Probability distributions of wave run-up on a TLP model

Izadparast, Amir H.; Niedzwecki, John M.

doi:10.1016/j.marstruc.2010.03.002

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…The probability density function (PDF) f(x) of the complete three-parameter Weibull distribution (Izadparast and Niedzwecki 2010) is given by…”

Section: Weibull Distribution and Its Lh-momentsmentioning

confidence: 99%

“…The cumulative distribution function (CDF) F(x) and quantile function x(F) (Izadparast and Niedzwecki 2010) are written as…”

Section: Weibull Distribution and Its Lh-momentsmentioning

confidence: 99%

“…The vessel vertical motions of the compliant platform may significantly influence the wave run-ups (Izadparast and Niedzwecki 2010). Table 3 lists standard deviations of heave, roll, and pitch at the COG and vessel vertical motions at six wave probes.…”

Section: Basic Statisticsmentioning

confidence: 99%

See 2 more Smart Citations

Probability Analysis of Wave Run-Ups and Air Gap Response of a Deepwater Semisubmersible Platform Using LH-Moments Estimation Method

Xiao

Lü

et al. 2016

J. Waterway, Port, Coastal, Ocean Eng.

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This version is available at https://strathprints.strath.ac.uk/62977/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the AbstractThe air gap response in harsh environments, a critical design issue for offshore platforms, is related to the wave run-ups due to wave-platform interactions and potentially results in serious wave impacts. Therefore, the reliable prediction of wave run-ups and air gap response in harsh environments is a challenging task and needs further study. In this study, probability analysis of the wave run-up data from an experimental study of a deepwater semi-submersible platform is conducted based on the three-parameter Weibull distribution model using the LH-moments method for parameter estimation.One of the highlights in the present study is that the explicit relationships between the first three LH-moments at arbitrary levels and the parameters of the Weibull distribution are established analytically. The accuracy of LH-kurtosis estimation is proposed to determine the appropriate level for probability analysis. The air gap response is more serious in quartering and beam seas than in head seas.In front of the columns along the wave incoming direction, especially the aft one, the wave run-ups show higher probability distributions leading to higher likeliness of suffering from negative air gap and wave impact accidents than the other platform areas. At the platform center, the wave run-up is significantly lower than the incident wave. The probability distributions based on LH-moments at the appropriate level can well represent large wave run-ups, except for that beyond the still-water air gap, where both measurement methods and probability analyses warrant further research.

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“…The probability density function (PDF) f(x) of the complete three-parameter Weibull distribution (Izadparast and Niedzwecki 2010) is given by…”

Section: Weibull Distribution and Its Lh-momentsmentioning

confidence: 99%

“…The cumulative distribution function (CDF) F(x) and quantile function x(F) (Izadparast and Niedzwecki 2010) are written as…”

Section: Weibull Distribution and Its Lh-momentsmentioning

confidence: 99%

See 1 more Smart Citation

Probability Analysis of Wave Run-Ups and Air Gap Response of a Deepwater Semisubmersible Platform Using LH-Moments Estimation Method

Xiao

Lü

et al. 2016

J. Waterway, Port, Coastal, Ocean Eng.

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“…In this paper a recently developed method for esti mating extreme value statistics [1] will be applied to the airgap problem for TLPs subjected to random seas. This paper can be seen as continuing the efforts in the challenging study of the airgap issue for offshore platforms [2][3][4][5][6][7][8][9][10], Prediction of a minimum airgap in harsh environments and of the probability of deck impact events is a challenging task due to strong nonlinear phe nomena. This was demonstrated in Ref.…”

Section: Introductionmentioning

confidence: 98%

Airgap Statistics for a Tension Leg Platform

Gaidai

Næss²,

Stansberg

2014

Journal of Offshore Mechanics and Arctic Engineering

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The paper discusses a method for estimating extreme value statistics of the airgap for floating offshore platforms subjected to random seas. It is an adaptation of a recently developed method, which is based on the mean upcrossing rate (MUR) function for uni-variate time series combined with an optimization procedure that allows prediction at extreme response levels by extrapolation. Extensive model tests were performed in a large wave basin for a tension leg platform (TLP) operating in the Norwegian Sea. Among several critical parameters, the airgap was measured at a number of locations under the platform deck. The wave in deck impact is a critical safety issue with respect to the deck damage and occurrence of extreme tether tensions. The authors have utilized experimental data to look at critical airgaps under the deck in random waves. Conclu-sions are drawn about extreme airgap statistics, and consequently about the wave impact probability in severe seas. [DOI: 10.1115/1.4028734

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“…The results obtained show that the model has better accuracy compared with other non-linear models but again deviations for smaller wave amplitudes were noted. Izadparast and Niedzwecki [16] modified the analytical form of the probability distribution for wave run-up by introducing a third empirical parameter with a physical interpretation and all the three-parameters can be estimated directly from the data by utilizing the method of L-moments [17]. The accuracy of the three-parameter Weibull distribution model for describing the run-up measurements of a compliant platform was validated by comparison to experimental data and that predicted by other theoretical models.…”

Section: Introductionmentioning

confidence: 99%