Heat Transfer Neural Analysis of Bingham-Papanastasiou Fluid in Lid-driven Rectangular Cavity with viscous dissipation

Heat Transfer Neural Analysis of Bingham-Papanastasiou Fluid in Lid-driven Rectangular Cavity with viscous dissipation | 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 Heat Transfer Neural Analysis of Bingham-Papanastasiou Fluid in Lid-driven Rectangular Cavity with viscous dissipation Arooj Tanveer, Sami Ul-Haq, Muhammad Bilal Ashraf This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3884960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study of Bingham-Papanastasiou fluids is conducted in lid-driven cavity with consideration of viscous dissipation. The left wall of the cavity is adiabatic while other walls are insulated. Numerical simulations are conducted to study the isotherms, temperature profile and Nusselt number. An inventive artificial neural network (ANN) model for precise predictions is made using the simulation data. Both COMSOL and ANN are used to find the best values for each input parameter to maximize the output parameters. The effectiveness of these two approaches in obtaining the best results is then evaluated through a comparison study. Plotting isotherms for larger Bingham numbers shows that the temperature distribution toward the left wall is behaving more and more rapidly. The rate of heat transfer rises with increasing Re values, but it exhibits the opposite behavior when Bn values are high close to the left adiabatic wall. After 406 epochs, the training state plot demonstrated convergence and optimization progress with gradient = 0.018363 and Mu = 0.0001. The novelty of this work is that the integrated approach involving Artificial Neural Networks (ANN) described in this study allows the prediction of flow behaviors for various cases without additional real-time CFD simulations once sufficient information is gathered through Computational Fluid Dynamics (CFD) simulations for a few flow cases. In real-world flow control applications where real-time CFD simulations might not be possible, this method seems to be beneficial. Physical sciences/Mathematics and computing/Applied mathematics Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Scientific data Physical sciences/Mathematics and computing/Software Bingham-Papanastasiou fluid Viscous dissipation ANN Lid-driven cavity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted 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-3884960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":271226458,"identity":"a4c67132-d012-49f6-a233-265352a2d150","order_by":0,"name":"Arooj Tanveer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYLACCTijwkYORB8grCUByrA4k2ZMnBYGmJbKtkOJDYQUG9w+fPCB5Q+7PIPjzcc+3Gw7kD4/7PDDAww1NtE4tZxLSzaQSEguNjhzLHnmjHN3cjfeTjM4wHAsLReXdQZneMwkJBKYE2fOyDFmlih7lrtxdoLBAcaGw4S01CfOnP/+M/MftsPphrPTPxCj5XBivwQPM4NE2+EEeekc/LZInmED+iXteGI/DzB8Jc6kGW6Qzik4kIDHL3xnmA8+lrCpTmxjP/wYFJXy8rPTN3/4UGODUwsIMEsg84DBhYgoXIDxAzJPHp/po2AUjIJRMCIBALzHX3nuw2pWAAAAAElFTkSuQmCC","orcid":"","institution":"COMSATS University Islamabad (CUI)","correspondingAuthor":true,"prefix":"","firstName":"Arooj","middleName":"","lastName":"Tanveer","suffix":""},{"id":271226459,"identity":"0e9eb266-b4be-4d27-8ce2-191cb316326e","order_by":1,"name":"Sami Ul-Haq","email":"","orcid":"","institution":"COMSATS University Islamabad (CUI)","correspondingAuthor":false,"prefix":"","firstName":"Sami","middleName":"","lastName":"Ul-Haq","suffix":""},{"id":271226460,"identity":"1bd0cb3b-b2e1-4fa4-a35a-cef26d077fe1","order_by":2,"name":"Muhammad Bilal Ashraf","email":"","orcid":"","institution":"COMSATS University Islamabad (CUI)","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Bilal","lastName":"Ashraf","suffix":""}],"badges":[],"createdAt":"2024-01-21 14:21:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3884960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3884960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52217561,"identity":"84b02dc1-64ca-4d88-b8b8-4bcfc45a3cc4","added_by":"auto","created_at":"2024-03-08 02:59:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1523121,"visible":true,"origin":"","legend":"","description":"","filename":"BinghamFluid.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3884960/v1_covered_9504613c-849e-4b75-884d-3903eecb0f50.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heat Transfer Neural Analysis of Bingham-Papanastasiou Fluid in Lid-driven Rectangular Cavity with viscous dissipation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bingham-Papanastasiou fluid, Viscous dissipation, ANN, Lid-driven cavity","lastPublishedDoi":"10.21203/rs.3.rs-3884960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3884960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study of Bingham-Papanastasiou fluids is conducted in lid-driven cavity with consideration of viscous dissipation. The left wall of the cavity is adiabatic while other walls are insulated. Numerical simulations are conducted to study the isotherms, temperature profile and Nusselt number. An inventive artificial neural network (ANN) model for precise predictions is made using the simulation data. Both COMSOL and ANN are used to find the best values for each input parameter to maximize the output parameters. The effectiveness of these two approaches in obtaining the best results is then evaluated through a comparison study. Plotting isotherms for larger Bingham numbers shows that the temperature distribution toward the left wall is behaving more and more rapidly. The rate of heat transfer rises with increasing Re values, but it exhibits the opposite behavior when Bn values are high close to the left adiabatic wall. After 406 epochs, the training state plot demonstrated convergence and optimization progress with gradient\u0026thinsp;=\u0026thinsp;0.018363 and Mu\u0026thinsp;=\u0026thinsp;0.0001. The novelty of this work is that the integrated approach involving Artificial Neural Networks (ANN) described in this study allows the prediction of flow behaviors for various cases without additional real-time CFD simulations once sufficient information is gathered through Computational Fluid Dynamics (CFD) simulations for a few flow cases. In real-world flow control applications where real-time CFD simulations might not be possible, this method seems to be beneficial.\u003c/p\u003e","manuscriptTitle":"Heat Transfer Neural Analysis of Bingham-Papanastasiou Fluid in Lid-driven Rectangular Cavity with viscous dissipation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 05:08:14","doi":"10.21203/rs.3.rs-3884960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"73afb8b9-c99d-4f57-8fb0-e6bb961037c0","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28589257,"name":"Physical sciences/Mathematics and computing/Applied mathematics"},{"id":28589258,"name":"Physical sciences/Mathematics and computing/Computational science"},{"id":28589259,"name":"Physical sciences/Mathematics and computing/Scientific data"},{"id":28589260,"name":"Physical sciences/Mathematics and computing/Software"}],"tags":[],"updatedAt":"2024-03-08T02:57:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 05:08:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3884960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3884960","identity":"rs-3884960","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}