Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network

Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network | 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 Research Article Heat transfer and Flow Analysis for Square Tube with Oscillating Electromagnetic Field with Experimental Data by Artificial Neural Network Anumut Sirijaroenpanitch, P. Vengsungnle, N. Naphon, S Eiamsa-ard, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4575077/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 heat transfer and friction factor of ferrofluid passing through a square tube with an oscillating electromagnetic field were investigated experimentally. The impact of electromagnetic rotating direction, flux, and frequency on heat and flow characteristics has been investigated. The Brownian motion of particles has been shown to considerably influence the direction, power, and frequency of electromagnetic rotation, resulting in higher heat transfer. The interruption of electromagnetic flow raises the friction factor even further. In addition, a three-layer back propagation network model is built, with input, hidden, and output layers numbered 5, 17, and 2, respectively. This ANN model performed well statistically, with correlation coefficients ranging from 0.99939 to 0.9996 and mean square errors (MSE) ranging from 0.0106 to 0.0190. The ANN results match the observed data within ± 5% and ± 10% error ranges for heat and flow characteristics, respectively. As a consequence, this machine learning approach might be used to forecast heat exchanger thermal performance. ANN square tube electromagnet field ferrofluid 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-4575077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322704472,"identity":"c7b54190-631a-47e8-8825-fc14e03d729f","order_by":0,"name":"Anumut Sirijaroenpanitch","email":"","orcid":"","institution":"King Mongkut’s University of Technology Thonburi","correspondingAuthor":false,"prefix":"","firstName":"Anumut","middleName":"","lastName":"Sirijaroenpanitch","suffix":""},{"id":322704473,"identity":"5bbf24ff-1dfa-4dd8-824e-27b3b754ae62","order_by":1,"name":"P. 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The impact of electromagnetic rotating direction, flux, and frequency on heat and flow characteristics has been investigated. The Brownian motion of particles has been shown to considerably influence the direction, power, and frequency of electromagnetic rotation, resulting in higher heat transfer. The interruption of electromagnetic flow raises the friction factor even further. In addition, a three-layer back propagation network model is built, with input, hidden, and output layers numbered 5, 17, and 2, respectively. This ANN model performed well statistically, with correlation coefficients ranging from 0.99939 to 0.9996 and mean square errors (MSE) ranging from 0.0106 to 0.0190. The ANN results match the observed data within \u0026plusmn;\u0026thinsp;5% and \u0026plusmn;\u0026thinsp;10% error ranges for heat and flow characteristics, respectively. 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