Physics-Informed Machine Learning Modelling of Hydrokinetic Energy Harvester

Physics-Informed Machine Learning Modelling of Hydrokinetic Energy Harvester | 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 Modelling of Hydrokinetic Energy Harvester Kombo Theophilus-Johnson, Ebughni Okoria Nangi, John Ndaa Samson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124844/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 Hydrokinetic energy converters are environmentally friendly and hold significant potential for bridging the energy gap in remote villages where mainstream electrical grid infrastructure is unavailable. However, the design of these energy conversion systems is complicated by the Multiphysics nature of fluid–structure interactions. Bluff bodies, such as cylinders commonly used in these converters, often exhibit complex fluid–structure interactions known as vortex-induced vibrations (VIV). Traditional low-order models can capture the basic physics of these systems but often struggle to account for nonlinearities and real-world complexities. On the other hand, purely data-driven models may suffer from overfitting or require large volumes of data to perform effectively. This paper presents a physics-informed machine learning (PIML) approach that integrates a simplified VIV model with a neural network trained to learn the unmodelled dynamics from experimental heave displacement measurements. We demonstrate the implementation of this hybrid model using PyTorch and validate its performance with experimental data of a cylinder’s heave response in fluid flow. The proposed PIML model effectively compensates for the unmodelled dynamic characteristics, achieving an 80% improvement in performance. The mean squared error (MSE) decreased from 149.92 in the physics-only model to 25.74 in the PIML model. Hydrokinetic energy harvester Physics-informed machine learning (PIML) physics-informed neural network (PINN) vortex-induced vibration heave motion 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. 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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-7124844","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488719965,"identity":"fafebbd6-c3ec-4a28-8b63-9660238df08e","order_by":0,"name":"Kombo Theophilus-Johnson","email":"data:image/png;base64,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","orcid":"","institution":"Rivers State University","correspondingAuthor":true,"prefix":"","firstName":"Kombo","middleName":"","lastName":"Theophilus-Johnson","suffix":""},{"id":488719966,"identity":"1b7e8798-23c1-45ce-8051-77868ea4da39","order_by":1,"name":"Ebughni Okoria Nangi","email":"","orcid":"","institution":"Rivers State University","correspondingAuthor":false,"prefix":"","firstName":"Ebughni","middleName":"Okoria","lastName":"Nangi","suffix":""},{"id":488719968,"identity":"e9e36d2c-ab72-44c8-ae10-0ee070f08003","order_by":2,"name":"John Ndaa Samson","email":"","orcid":"","institution":"University of Nottingham","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Ndaa","lastName":"Samson","suffix":""}],"badges":[],"createdAt":"2025-07-14 23:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124844/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88765591,"identity":"a25bde30-5c26-4a07-aec5-2f163eacc4fa","added_by":"auto","created_at":"2025-08-11 08:47:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034287,"visible":true,"origin":"","legend":"","description":"","filename":"PhysicsInformedMachineLearningModellingofcylinderHeaveMotionCopy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124844/v1_covered_812f8d00-a2e9-40b6-aa66-ebc52a27f4ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physics-Informed Machine Learning Modelling of Hydrokinetic Energy Harvester","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Hydrokinetic energy harvester, Physics-informed machine learning (PIML), physics-informed neural network (PINN), vortex-induced vibration, heave motion","lastPublishedDoi":"10.21203/rs.3.rs-7124844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHydrokinetic energy converters are environmentally friendly and hold significant potential for bridging the energy gap in remote villages where mainstream electrical grid infrastructure is unavailable. However, the design of these energy conversion systems is complicated by the Multiphysics nature of fluid\u0026ndash;structure interactions. Bluff bodies, such as cylinders commonly used in these converters, often exhibit complex fluid\u0026ndash;structure interactions known as vortex-induced vibrations (VIV). Traditional low-order models can capture the basic physics of these systems but often struggle to account for nonlinearities and real-world complexities. On the other hand, purely data-driven models may suffer from overfitting or require large volumes of data to perform effectively. This paper presents a physics-informed machine learning (PIML) approach that integrates a simplified VIV model with a neural network trained to learn the unmodelled dynamics from experimental heave displacement measurements. We demonstrate the implementation of this hybrid model using PyTorch and validate its performance with experimental data of a cylinder\u0026rsquo;s heave response in fluid flow. The proposed PIML model effectively compensates for the unmodelled dynamic characteristics, achieving an 80% improvement in performance. 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