Neural network-based Aeroelastic System Identification for Predicting Flutter of High Flexibility Wings

Neural network-based Aeroelastic System Identification for Predicting Flutter of High Flexibility Wings | 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 Neural network-based Aeroelastic System Identification for Predicting Flutter of High Flexibility Wings Qing Guo, Xiaoqiang Li, Zhijie Zhou, Dexiao Ma, Yuzhuo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4467748/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Flutter is an extremely significant academic topic in both aerodynamics and aircraft design. Since flutter can cause multiple types of phenomena including bifurcation, period doubling, and chaos, it becomes one of the most unpredictable instability phenomena. The complexity of modeling aeroelasticity of high flexibility wings will be substantially simplified by investigating the prospect of system identification techniques to forecast flutter velocity. Therefore, a novel neural network (NN)-based method for aeroelastic system identification is proposed. The proposed NN-based approach constructs an NN framework of high flexibility wings flutter models with different materials and sizes, which can effectively predict the flutter velocity of flexible wings. The accuracy of the method is demonstrated by comparing with the simulation results. Physical sciences/Engineering/Aerospace engineering Physical sciences/Engineering/Mechanical engineering Physical sciences/Mathematics and computing/Computer science Flutter High Flexibility Wings Neural Network Aeroelasticity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Reviews received at journal 25 Jun, 2024 Reviewers agreed at journal 08 Jun, 2024 Reviewers invited by journal 04 Jun, 2024 Editor assigned by journal 04 Jun, 2024 Editor invited by journal 24 May, 2024 Submission checks completed at journal 24 May, 2024 First submitted to journal 23 May, 2024 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. 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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-4467748","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310379498,"identity":"4999e88b-48e5-4e1e-aa2d-cf967d35ed08","order_by":0,"name":"Qing Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYFAC5gNQRgLRWthgSonXwmNAohZ595xvj3l3HGbgZ88xYPi5gwgthmfebjfmPXOYQbLnjQFj7xlitMzI3Sad23aYweBGjgEzYxtRWnKegbXYE61FXiKHDWKLBLFaDHiemUn/PZPOI3HmWcHBXqJsaU9+Jjlzh7Ucf3vyxgc/ibLlQAIDA2MDAw+Ic4AIDUBbGiBaRsEoGAWjYBTgBgBdSTQ47m+7BAAAAABJRU5ErkJggg==","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"Qing","middleName":"","lastName":"Guo","suffix":""},{"id":310379499,"identity":"838a5568-ff3a-4453-9733-f66ee4bf65f6","order_by":1,"name":"Xiaoqiang Li","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoqiang","middleName":"","lastName":"Li","suffix":""},{"id":310379500,"identity":"e7f4d4a5-fb0f-4e68-a091-0bc8e6e49e06","order_by":2,"name":"Zhijie Zhou","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Zhijie","middleName":"","lastName":"Zhou","suffix":""},{"id":310379501,"identity":"504b967b-8323-4a55-80e5-0872a95a915f","order_by":3,"name":"Dexiao Ma","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Dexiao","middleName":"","lastName":"Ma","suffix":""},{"id":310379502,"identity":"400a4ed1-f270-4b7a-bc0f-4c29017636a5","order_by":4,"name":"Yuzhuo Wang","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Yuzhuo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-23 15:07:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4467748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4467748/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-82573-7","type":"published","date":"2025-01-03T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73094216,"identity":"183eb712-38d8-44d6-b2db-dadfee548f4e","added_by":"auto","created_at":"2025-01-06 16:23:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1176963,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4467748/v1_covered_1c83b30c-8bcc-4cc8-8978-07a9f487e8f7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neural network-based Aeroelastic System Identification for Predicting Flutter of High Flexibility Wings","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Flutter, High Flexibility Wings, Neural Network, Aeroelasticity","lastPublishedDoi":"10.21203/rs.3.rs-4467748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4467748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlutter is an extremely significant academic topic in both aerodynamics and aircraft design. 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