purposes. However, there are various ways of estimating the required return values, which introduce additional uncertainties in extreme weather and climate variables pertaining to both current and future climates. Many of these approaches are investigated in this paper by applying different methods to particular data sets of significant wave height, corresponding to the historic climate and two future projections of the climate assuming different forcing scenarios. In this way, the uncertainty due to the extreme value analysis can also be compared to the uncertainty due to a changing climate. The different approaches that are considered in this paper are the initial distribution approach, the block maxima approach, the peak over threshold approach and the average conditional exceedance rate method. Furthermore, the effect of different modelling choices within each of the approaches will be explored. Thus, a range of different return value estimates for the different data sets is obtained. This exercise reveals that the uncertainty due to the extreme value analysis method is notable and, as expected, the variability of the estimates increases for higher return periods. Moreover, even though the variability due to the extreme value analysis is greater than the climate variability, a shift towards higher extremes in a future wave climate

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“…The probability distribution is determined for each variable, with information on mean, variance and standard deviation [12]. Joint probability distribution is advantageous for structural reliability analysis (SRA) because it helps to forecast of limit state function for structural damage [3]. The physics of offshore metocean parameters indicate that these variables should undergo multivariate analysis as compared with the values indicated by a single variable [10].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The wind, wave and current are considered to be random variables and may differ with time [1][2][3][4][5][6][7][8][9]. The jacket platforms are designed for extreme load conditions this is to make sure its structural reliability throughout its design life [10].…”

Section: Literature Reviewmentioning

confidence: 99%

Determination of Correlation for Extreme Metocean Variables

et al. 2017

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Abstract. Metocean environmental load includes wind, wave and currents. Offshore structures are designed for two environmental load design conditions i.e. extreme and operational load conditions of environmental loads are evaluated. The ccorrelation between load variables using Joint probability distribution, Pearson correlation coefficient and Spearman's rank correlation coefficients methods in Peninsular Malaysia (PM), Sabah and Sarawak are computed. Joint probability distribution method is considered as a reliable method among three different methods to determine the relationship between load variables. The PM has good correlation between the wind-wave and wave-current; Sabah has both strong relationships of wind-wave and wind-current with 50 year return period; Sarawak has good correlation between wind and current in both 50 years and 100 years return period. Since Sabah has good correlation between the associated load variables, no matter in 50 years or 100 years of return period of load combination. Thus, method 1 of ISO 19901-1, specimen provides guideline for metocean loading conditions, can be adopted for design for offshore structure in Sabah. However, due to weak correlations in PM and Sarawak, this method cannot be applied and method 2, which is current practice in offshore industry, should continueto be used.

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Joint met-ocean description for design and operations of marine structures

Cited by 98 publications

References 18 publications

Availability of offloading from an LNG barge

Availability of offloading from an LNG barge

Uncertainties in extreme value modelling of wave data in a climate change perspective

Determination of Correlation for Extreme Metocean Variables

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