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Pré-Publication, Document De Travail Année : 2022

Unified depth-limited wave breaking detection and dissipation in fully nonlinear potential flow models

Résumé

A new method is proposed for simulating the energy dissipation resulting from depth-limited wave breaking, in combination with a universal breaking onset criterion, in two-dimensional (2D) fully nonlinear potential flow (FNPF) models, based on a non-dimensional breaking strength parameter. Two different 2D-FNPF models are used, which solve the Laplace equation based on Chebyshev polynomial expansions or a boundary element method. In these models, impending breaking waves are detected in real time using a universal breaking onset criterion proposed in earlier work, based on the ratio of the horizontal particle velocity at the crest u, relative to the crest velocity c, B = u/c > 0.85. For these waves wave energy is dissipated locally using an absorbing surface pressure that is calibrated using an inverted hydraulic jump analog. This approach is first validated for periodic spilling breakers over plane beaches and bars, for which results are shown to be in good agreement with experimental data. Recasting this breaking dissipation model in terms of a non-dimensional breaking strength, the hydraulic jump analog is shown to provide results similar to those of a constant breaking strength model, and to yield good agreement for periodic plunging breakers as well. The same approach is then applied to irregular waves shoaling over a submerged bar, and shown to agree well with experimental data for the wave height, asymmetry, skewness, and kurtosis. Future work will extend this 2D breaker model to cases of three-dimensional (3D) breaking waves, simulated in existing 3D-FNPF models, in shallow or deep water conditions.
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Dates et versions

hal-03609388 , version 1 (15-03-2022)

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  • HAL Id : hal-03609388 , version 1

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Sunil Mohanlal, Jeffrey Harris, Marissa Yates, Stephan Grilli. Unified depth-limited wave breaking detection and dissipation in fully nonlinear potential flow models. 2022. ⟨hal-03609388⟩
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