Machine learning for flow field measurements: a perspective

Discetti, Stefano; Liu, Yingzheng

Publication:
Machine learning for flow field measurements: a perspective

dc.affiliation.dpto	UC3M. Departamento de Ingeniería Aeroespacial	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Ingeniería Aeroespacial	es
dc.contributor.author	Discetti, Stefano
dc.contributor.author	Liu, Yingzheng
dc.contributor.funder	European Commission	en
dc.date.accessioned	2022-11-30T09:18:48Z
dc.date.available	2022-11-30T09:18:48Z
dc.date.issued	2023-02
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.	en
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).	en
dc.format.extent	19
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	en
dc.identifier.doi	https://doi.org/10.1088/1361-6501/ac9991
dc.identifier.issn	0957-0233
dc.identifier.publicationfirstpage	1
dc.identifier.publicationissue	2, 021001
dc.identifier.publicationlastpage	19
dc.identifier.publicationtitle	Measurement Science and Technology	en
dc.identifier.publicationvolume	34
dc.identifier.uri	https://hdl.handle.net/10016/36131
dc.identifier.uxxi	AR/0000031486
dc.language.iso	eng	en
dc.publisher	IOP Publising	en
dc.relation.projectID	info:eu-repo/grantAgreement/EC/H2020/949085	en
dc.rights	© 2022 IOP Publishing Ltd	en
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/es/	*
dc.subject.eciencia	Ingeniería Mecánica	es
dc.subject.other	Machine learning	en
dc.subject.other	Flow-field measurements	en
dc.subject.other	Image processing	en
dc.subject.other	Particle image velocimetry	en
dc.title	Machine learning for flow field measurements: a perspective	en
dc.type	research article	*
dc.type.hasVersion	SMUR	*
dspace.entity.type	Publication