A Non-Intrusive Framework Using Acoustic Signals and Deep Learning for Boiling Diagnostics in Visual-Limited Environments

A Non-Intrusive Framework Using Acoustic Signals and Deep Learning for Boiling Diagnostics in Visual-Limited Environments | 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 A Non-Intrusive Framework Using Acoustic Signals and Deep Learning for Boiling Diagnostics in Visual-Limited Environments PEI-HSUN HUANG, Jee Hyun Seong, Jonathan Mario Castro-Aguilar, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8436757/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved high predictive accuracy across diverse operating conditions and demonstrated strong robustness under acoustic contamination for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results establish hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Physics Subcooled flow boiling Boiling acoustics Machine learning Critical heat flux CFD Full Text Additional Declarations No competing interests reported. Supplementary Files dataset.zip Cite Share Download PDF Status: Published Journal Publication published 25 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Jan, 2026 Reviews received at journal 21 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviews received at journal 14 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviews received at journal 06 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers invited by journal 05 Jan, 2026 Editor assigned by journal 05 Jan, 2026 Editor invited by journal 02 Jan, 2026 Submission checks completed at journal 31 Dec, 2025 First submitted to journal 31 Dec, 2025 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-8436757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":570081311,"identity":"a620d5aa-a414-4882-b3bc-dc1b8d40cff6","order_by":0,"name":"PEI-HSUN HUANG","email":"","orcid":"","institution":"Los Alamos National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"PEI-HSUN","middleName":"","lastName":"HUANG","suffix":""},{"id":570081314,"identity":"61e2c5e2-ffc9-4ea3-a925-1316e2e417ff","order_by":1,"name":"Jee Hyun Seong","email":"","orcid":"","institution":"Korea Advanced Institute of Science and 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Environments","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":"Subcooled flow boiling, Boiling acoustics, Machine learning, Critical heat flux, CFD","lastPublishedDoi":"10.21203/rs.3.rs-8436757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8436757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved high predictive accuracy across diverse operating conditions and demonstrated strong robustness under acoustic contamination for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results establish hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.\u003c/p\u003e","manuscriptTitle":"A Non-Intrusive Framework Using Acoustic Signals and Deep Learning for Boiling Diagnostics in Visual-Limited Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 12:44:07","doi":"10.21203/rs.3.rs-8436757/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-22T12:29:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T16:55:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-15T09:42:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-14T05:02:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261756651321940224287189408417443092879","date":"2026-01-12T10:25:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336366581740685227562691414375140534125","date":"2026-01-07T12:08:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T09:08:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"336224857263849115590935945816253204685","date":"2026-01-05T12:57:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201498355251116790539501328138136874301","date":"2026-01-05T12:53:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224711497447991572427980003291322436904","date":"2026-01-05T12:32:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-05T11:30:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T11:27:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-02T11:16:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T20:06:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-31T20:01:20+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":"d6c3bdcc-97f9-44e4-bab3-4a054ce44e09","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60659380,"name":"Physical sciences/Energy science and technology"},{"id":60659382,"name":"Physical sciences/Engineering"},{"id":60659384,"name":"Physical sciences/Physics"}],"tags":[],"updatedAt":"2026-03-30T16:19:59+00:00","versionOfRecord":{"articleIdentity":"rs-8436757","link":"https://doi.org/10.1038/s41598-026-41757-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-25 16:13:18","publishedOnDateReadable":"March 25th, 2026"},"versionCreatedAt":"2026-01-07 12:44:07","video":"","vorDoi":"10.1038/s41598-026-41757-z","vorDoiUrl":"https://doi.org/10.1038/s41598-026-41757-z","workflowStages":[]},"version":"v1","identity":"rs-8436757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8436757","identity":"rs-8436757","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}