Classically studied coherent structures only paint a partial picture of wall-bounded turbulence

Classically studied coherent structures only paint a partial picture of wall-bounded turbulence | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Classically studied coherent structures only paint a partial picture of wall-bounded turbulence Ricardo Vinuesa, Andres Cremades, Sergio Hoyas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5587182/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract For the last 140 years, the mechanisms of transport and dissipation of energy in a turbulent flow have not been completely understood due to the complexity of this phenomenon. The dissipation of energy due to turbulence is significative, and understanding turbulence physics is crucial for fighting the present climate emergency. Previous research has focused on analyzing the so-called coherent structures of the flow (Q events, streaks, and vortices), which are regions of high turbulence transport, high/low streamwise fluctuation, and rotation, respectively. However, the connection between these classically studied structures and the flow development is still uncertain. In a previous analysis, the importance of the different Q events was quantified through a data-driven methodology, showing that the calculated importance did not perfectly agree with the definition of the structures. To fill this gap, here we show a data-driven methodology for objectively identifying high-importance regions in a turbulent flow. A deep-learning model is trained to predict a future state of a turbulent channel flow and the gradient-SHAP explainability algorithm is used to calculate the importance of each grid point for such a prediction. Finally, high-importance regions are computed using the SHAP data, analyzing and comparing their characteristics with those of the other coherent structures. The SHAP analysis provides an objective way to identify the regions of highest importance in the turbulent flow, which exhibit different levels of agreement with the classically studied structures. Physical sciences/Engineering/Aerospace engineering Physical sciences/Physics/Fluid dynamics Physical sciences/Engineering/Mechanical engineering Turbulence Deep learning Machine learning Shapley values Explainability Coherent structures Full Text Additional Declarations There is NO Competing Interest. Supplementary Files XAINATCOMMUNSupplementary.pdf Supplementary Material Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5587182","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388165370,"identity":"11304ca5-d722-469e-b4e3-4f2a09207203","order_by":0,"name":"Ricardo Vinuesa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYFACxgYwxQ/EzAwMCQwGRGuRbIBokSBCCxQYHCBWi/mM5LbHPBV18sbXDh/8XMCQVmfOwP7wAT4tMjcS2415zhw23HY7LVl6BkOOhGUDjzFemyR4DrZJ87YdYNx2O8eMmYehQsLgAA+bBGEt/+rsN8/O/wbVwv78B14t7I1ALQ3MiRukc9iAWnKAWhjM8OkAa5Gcc+xw8ozbacbSPAZpkhsO8xjjdxgz+zOJNzV1tv2zkx9+5qlI5jc43v7wA15rUAEoqJhJUD8KRsEoGAWjADsAADgmQJq9/ta4AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6570-5499","institution":"KTH Royal Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Vinuesa","suffix":""},{"id":388165371,"identity":"6dff5831-44b6-44c2-9966-a824386f2401","order_by":1,"name":"Andres Cremades","email":"","orcid":"https://orcid.org/0000-0002-7052-4913","institution":"KTH","correspondingAuthor":false,"prefix":"","firstName":"Andres","middleName":"","lastName":"Cremades","suffix":""},{"id":388165372,"identity":"edca49d3-16ba-4832-a4c6-3ead6da194e0","order_by":2,"name":"Sergio Hoyas","email":"","orcid":"https://orcid.org/0000-0002-8458-7288","institution":"UPV","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Hoyas","suffix":""}],"badges":[],"createdAt":"2024-12-05 13:05:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5587182/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5587182/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-65199-9","type":"published","date":"2025-11-19T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96354839,"identity":"1b30f629-0414-48e1-955a-0de12e19ff1e","added_by":"auto","created_at":"2025-11-20 08:11:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1363362,"visible":true,"origin":"","legend":"Article File","description":"","filename":"XAINATCOMMUNMain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5587182/v1_covered_7bc566d1-f4b5-4576-b9e2-309f77321f19.pdf"},{"id":71089676,"identity":"a4ce7265-0ab8-46d6-9e62-e143a71afdc2","added_by":"auto","created_at":"2024-12-11 04:34:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6910737,"visible":true,"origin":"","legend":"Supplementary Material","description":"","filename":"XAINATCOMMUNSupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5587182/v1/5782c89a92a3a11e648070e0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Classically studied coherent structures only paint a partial picture of wall-bounded turbulence","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Turbulence, Deep learning, Machine learning, Shapley values, Explainability, Coherent structures","lastPublishedDoi":"10.21203/rs.3.rs-5587182/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5587182/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"For the last 140 years, the mechanisms of transport and dissipation of energy\r\nin a turbulent flow have not been completely understood due to the complexity\r\nof this phenomenon. The dissipation of energy due to turbulence is significative,\r\nand understanding turbulence physics is crucial for fighting the present climate\r\nemergency. Previous research has focused on analyzing the so-called coherent\r\nstructures of the flow (Q events, streaks, and vortices), which are regions of high\r\nturbulence transport, high/low streamwise fluctuation, and rotation, respectively.\r\nHowever, the connection between these classically studied structures and the\r\nflow development is still uncertain. In a previous analysis, the importance of the\r\ndifferent Q events was quantified through a data-driven methodology, showing\r\nthat the calculated importance did not perfectly agree with the definition of the\r\nstructures. To fill this gap, here we show a data-driven methodology for objectively\r\nidentifying high-importance regions in a turbulent flow. A deep-learning\r\nmodel is trained to predict a future state of a turbulent channel flow and the\r\ngradient-SHAP explainability algorithm is used to calculate the importance of\r\neach grid point for such a prediction. Finally, high-importance regions are computed\r\nusing the SHAP data, analyzing and comparing their characteristics with\r\nthose of the other coherent structures. 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