{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:53:20Z","timestamp":1762509200897,"version":"3.41.2"},"reference-count":0,"publisher":"American Society of Mechanical Engineers","license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.asme.org\/publications-submissions\/publishing-information\/legal-policies"}],"content-domain":{"domain":["asmedigitalcollection.asme.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,11,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>This work proposes a novel approach that combines an optimal sparse sensor placement algorithm with a Convolutional Neural Network (CNN) to accurately reconstruct computational fluid dynamics (CFD) simulations. Fluid mechanics is a field with an ever-increasing amount of high-quality data, and machine learning offers new opportunities to extract essential flow features and enable real-time predictions and control.<\/jats:p>\n               <jats:p>The key challenge addressed herein is to determine the optimal placement of a minimal number of sensors to capture the complexity of fluid flows while enabling fast CNN-based reconstruction. The proposed methodology was applied to a lid-driven cavity flow dataset comprising 100 steady laminar simulations of the vertical velocity component. The sparse sensor placement algorithm was used to determine the optimal sensor positions, which were then used to transform the original CFD dataset to feed into the CNN.<\/jats:p>\n               <jats:p>The results demonstrate the approach\u2019s effectiveness, achieving an accuracy of 95.50% using only 1000 sensors out of a possible set of 16384 and a single Proper Orthogonal Decomposition (POD) mode. This work has the potential to significantly reduce the computational burden associated with CFD simulations within Digital Twins, enabling real-time predictions and control for various fluid flow applications.<\/jats:p>","DOI":"10.1115\/imece2024-139077","type":"proceedings-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T13:05:58Z","timestamp":1737637558000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":3,"title":["Theoretical Framework and Use of CNN Reconstruction With Optimal Sparse Sensor Placement in a Flow Field"],"prefix":"10.1115","author":[{"given":"David","family":"Gomes","sequence":"additional","affiliation":[{"name":"University of Beira Interior , ,","place":["Covilh\u00e3, Portugal"]}]},{"given":"Ant\u00f3nio","family":"Esp\u00edrito Santo","sequence":"additional","affiliation":[{"name":"University of Beira Interior , ,","place":["Covilh\u00e3, Portugal"]}]},{"given":"Jos\u00e9 C.","family":"P\u00e1scoa","sequence":"additional","affiliation":[{"name":"University of Beira Interior , ,","place":["Covilh\u00e3, Portugal"]}]}],"member":"33","published-online":{"date-parts":[[2025,1,23]]},"event":{"name":"ASME 2024 International Mechanical Engineering Congress and Exposition","start":{"date-parts":[[2024,11,17]]},"sponsor":["ASME"],"location":"Portland, Oregon, USA","end":{"date-parts":[[2024,11,21]]},"acronym":"IMECE2024"},"container-title":["Volume 8: Fluids Engineering"],"original-title":[],"link":[{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings-pdf\/doi\/10.1115\/IMECE2024-139077\/7427676\/v008t10a007-imece2024-139077.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings-pdf\/doi\/10.1115\/IMECE2024-139077\/7427676\/v008t10a007-imece2024-139077.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T13:05:58Z","timestamp":1737637558000},"score":1,"resource":{"primary":{"URL":"https:\/\/asmedigitalcollection.asme.org\/IMECE\/proceedings\/IMECE2024\/88667\/V008T10A007\/1212014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,17]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1115\/imece2024-139077","relation":{},"subject":[],"published":{"date-parts":[[2024,11,17]]},"article-number":"V008T10A007"}}