Nonlinear wave evolution with data-driven breaking
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s41467-022-30025-z.pdf
Size
2.19 MB
Format
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Checksum (MD5)
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Author(s)
Eeltink, D.
Branger, H.
Luneau, C.
He, Y.
Chabchoub, A.
Kasparian, J.
van den Bremer, T. S.
Sapsis, T. P.
Date Issued
April 29, 2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Eeltink, D., Branger, H., Luneau, C. et al. Nonlinear wave evolution with data-driven breaking. Nat Commun 13, 2343 (2022).
Version
Final published version
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.
Subjects
General Physics and Astronomy
General Biochemistry, Genetics and Molecular Biology
General Chemistry
Multidisciplinary
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1038/s41467-022-30025-z
