Nonlinear wave evolution with data-driven breaking
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 turbulen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/154221 |
| _version_ | 1826190141294641152 |
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| author | Eeltink, D. Branger, H. Luneau, C. He, Y. Chabchoub, A. Kasparian, J. van den Bremer, T. S. Sapsis, T. P. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Eeltink, D. Branger, H. Luneau, C. He, Y. Chabchoub, A. Kasparian, J. van den Bremer, T. S. Sapsis, T. P. |
| author_sort | Eeltink, D. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T08:35:40Z |
| format | Article |
| id | mit-1721.1/154221 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2025-02-19T04:17:16Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1542212024-11-20T21:31:11Z 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. Massachusetts Institute of Technology. Department of Mechanical Engineering General Physics and Astronomy General Biochemistry, Genetics and Molecular Biology General Chemistry Multidisciplinary 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. 2024-04-18T21:04:28Z 2024-04-18T21:04:28Z 2022-04-29 2024-04-18T21:00:39Z Article http://purl.org/eprint/type/JournalArticle 2041-1723 https://hdl.handle.net/1721.1/154221 Eeltink, D., Branger, H., Luneau, C. et al. Nonlinear wave evolution with data-driven breaking. Nat Commun 13, 2343 (2022). en 10.1038/s41467-022-30025-z Nature Communications Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Springer Science and Business Media LLC |
| spellingShingle | General Physics and Astronomy General Biochemistry, Genetics and Molecular Biology General Chemistry Multidisciplinary Eeltink, D. Branger, H. Luneau, C. He, Y. Chabchoub, A. Kasparian, J. van den Bremer, T. S. Sapsis, T. P. Nonlinear wave evolution with data-driven breaking |
| title | Nonlinear wave evolution with data-driven breaking |
| title_full | Nonlinear wave evolution with data-driven breaking |
| title_fullStr | Nonlinear wave evolution with data-driven breaking |
| title_full_unstemmed | Nonlinear wave evolution with data-driven breaking |
| title_short | Nonlinear wave evolution with data-driven breaking |
| title_sort | nonlinear wave evolution with data driven breaking |
| topic | General Physics and Astronomy General Biochemistry, Genetics and Molecular Biology General Chemistry Multidisciplinary |
| url | https://hdl.handle.net/1721.1/154221 |
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