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Wave analysis by Slepian models

Lindgren, Georg LU orcid (2000) In Probabilistic Engineering Mechanics 15(1). p.49-57
Abstract
Ocean waves exhibit more or less a Gaussian distribution for the instantaneous water surface height, and there is a need to develop simple models for generation of the characteristic non-Gaussian statistics, namely the asymmetric distributions of water surface height and wave slope. We argue that a simple class of non-linear oscillators can reproduce some of the characteristic features of random water wave processes and linear or non-linear response to ocean waves. We describe the Slepian model for the Gaussian case, and explain the use of the regression approximation for level crossing distances and associated variables, such as wave period and amplitude. Finally we speculate about a generalization of the regression technique to the... (More)
Ocean waves exhibit more or less a Gaussian distribution for the instantaneous water surface height, and there is a need to develop simple models for generation of the characteristic non-Gaussian statistics, namely the asymmetric distributions of water surface height and wave slope. We argue that a simple class of non-linear oscillators can reproduce some of the characteristic features of random water wave processes and linear or non-linear response to ocean waves. We describe the Slepian model for the Gaussian case, and explain the use of the regression approximation for level crossing distances and associated variables, such as wave period and amplitude. Finally we speculate about a generalization of the regression technique to the non-linear Markov process case. (C) 2000 Elsevier Science Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
non-Gaussian process, asymmetric waves, non-linear models, wave amplitude, random waves, wave period, REGRESSION APPROXIMATIONS
in
Probabilistic Engineering Mechanics
volume
15
issue
1
pages
49 - 57
publisher
Elsevier
external identifiers
  • scopus:0033882233
ISSN
0266-8920
DOI
10.1016/S0266-8920(99)00008-9
language
English
LU publication?
yes
id
27a0e5b5-a36a-4dbb-9a2a-3506f9b003f1 (old id 1210390)
alternative location
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V4M-3Y6H1DM-7&_user=745831&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_version=1&_urlVersion=0&_userid=745831&md5=31b26a179de717ec650954451fc41c4a
date added to LUP
2016-04-01 11:50:17
date last changed
2022-03-13 01:28:27
@article{27a0e5b5-a36a-4dbb-9a2a-3506f9b003f1,
  abstract     = {{Ocean waves exhibit more or less a Gaussian distribution for the instantaneous water surface height, and there is a need to develop simple models for generation of the characteristic non-Gaussian statistics, namely the asymmetric distributions of water surface height and wave slope. We argue that a simple class of non-linear oscillators can reproduce some of the characteristic features of random water wave processes and linear or non-linear response to ocean waves. We describe the Slepian model for the Gaussian case, and explain the use of the regression approximation for level crossing distances and associated variables, such as wave period and amplitude. Finally we speculate about a generalization of the regression technique to the non-linear Markov process case. (C) 2000 Elsevier Science Ltd. All rights reserved.}},
  author       = {{Lindgren, Georg}},
  issn         = {{0266-8920}},
  keywords     = {{non-Gaussian process; asymmetric waves; non-linear models; wave amplitude; random waves; wave period; REGRESSION APPROXIMATIONS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{49--57}},
  publisher    = {{Elsevier}},
  series       = {{Probabilistic Engineering Mechanics}},
  title        = {{Wave analysis by Slepian models}},
  url          = {{http://dx.doi.org/10.1016/S0266-8920(99)00008-9}},
  doi          = {{10.1016/S0266-8920(99)00008-9}},
  volume       = {{15}},
  year         = {{2000}},
}