Nonlinear wave evolution with data-driven breaking
Eeltink, D.; Branger, H.; Luneau, C.; He, Y.; Chabchoub, A.; Kasparian, J.; van den Bremer, T.S.; Sapsis, T.P. (2022). Nonlinear wave evolution with data-driven breaking. Nature Comm. 13(1): 2343. https://dx.doi.org/10.1038/s41467-022-30025-z
In: Nature Communications. Nature Publishing Group: London. ISSN 2041-1723; e-ISSN 2041-1723, meer
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| Auteurs | | Top |
- Eeltink, D.
- Branger, H.
- Luneau, C.
- He, Y.
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- Chabchoub, A.
- Kasparian, J.
- van den Bremer, T.S.
- Sapsis, T.P.
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| Abstract |
Wave breaking is the main mechanism that dissipates energy input into ocean waves by wind and transferred across the spectrum by nonlinearity. It determines the properties of a sea state and plays a crucial role in ocean-atmosphere interaction, ocean pollution, and rogue waves. Owing to its turbulent nature, wave breaking remains too computationally demanding to solve using direct numerical simulations except in simple, short-duration circumstances. To overcome this challenge, we present a blended machine learning framework in which a physics-based nonlinear evolution model for deep-water, non-breaking waves and a recurrent neural network are combined to predict the evolution of breaking waves. We use wave tank measurements rather than simulations to provide training data and use a long short-term memory neural network to apply a finite-domain correction to the evolution model. Our blended machine learning framework gives excellent predictions of breaking and its effects on wave evolution, including for external data. |
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