Estimating wave crest distributions using the method of L-moments

Abstract The design of fixed or floating offshore structures requires accurate information of the met-ocean data at the intended offshore site. In the design process it is recognized that this environmental data is modified in the near-field by the interaction with the particular geometrical configuration of the offshore structure. This transformation of the incident wave field around and beneath an offshore structure presents a challenge for ocean engineers when specifying the wave gap elevation to avoid impact loads on the underside of the deck and inundation of the topsides. Thus, the accurate estimation of the wave crest distributions from measurements at various locations near and under the offshore structure during model test studies is essential. A semi-empirical approach is presented herein that builds upon the findings of previous studies and introduces the Method of L-moments. A three parameter model for a wave crest probability distribution function is presented and explicit relationships between the parameters of the distribution and its’ first three L-Moments are established. Furthermore, three narrow-band models from earlier research studies are reviewed and compared with the new model. Wave measurements from a mini-TLP model test program are used as the basis for comparison of the four distributions. The root-mean-square error is used as a metric to quantify the overall fit of the data and its accuracy in the high end tail of the data. The L-Moment model is shown to be more robust in representing the data in both the far-field and beneath the deck of the mini-TLP where the wave field demonstrates increased non-linear behavior.

[1]  John M. Niedzwecki,et al.  An Experimental Research Study of a Mini-TLP , 2001 .

[2]  M. A. Tayfun,et al.  Distribution of nonlinear wave crests , 2002 .

[3]  Marc Prevosto,et al.  Statistics of Wave Crests From Models vs. Measurements , 2002 .

[4]  M. A. Tayfun,et al.  Narrow-band nonlinear sea waves , 1980 .

[5]  D. Kriebel,et al.  Nonlinear Effects on Wave Groups in Random Seas , 1991 .

[6]  J. R. Wallis,et al.  Estimation of the generalized extreme-value distribution by the method of probability-weighted moments , 1985 .

[7]  J. Hosking,et al.  Parameter and quantile estimation for the generalized pareto distribution , 1987 .

[8]  M. Arhan,et al.  Non-linear deformation of sea-wave profiles in intermediate and shallow water , 1981 .

[9]  Geir Moe,et al.  Frequency of maxima of non-narrow banded stochastic processes , 2005 .

[10]  M. Longuet-Higgins,et al.  The statistical distribution of the maxima of a random function , 1956, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[11]  G. Forristall Wave Crest Distributions: Observations and Second-Order Theory , 2000 .

[12]  M. A. Tayfun,et al.  Statistics of nonlinear wave crests and groups , 2006 .

[13]  Thomas H. Dawson,et al.  Nonlinearity in Wave Crest Statistics , 1994 .

[14]  G. Forristall On the statistical distribution of wave heights in a storm , 1978 .

[15]  M. Tayfun Distributions of envelope and phase in weakly nonlinear random waves , 1994 .

[16]  Hoon Sohn,et al.  Parameter estimation of the generalized extreme value distribution for structural health monitoring , 2006 .

[17]  M. S. Longuet-Higgins,et al.  On the distribution of the heights of sea waves: Some effects of nonlinearity and finite band width , 1980 .

[18]  N. Huang,et al.  A non-Gaussian statistical model for surface elevation of nonlinear random wave fields , 1983 .

[19]  J. Hosking L‐Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics , 1990 .

[20]  Billy L. Edge,et al.  Ocean Wave Measurement and Analysis , 1994 .

[21]  J. Vrijling,et al.  The estimation of extreme quantiles of wind velocity using L-moments in the peaks-over-threshold approach , 2001 .

[22]  J. R. Wallis,et al.  Regional Frequency Analysis: An Approach Based on L-Moments , 1997 .