Machine learning for flow field measurements: a perspective

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Show simple item record Discetti, Stefano Liu, Yingzheng 2022-11-30T09:18:48Z 2022-11-30T09:18:48Z 2023-02
dc.identifier.bibliographicCitation Discetti, S. & Liu, Y. Machine learning for flow field measurements: a perspective. In: Measurement Science and Technology, 34(2), 021001, Feb. 2023
dc.identifier.issn 0957-0233
dc.description.abstract Advancements in machine-learning (ML) techniques are driving a paradigm shift in image processing. Flow diagnostics with optical techniques is not an exception. Considering the existing and foreseeable disruptive developments in flow field measurement techniques, we elaborate this perspective, particularly focused to the field of particle image velocimetry. The driving forces for the advancements in ML methods for flow field measurements in recent years are reviewed in terms of image preprocessing, data treatment and conditioning. Finally, possible routes for further developments are highlighted.
dc.description.sponsorship Stefano Discetti acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 949085). Yingzheng Liu acknowledges financial support from the National Natural Science Foundation of China (11725209).
dc.format.extent 19
dc.language.iso eng
dc.publisher IOP Publising
dc.rights © 2022 IOP Publishing Ltd
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Machine learning
dc.subject.other Flow-field measurements
dc.subject.other Image processing
dc.subject.other Particle image velocimetry
dc.title Machine learning for flow field measurements: a perspective
dc.type article
dc.subject.eciencia Ingeniería Mecánica
dc.rights.accessRights openAccess
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/949085
dc.type.version submittedVersion
dc.identifier.publicationfirstpage 1
dc.identifier.publicationissue 2, 021001
dc.identifier.publicationlastpage 19
dc.identifier.publicationtitle Measurement Science and Technology
dc.identifier.publicationvolume 34
dc.identifier.uxxi AR/0000031486
dc.contributor.funder European Commission
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