Wave analysis by Slepian models
(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:
https://lup.lub.lu.se/record/1210390
- author
- Lindgren, Georg
LU
- organization
- publishing date
- 2000
- 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}}, }