Publication:
Machine learning for flow field measurements: a perspective

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URI: https://hdl.handle.net/10016/36131
ISSN: 0957-0233
DOI: https://doi.org/10.1088/1361-6501/ac9991
UXXI: AR/0000031486
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Machine_Disceti_2022_pe.pdf (3.16 MB)
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2023-02
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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.
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Machine learning, Flow-field measurements, Image processing, Particle image velocimetry
Bibliographic citation
Discetti, S. & Liu, Y. Machine learning for flow field measurements: a perspective. In: Measurement Science and Technology, 34(2), 021001, Feb. 2023
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