Irreversibility Analysis through Neural Networking of the Hybrid Nanofluid for the Solar Collector Optimization

Irreversibility Analysis through Neural Networking of the Hybrid Nanofluid for the Solar Collector Optimization | 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 Irreversibility Analysis through Neural Networking of the Hybrid Nanofluid for the Solar Collector Optimization Sayer Obaid Alharbi, Taza Gul, Ilyas Khan, Mohd Shakir Khan, Saleh Alzahrani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3018644/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Aug, 2023 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Advanced techniques are used to increase the efficiency of the energy assets and maximize the appliance efficiency of the main resources. In the recent study, the focus is paid to the solar collector to cover thermal radiation through optimization and enhance the performance of the solar panel. Hybrid nanofluids (HNs) consist of a base liquid (C3H8O2) glycol whereas copper (Cu), and aluminum oxide (Al2O3) are used as nanomaterials for formation (HNs). The flow of the stagnation point is considered in the presence of the Riga plate. The state of the solar thermal system is termed viva stagnation to control the additional heating through the flow variation in the collector loop. The inclusion of entropy generation and Bejan number formation is primarily conceived under the influence of physical parameters for energy optimization. The computational analysis was carried out utilizing the control volume finite element method (CVFEM), and Runge–Kutta 4 (RK-4) methods. The results are further validated through a machine learning neural networking procedure. The conclusions showed that the heat transfer rate is greatly upgraded with a variation of the nanoparticle's volume fraction. We expect this improvement to progress the stability of heat transfer in the solar power system. Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology Stagnation for the optimization and irreversibility of Energy Riga Plate in term of the Magnetic actuator Solar radiation Neural networking machine learning and RK-4 technique Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Aug, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 30 Jul, 2023 Reviews received at journal 26 Jul, 2023 Reviews received at journal 16 Jun, 2023 Reviewers agreed at journal 08 Jun, 2023 Reviewers agreed at journal 08 Jun, 2023 Reviewers invited by journal 08 Jun, 2023 Editor assigned by journal 08 Jun, 2023 Editor invited by journal 08 Jun, 2023 Submission checks completed at journal 08 Jun, 2023 First submitted to journal 03 Jun, 2023 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-3018644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":208017236,"identity":"b663d92a-8482-4a0c-b84c-d978dc355fc4","order_by":0,"name":"Sayer Obaid Alharbi","email":"","orcid":"","institution":"Majmaah University","correspondingAuthor":false,"prefix":"","firstName":"Sayer","middleName":"Obaid","lastName":"Alharbi","suffix":""},{"id":208017238,"identity":"7703e91f-1daa-46bb-81a1-aa7a78bfb983","order_by":1,"name":"Taza Gul","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYPACCzkQeQCIGRsYGJjxquWBUBLGYC0HSNGS2AC1hrAWe/bjDz9+bZNI75+R/vDwBwYb2Q0HeA8b4LWFJyFZWrZNInfGjRwDoMPSjDcc4EtOwO+whAPSkkAtGyRyQH45nLjhAI/xAbxa+B82/wZqSTeQSH8A1PKfCC0SyWySH9skEgyACKjlAFgLfofdeMZmzXBOwnDGmTcGB84YJBvPPMyXjNf77P3pj2/+KLOR529Pf/yhosJOtu9472EJfFpAgJkHzjRA5eIEjD/QHEtYyygYBaNgFIwoAACbQ0xqrKjmZQAAAABJRU5ErkJggg==","orcid":"","institution":"City University of Science and Information Technology","correspondingAuthor":true,"prefix":"","firstName":"Taza","middleName":"","lastName":"Gul","suffix":""},{"id":208017239,"identity":"f06a982e-ceff-4757-9364-56ffb2176bff","order_by":2,"name":"Ilyas Khan","email":"","orcid":"","institution":"Majmaah University","correspondingAuthor":false,"prefix":"","firstName":"Ilyas","middleName":"","lastName":"Khan","suffix":""},{"id":208017240,"identity":"54423f16-ba1c-40a7-a8b8-9a1f2ed679a0","order_by":3,"name":"Mohd Shakir Khan","email":"","orcid":"","institution":"Majmaah University","correspondingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Shakir","lastName":"Khan","suffix":""},{"id":208017241,"identity":"422e7c2f-bf14-45ed-8894-ecb05cf36853","order_by":4,"name":"Saleh Alzahrani","email":"","orcid":"","institution":"University College in Al-Qunfudhah, Umm Al-Qura University","correspondingAuthor":false,"prefix":"","firstName":"Saleh","middleName":"","lastName":"Alzahrani","suffix":""}],"badges":[],"createdAt":"2023-06-03 16:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3018644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3018644/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-40519-5","type":"published","date":"2023-08-16T21:59:30+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":44736253,"identity":"92550c5a-6874-40d6-a805-8a2c7d8c5755","added_by":"auto","created_at":"2023-10-16 22:29:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1221225,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3018644/v1_covered_38b04432-022f-4cf7-924f-ff63ebe4575c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Irreversibility Analysis through Neural Networking of the Hybrid Nanofluid for the Solar Collector Optimization","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":"Stagnation for the optimization and irreversibility of Energy, Riga Plate in term of the Magnetic actuator, Solar radiation, Neural networking, machine learning and RK-4 technique","lastPublishedDoi":"10.21203/rs.3.rs-3018644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3018644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdvanced techniques are used to increase the efficiency of the energy assets and maximize the appliance efficiency of the main resources. In the recent study, the focus is paid to the solar collector to cover thermal radiation through optimization and enhance the performance of the solar panel. Hybrid nanofluids (HNs) consist of a base liquid (C3H8O2) glycol whereas copper (Cu), and aluminum oxide (Al2O3) are used as nanomaterials for formation (HNs). The flow of the stagnation point is considered in the presence of the Riga plate. The state of the solar thermal system is termed viva stagnation to control the additional heating through the flow variation in the collector loop. The inclusion of entropy generation and Bejan number formation is primarily conceived under the influence of physical parameters for energy optimization. \u0026nbsp;The computational analysis was carried out utilizing the control volume finite element method (CVFEM), and \u003cem\u003eRunge–Kutta 4 \u003c/em\u003e(RK-4) methods. The results are further validated through a machine learning neural networking procedure. The conclusions showed that the heat transfer rate is greatly upgraded with a variation of the nanoparticle's volume fraction. 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