Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning | 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 Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning Mohamed Elrefaie, Steffen Hüttig, Mariia Gladkova, Timo Gericke, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3875828/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Experiments in Fluids → Version 1 posted 7 You are reading this latest preprint version Abstract We introduce Recurrent All-Pairs Field Transforms for Stereoscopic ParticleImage Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flowlearning to analyze time-resolved and double-frame particle images from on-sitemeasurements, particularly from the 'Ring of Fire,' as well as from wind tunnelmeasurements for real-time aerodynamic analysis. A multi-fidelity datasetcomprising both Reynolds-Averaged Navier-Stokes (RANS) and Direct NumericalSimulation (DNS) was used to train our model. RAFT-StereoPIV outperforms allPIV state-of-the-art deep learning models on benchmark datasets, with a 68% error reduction on the validation dataset, Problem Class 2, and a 47% errorreduction on the unseen test dataset, Problem Class 1, demonstrating itsrobustness and generalizability. In comparison to the most recent works in thefield of deep learning for PIV, where the main focus was the methodologydevelopment and the application was limited to either 2D flow cases or simpleexperimental data, we extend deep learning-based PIV for industrialapplications and 3D flow field estimation. As we apply the trained network tothree-dimensional highly turbulent PIV data, we are able to obtain flowestimates that maintain spatial resolution of the input image sequence. Incontrast, the traditional methods produce the flow field of~16 × lower resolution. We believe that this study brings the field of experimentalfluid dynamics one step closer to the long-term goal of having experimentalmeasurement systems that can be used for real-time flow field estimation. Deep Optical Flow Learning Car Aerodynamics Stereoscopic PIV Ring of Fire Wind Tunnel Measurements Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2024 Read the published version in Experiments in Fluids → Version 1 posted Editorial decision: Revision requested 16 Feb, 2024 Reviews received at journal 12 Feb, 2024 Reviewers agreed at journal 28 Jan, 2024 Reviewers invited by journal 27 Jan, 2024 Editor assigned by journal 24 Jan, 2024 Submission checks completed at journal 24 Jan, 2024 First submitted to journal 18 Jan, 2024 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-3875828","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269736746,"identity":"3b573c05-7e3d-477e-a6aa-3e3a8920fb89","order_by":0,"name":"Mohamed Elrefaie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYFACHhDBzMDADqQqGCQY+MGibMRoASKGM0Atkm0kamFgMDhGQItue+/BTzcqrBn4mXkfPjhQYyFnfL/HdANDmQ1OLWZnziVL55xJZ5BsZjc2OHBMwtjsGI/ZDYZzabi13MgxkM5tO8xgcJiNTfoDm0TiNpAWRqAIHi3Gv3P/HWawP8zG/uPAP4n6zW1gLf/xaTGTzm0A2sLMxsZwsE0iwYANrOUAHr+cMbPOOZbOI3GYjVniYJ+E4YxjaWU3Es4l49ZyvMf4dk6NtRx/exvjhwPf6uT5mw9vu/GhzA6nFhjgQeUmENQwCkbBKBgFowAfAAA2dVCqk/qEvQAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University of Munich","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"","lastName":"Elrefaie","suffix":""},{"id":269736747,"identity":"5849bb04-82b5-4de5-9351-e2b85f2107f6","order_by":1,"name":"Steffen Hüttig","email":"","orcid":"","institution":"Volkswagen Group (Germany)","correspondingAuthor":false,"prefix":"","firstName":"Steffen","middleName":"","lastName":"Hüttig","suffix":""},{"id":269736748,"identity":"f999ce8d-7b9b-4431-bc9a-1dd9c09a42a6","order_by":2,"name":"Mariia Gladkova","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Mariia","middleName":"","lastName":"Gladkova","suffix":""},{"id":269736749,"identity":"191ce325-9a52-4674-9e9f-69a5a951aff5","order_by":3,"name":"Timo Gericke","email":"","orcid":"","institution":"Volkswagen Group (Germany)","correspondingAuthor":false,"prefix":"","firstName":"Timo","middleName":"","lastName":"Gericke","suffix":""},{"id":269736750,"identity":"30a0bf88-05b2-4e43-acbd-0d387778b2cc","order_by":4,"name":"Daniel Cremers","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Cremers","suffix":""},{"id":269736751,"identity":"4d93be57-9283-426a-864b-281dcbf76a3a","order_by":5,"name":"Christian Breitsamter","email":"","orcid":"","institution":"Technical University of Munich","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Breitsamter","suffix":""}],"badges":[],"createdAt":"2024-01-18 12:59:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3875828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3875828/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00348-024-03914-w","type":"published","date":"2024-11-27T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70382863,"identity":"203ebbc1-5dc6-46bc-a9f4-b08758f65ff0","added_by":"auto","created_at":"2024-12-02 16:34:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4270238,"visible":true,"origin":"","legend":"","description":"","filename":"ExperimentsinfluidsRAFTStereoPIVfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3875828/v1_covered_312139ff-3363-49a2-bedb-8c13c1e79fc4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning","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":"experiments-in-fluids","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"exif","sideBox":"Learn more about [Experiments in Fluids](http://link.springer.com/journal/348)","snPcode":"348","submissionUrl":"https://submission.nature.com/new-submission/348/3","title":"Experiments in Fluids","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep Optical Flow Learning, Car Aerodynamics, Stereoscopic PIV, Ring of Fire, Wind Tunnel Measurements","lastPublishedDoi":"10.21203/rs.3.rs-3875828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3875828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe introduce Recurrent All-Pairs Field Transforms for Stereoscopic ParticleImage Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flowlearning to analyze time-resolved and double-frame particle images from on-sitemeasurements, particularly from the 'Ring of Fire,' as well as from wind tunnelmeasurements for real-time aerodynamic analysis. A multi-fidelity datasetcomprising both Reynolds-Averaged Navier-Stokes (RANS) and Direct NumericalSimulation (DNS) was used to train our model. RAFT-StereoPIV outperforms allPIV state-of-the-art deep learning models on benchmark datasets, with a 68% error reduction on the validation dataset, Problem Class 2, and a 47% errorreduction on the unseen test dataset, Problem Class 1, demonstrating itsrobustness and generalizability. In comparison to the most recent works in thefield of deep learning for PIV, where the main focus was the methodologydevelopment and the application was limited to either 2D flow cases or simpleexperimental data, we extend deep learning-based PIV for industrialapplications and 3D flow field estimation. As we apply the trained network tothree-dimensional highly turbulent PIV data, we are able to obtain flowestimates that maintain spatial resolution of the input image sequence. Incontrast, the traditional methods produce the flow field of~16 × lower resolution. We believe that this study brings the field of experimentalfluid dynamics one step closer to the long-term goal of having experimentalmeasurement systems that can be used for real-time flow field estimation.\u003c/p\u003e","manuscriptTitle":"Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 02:42:39","doi":"10.21203/rs.3.rs-3875828/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-02-17T01:13:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-13T04:07:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17188456-6107-4401-b7a4-eb7abb756a15","date":"2024-01-28T18:18:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-28T01:33:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-25T03:20:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-24T17:39:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Experiments in Fluids","date":"2024-01-18T12:54:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"experiments-in-fluids","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"exif","sideBox":"Learn more about [Experiments in Fluids](http://link.springer.com/journal/348)","snPcode":"348","submissionUrl":"https://submission.nature.com/new-submission/348/3","title":"Experiments in Fluids","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"78f2b47f-534f-44c4-8bd7-acddda9f49b1","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T16:04:57+00:00","versionOfRecord":{"articleIdentity":"rs-3875828","link":"https://doi.org/10.1007/s00348-024-03914-w","journal":{"identity":"experiments-in-fluids","isVorOnly":false,"title":"Experiments in Fluids"},"publishedOn":"2024-11-27 15:58:10","publishedOnDateReadable":"November 27th, 2024"},"versionCreatedAt":"2024-01-29 02:42:39","video":"","vorDoi":"10.1007/s00348-024-03914-w","vorDoiUrl":"https://doi.org/10.1007/s00348-024-03914-w","workflowStages":[]},"version":"v1","identity":"rs-3875828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3875828","identity":"rs-3875828","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}