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Estimation of incident and reflected components in highly nonlinear regular waves
AbstractKnowledge of the incident and reflected waves present in laboratory experiments is a key issue in order to correctly assess the behaviour of the tested structure. Usual applied reflection separation algorithms are based on linear wave theory. These linear methods might result in unreliable estimates of the incident and reflected waves in case the waves are nonlinear. In the present short paper a new nonlinear reflection separation algorithm optimized for regular waves is presented. The method separates the superharmonics into bound/free and incident/reflected components. The separation in bound and free components is possible because they travel with different celerity. The new method is an extension of the Lin and Huang (2004) method as they used linear dispersion so indirectly assumed the bound waves to be of 2nd order maximum. They did thus not take into account the amplitude dispersive effect of nonlinear waves (3rd and higher order). The present method uses nonlinear wave celerity in order to overcome this limitation. It is shown in the present paper that for highly nonlinear regular waves none of the existing state-of-the-art tools are reliable. The new method showed on the other hand a good match for all of the tested synthetically generated wave conditions including shallow and deep water and proved also to be robust to noise. Even though the new method is developed for horizontal sea bed it is demonstrated to provide reasonable results on numerical data for vertical asymmetric waves on mildly sloping foreshores.
HighlightsExisting reflection separation methods are inadequate for non-linear regular waves.Lin-Huang reflection separation method is extended to cover highly non-linear waves.The new method is tested on synthetic and numerical data and found reliable for all test cases.
Estimation of incident and reflected components in highly nonlinear regular waves
AbstractKnowledge of the incident and reflected waves present in laboratory experiments is a key issue in order to correctly assess the behaviour of the tested structure. Usual applied reflection separation algorithms are based on linear wave theory. These linear methods might result in unreliable estimates of the incident and reflected waves in case the waves are nonlinear. In the present short paper a new nonlinear reflection separation algorithm optimized for regular waves is presented. The method separates the superharmonics into bound/free and incident/reflected components. The separation in bound and free components is possible because they travel with different celerity. The new method is an extension of the Lin and Huang (2004) method as they used linear dispersion so indirectly assumed the bound waves to be of 2nd order maximum. They did thus not take into account the amplitude dispersive effect of nonlinear waves (3rd and higher order). The present method uses nonlinear wave celerity in order to overcome this limitation. It is shown in the present paper that for highly nonlinear regular waves none of the existing state-of-the-art tools are reliable. The new method showed on the other hand a good match for all of the tested synthetically generated wave conditions including shallow and deep water and proved also to be robust to noise. Even though the new method is developed for horizontal sea bed it is demonstrated to provide reasonable results on numerical data for vertical asymmetric waves on mildly sloping foreshores.
HighlightsExisting reflection separation methods are inadequate for non-linear regular waves.Lin-Huang reflection separation method is extended to cover highly non-linear waves.The new method is tested on synthetic and numerical data and found reliable for all test cases.
Estimation of incident and reflected components in highly nonlinear regular waves
Lykke Andersen, Thomas (Autor:in) / Eldrup, Mads Røge (Autor:in) / Frigaard, Peter (Autor:in)
Coastal Engineering ; 119 ; 51-64
28.08.2016
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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