Research on wear and grinding treatment of high-speed turnout rails

Research on wear and grinding treatment of high-speed turnout rails | 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 Research on wear and grinding treatment of high-speed turnout rails Jincheng Li, Yayun Qi, Pengpeng Wu, Junjun Ding, Haohao Ding This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734205/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract To mitigate steel rail wear in turnouts, this study examines wear mechanisms and influencing factors in high-speed turnouts. We establish a coupled dynamics model to analyze vehicle-turnout interactions, incorporating actual turnout rail profiles, continuous traction forces from passing vehicles, and running resistance. This study evaluates the suitability of various wear models, update procedures, wear superposition methods, and filtering techniques for predicting turnout rail wear. A predictive model for turnout rail wear is developed, and the influence of different turnout parameters on rail wear is investigated. Computational results indicate that the wear power model provides superior accuracy and efficiency in predicting turnout rail wear. A depth update step of 0.05mm proves optimal for turnout steel rail wear, while an adaptive filtering algorithm tailored to turnout wear characteristics effectively filters rail wear. Compared to traditional models, our dynamic model, which accounts for traction-resistance, closely aligns with actual measured data, demonstrating higher computational accuracy. Excessive longitudinal and lateral stiffness during vehicle passage through turnouts is found to be detrimental to reducing turnout steel rail wear. Conversely, appropriately increasing anti-snaking damper stiffness and lateral damper damping can mitigate rail wear. Based on the distribution of rail wear in the turnout area, an holistic grinding scheme was formulated. This scheme effectively improved the wheel-rail contact conditions in the turnout area, reducing the lateral vibration acceleration of vehicles passing through the turnout by 52.56% and the vertical vibration acceleration by 30.43%. The holistic grinding scheme can effectively improve the smoothness of the turnout and reduce vehicle vibrations when passing through. This research provides a theoretical basis for suppressing rail wear in high-speed turnouts, effectively reducing the maintenance costs of turnout rails, and has significant implications for improving the operational safety and economic benefits of high-speed trains. Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Mechanical engineering High speed turnouts Vehicle turnout coupled dynamic model Wheel rail interaction Wear prediction Turnout grinding Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 28 Oct, 2024 Reviews received at journal 25 Oct, 2024 Reviews received at journal 17 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers invited by journal 24 Sep, 2024 Editor assigned by journal 15 Jul, 2024 Editor invited by journal 15 Jul, 2024 Submission checks completed at journal 13 Jul, 2024 First submitted to journal 13 Jul, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4734205","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330589346,"identity":"34b5c47c-6378-474b-8bf0-df83f0bce28e","order_by":0,"name":"Jincheng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBAC+2b2gw8+GNjwMLY3EKnFgJ0n2XBGQZoMc88BYrXwM5hJc3w4bMM+I4FILebMDGnSDAaHeXhnPt54g6HGJpqgFstmxsPWBQbpPJKz04otGI6l5TYQ1HOYIfH2DANrHsPZOWYSjA2HidJiIM1jwMxjf/MMkVoMDjMYAbU48zDO4CFSi2QzKJAN0ngYe4B+SSDGL/z8x4FR+cfGnrH98MYbH2psiPALsiMlEkhRDtFCqo5RMApGwSgYGQAAc2w7377b9igAAAAASUVORK5CYII=","orcid":"","institution":"Shandong Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Jincheng","middleName":"","lastName":"Li","suffix":""},{"id":330589347,"identity":"59536bbe-540b-4469-b5d7-51a1eda57528","order_by":1,"name":"Yayun Qi","email":"","orcid":"","institution":"Chongqing Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yayun","middleName":"","lastName":"Qi","suffix":""},{"id":330589348,"identity":"c1af6539-158f-4d5f-9045-839e03dd0c4f","order_by":2,"name":"Pengpeng Wu","email":"","orcid":"","institution":"China Railway Materials Operation and Maintenance Technology CO., LTD","correspondingAuthor":false,"prefix":"","firstName":"Pengpeng","middleName":"","lastName":"Wu","suffix":""},{"id":330589349,"identity":"5f011bf7-822a-432d-b9b7-23cf9a5d9c1a","order_by":3,"name":"Junjun Ding","email":"","orcid":"","institution":"Southwest Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Ding","suffix":""},{"id":330589350,"identity":"37328ae0-7630-4ff0-89e4-e2647945b5a2","order_by":4,"name":"Haohao Ding","email":"","orcid":"","institution":"Southwest Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Haohao","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-07-13 08:14:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4734205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4734205/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-85016-5","type":"published","date":"2025-01-02T15:56:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":73093319,"identity":"7c88e5a2-0476-40a9-8c4d-33c78b171171","added_by":"auto","created_at":"2025-01-06 16:13:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1175440,"visible":true,"origin":"","legend":"","description":"","filename":"Researchonwearandgrindingtreatmentofhighspeedturnoutrails.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4734205/v1_covered_9cd64ea3-1200-47ac-ac74-75c9351c9881.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on wear and grinding treatment of high-speed turnout rails","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":"High speed turnouts, Vehicle turnout coupled dynamic model, Wheel rail interaction, Wear prediction, Turnout grinding","lastPublishedDoi":"10.21203/rs.3.rs-4734205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4734205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To mitigate steel rail wear in turnouts, this study examines wear mechanisms and influencing factors in high-speed turnouts. We establish a coupled dynamics model to analyze vehicle-turnout interactions, incorporating actual turnout rail profiles, continuous traction forces from passing vehicles, and running resistance. This study evaluates the suitability of various wear models, update procedures, wear superposition methods, and filtering techniques for predicting turnout rail wear. A predictive model for turnout rail wear is developed, and the influence of different turnout parameters on rail wear is investigated. Computational results indicate that the wear power model provides superior accuracy and efficiency in predicting turnout rail wear. A depth update step of 0.05mm proves optimal for turnout steel rail wear, while an adaptive filtering algorithm tailored to turnout wear characteristics effectively filters rail wear. Compared to traditional models, our dynamic model, which accounts for traction-resistance, closely aligns with actual measured data, demonstrating higher computational accuracy. Excessive longitudinal and lateral stiffness during vehicle passage through turnouts is found to be detrimental to reducing turnout steel rail wear. Conversely, appropriately increasing anti-snaking damper stiffness and lateral damper damping can mitigate rail wear. Based on the distribution of rail wear in the turnout area, an holistic grinding scheme was formulated. This scheme effectively improved the wheel-rail contact conditions in the turnout area, reducing the lateral vibration acceleration of vehicles passing through the turnout by 52.56% and the vertical vibration acceleration by 30.43%. The holistic grinding scheme can effectively improve the smoothness of the turnout and reduce vehicle vibrations when passing through. This research provides a theoretical basis for suppressing rail wear in high-speed turnouts, effectively reducing the maintenance costs of turnout rails, and has significant implications for improving the operational safety and economic benefits of high-speed trains.","manuscriptTitle":"Research on wear and grinding treatment of high-speed turnout rails","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-07 01:26:54","doi":"10.21203/rs.3.rs-4734205/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-28T06:04:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-25T09:30:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-17T14:25:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107963861048032774045278648443791897190","date":"2024-10-01T11:34:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242969536079855089325618014482468690790","date":"2024-09-25T07:34:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-24T14:58:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-15T08:22:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-15T08:16:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-13T13:31:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-13T08:11:59+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"c6c1446e-fff4-47a7-ab5f-647338452dfb","owner":[],"postedDate":"August 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34995503,"name":"Physical sciences/Mathematics and computing/Computer science"},{"id":34995504,"name":"Physical sciences/Engineering/Civil engineering"},{"id":34995505,"name":"Physical sciences/Engineering/Mechanical engineering"}],"tags":[],"updatedAt":"2025-01-06T16:01:27+00:00","versionOfRecord":{"articleIdentity":"rs-4734205","link":"https://doi.org/10.1038/s41598-024-85016-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-02 15:56:57","publishedOnDateReadable":"January 2nd, 2025"},"versionCreatedAt":"2024-08-07 01:26:54","video":"","vorDoi":"10.1038/s41598-024-85016-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-85016-5","workflowStages":[]},"version":"v1","identity":"rs-4734205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4734205","identity":"rs-4734205","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}