LH-moment estimation for statistical analysis on the wave crest distributions of a deepwater spar platform model test

Longfei Xiao, Haining Lu, Longbin Tao, Lijun Yang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The design of fixed and compliant offshore platforms requires the reliable estimation of extreme values with small probabilities of exceedance based on an appropriate probability distribution. The Weibull distribution is commonly utilised for the statistical analysis of wave crests, including near-field wave run-ups. The parameters are estimated empirically from experimental or onsite measurements. In this paper, the data set of wave crests from a Spar model test was statistically analysed by using the method of LH-moments for parameter estimation of the Weibull distribution. The root-mean-square errors (RMSEs) and the error of LH-kurtosis were used to examine the goodness-of-fit. The results for the first four LH-moments, the estimated parameters, and the probability distributions showed that the level of the LH-moments has a significant influence. At higher levels, the estimation results gave a more focused representation of the upper part of the wave crest distributions, which indicates consistency with the intention of the method of LH-moments. The low tail RMSE values of less than 2.5% demonstrated that a Weibull distribution model estimated by using high-level LH-moments can accurately represent the probability distribution of large extreme wave crests for incident waves, wave run-ups, and moon pool waves. Goodness-of-fit test on the basis of comparison of sampling LH-kurtosis and theoretical LH-kurtosis was recommended as a procedure for selecting an optimum level.

LanguageEnglish
Pages15-33
Number of pages19
JournalMarine Structures
Volume52
Early online date23 Nov 2016
DOIs
Publication statusPublished - 31 Mar 2017

Fingerprint

Spar platforms
Statistical methods
Weibull distribution
Probability distributions
Mean square error
Moon
Parameter estimation
Sampling

Keywords

  • LH-moments
  • parameter estimation
  • spar platform
  • wave crest
  • Weibull distribution

Cite this

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title = "LH-moment estimation for statistical analysis on the wave crest distributions of a deepwater spar platform model test",
abstract = "The design of fixed and compliant offshore platforms requires the reliable estimation of extreme values with small probabilities of exceedance based on an appropriate probability distribution. The Weibull distribution is commonly utilised for the statistical analysis of wave crests, including near-field wave run-ups. The parameters are estimated empirically from experimental or onsite measurements. In this paper, the data set of wave crests from a Spar model test was statistically analysed by using the method of LH-moments for parameter estimation of the Weibull distribution. The root-mean-square errors (RMSEs) and the error of LH-kurtosis were used to examine the goodness-of-fit. The results for the first four LH-moments, the estimated parameters, and the probability distributions showed that the level of the LH-moments has a significant influence. At higher levels, the estimation results gave a more focused representation of the upper part of the wave crest distributions, which indicates consistency with the intention of the method of LH-moments. The low tail RMSE values of less than 2.5{\%} demonstrated that a Weibull distribution model estimated by using high-level LH-moments can accurately represent the probability distribution of large extreme wave crests for incident waves, wave run-ups, and moon pool waves. Goodness-of-fit test on the basis of comparison of sampling LH-kurtosis and theoretical LH-kurtosis was recommended as a procedure for selecting an optimum level.",
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LH-moment estimation for statistical analysis on the wave crest distributions of a deepwater spar platform model test. / Xiao, Longfei; Lu, Haining; Tao, Longbin; Yang, Lijun.

In: Marine Structures, Vol. 52, 31.03.2017, p. 15-33.

Research output: Contribution to journalArticle

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