Physics-Informed Machine Learning for Subcooled Boiling Flow Prediction with DEBORA Experiment

Physics-Informed Machine Learning for Subcooled Boiling Flow Prediction with DEBORA Experiment | 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 Physics-Informed Machine Learning for Subcooled Boiling Flow Prediction with DEBORA Experiment Guang Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7757227/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 Subcooled boiling flows are critical phenomena in energy systems, characterized by complex multiphase interactions that challenge traditional computational fluid dynamics (CFD) modeling approaches due to their computational intensity and limited real-time applicability. This study presents a comprehensive physics-informed machine learning (PIML) framework for predicting key subcooled boiling flow parameters using sparse 18 experimental data points from the DEBORA (DEveloppement de BOiling Refrigerant Applications) facility. Six ML algorithms were systematically evaluated through multi-output predictions of void fraction, liquid/gas superficial velocity, and mean bubble diameter as functions of radial position and operating conditions. The framework incorporated eight comprehensive physics-based features including dimensionless numbers (Reynolds, Weber, Froude), geometric parameters, and derived flow-specific variables (slip velocity, velocity ratio, drift velocity) to enhance predictive capability and physical interpretability while respecting fundamental conservation principles. Ridge Regression emerged as the superior algorithm for velocity and void fraction predictions, achieving exceptional R² scores of 0.978, 0.995, and 0.999 for void fraction, liquid superficial velocity, and gas superficial velocity respectively, while Random Forest excelled in mean bubble diameter prediction (R²=0.860). The PIML framework successfully captured characteristic subcooled boiling phenomena including wall-peaking void fraction distributions (0.01–0.02 core to 0.16 wall), parabolic velocity profiles with center peaks (~ 2.05 m/s), and radial bubble diameter variations (0.62 mm core to 0.40 mm wall). Comparative analysis with CFD simulations demonstrated equivalent or superior accuracy across most flow parameters, with Ridge Regression achieving maximum relative errors of ~ 5% for void fraction compared to CFD's ~ 12% and Random Forest achieving 3.5% error for gas superficial velocity versus CFD's 5.5%, while providing good computational speedups of 2,330× to 4,566× (394.2 ms versus 30 minutes). Feature importance analysis revealed dimensionless numbers as the most impactful contributors, while extrapolation studies confirmed the models' capability to predict flow behavior beyond experimental measurement ranges with tree-based ensemble methods showing superior extrapolation performance for non-linear physics. Robustness analysis across 100 iterations validated exceptional model stability for Ridge Regression (R²>0.99 for velocities and void fraction), though mean bubble diameter prediction remained challenging across all methods with extreme variability, indicating fundamental modeling challenges requiring specialized approaches. This work establishes PIML as a powerful complement to traditional CFD methods for rapid and accurate prediction of subcooled boiling flow parameters, enabling real-time monitoring, interactive design optimization, and democratized access to advanced thermal fluids analysis for energy system applications. Artificial Intelligence and Machine Learning Energy Engineering Physics-informed machine learning Subcooled boiling Void fraction Gas/liquid superficial velocity Mean bubble diameter Full Text Additional Declarations The authors declare no competing interests. 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-7757227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523231322,"identity":"4baa0399-7cad-45e1-b05a-8fd91c12174c","order_by":0,"name":"Guang 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Experiment\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Eindhoven University of Technology","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":"Physics-informed machine learning, Subcooled boiling, Void fraction, Gas/liquid superficial velocity, Mean bubble diameter","lastPublishedDoi":"10.21203/rs.3.rs-7757227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7757227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSubcooled boiling flows are critical phenomena in energy systems, characterized by complex multiphase interactions that challenge traditional computational fluid dynamics (CFD) modeling approaches due to their computational intensity and limited real-time applicability. This study presents a comprehensive physics-informed machine learning (PIML) framework for predicting key subcooled boiling flow parameters using sparse 18 experimental data points from the DEBORA (DEveloppement de BOiling Refrigerant Applications) facility. Six ML algorithms were systematically evaluated through multi-output predictions of void fraction, liquid/gas superficial velocity, and mean bubble diameter as functions of radial position and operating conditions. The framework incorporated eight comprehensive physics-based features including dimensionless numbers (Reynolds, Weber, Froude), geometric parameters, and derived flow-specific variables (slip velocity, velocity ratio, drift velocity) to enhance predictive capability and physical interpretability while respecting fundamental conservation principles. Ridge Regression emerged as the superior algorithm for velocity and void fraction predictions, achieving exceptional R\u0026sup2; scores of 0.978, 0.995, and 0.999 for void fraction, liquid superficial velocity, and gas superficial velocity respectively, while Random Forest excelled in mean bubble diameter prediction (R\u0026sup2;=0.860). The PIML framework successfully captured characteristic subcooled boiling phenomena including wall-peaking void fraction distributions (0.01\u0026ndash;0.02 core to 0.16 wall), parabolic velocity profiles with center peaks (~\u0026thinsp;2.05 m/s), and radial bubble diameter variations (0.62 mm core to 0.40 mm wall). Comparative analysis with CFD simulations demonstrated equivalent or superior accuracy across most flow parameters, with Ridge Regression achieving maximum relative errors of ~\u0026thinsp;5% for void fraction compared to CFD's\u0026thinsp;~\u0026thinsp;12% and Random Forest achieving 3.5% error for gas superficial velocity versus CFD's 5.5%, while providing good computational speedups of 2,330\u0026times; to 4,566\u0026times; (394.2 ms versus 30 minutes). Feature importance analysis revealed dimensionless numbers as the most impactful contributors, while extrapolation studies confirmed the models' capability to predict flow behavior beyond experimental measurement ranges with tree-based ensemble methods showing superior extrapolation performance for non-linear physics. Robustness analysis across 100 iterations validated exceptional model stability for Ridge Regression (R\u0026sup2;\u0026gt;0.99 for velocities and void fraction), though mean bubble diameter prediction remained challenging across all methods with extreme variability, indicating fundamental modeling challenges requiring specialized approaches. This work establishes PIML as a powerful complement to traditional CFD methods for rapid and accurate prediction of subcooled boiling flow parameters, enabling real-time monitoring, interactive design optimization, and democratized access to advanced thermal fluids analysis for energy system applications.\u003c/p\u003e","manuscriptTitle":"Physics-Informed Machine Learning for Subcooled Boiling Flow Prediction with DEBORA Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 18:23:04","doi":"10.21203/rs.3.rs-7757227/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":"56263931-5b51-4c14-88fe-e0d729581a29","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55615075,"name":"Artificial Intelligence and Machine Learning"},{"id":55615076,"name":"Energy Engineering"}],"tags":[],"updatedAt":"2025-10-03T18:23:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 18:23:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7757227","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7757227","identity":"rs-7757227","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}