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Experimental dataset investigation of deep recurrent optical flow learning for particle image velocimetry: flow past a circular cylinder
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- Anjaneya Reddy, Yuvarajendra (författare)
- Luleå tekniska universitet,Strömningslära och experimentell mekanik
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- Wahl, Joel (författare)
- Luleå tekniska universitet,Strömningslära och experimentell mekanik
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- Sjödahl, Mikael, 1965- (författare)
- Luleå tekniska universitet,Strömningslära och experimentell mekanik
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(creator_code:org_t)
- Institute of Physics (IOP), 2024
- 2024
- Engelska.
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Ingår i: Measurement science and technology. - : Institute of Physics (IOP). - 0957-0233 .- 1361-6501. ; 35:8
- Relaterad länk:
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https://doi.org/10.1...
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Current optical flow-based neural networks for particle image velocimetry (PIV) are largely trained on synthetic datasets emulating real-world scenarios. While synthetic datasets provide greater control and variation than what can be achieved using experimental datasets for supervised learning, it requires a deeper understanding of how or what factors dictate the learning behaviors of deep neural networks for PIV. In this study, we investigate the performance of the recurrent all-pairs field transforms-PIV (RAFTs-PIV) network, the current state-of-the-art deep learning architecture for PIV, by testing it on unseen experimentally generated datasets. The results from RAFT-PIV are compared with a conventional cross-correlation-based method, Adaptive PIV. The experimental PIV datasets were generated for a typical scenario of flow past a circular cylinder in a rectangular channel. These test datasets encompassed variations in particle diameters, particle seeding densities, and flow speeds, all falling within the parameter range used for training RAFT-PIV. We also explore how different image pre-processing techniques can impact and potentially enhance the performance of RAFT-PIV on real-world datasets. Thorough testing with real-world experimental PIV datasets reveals the resilience of the optical flow-based method's variations to PIV hyperparameters, in contrast to the conventional PIV technique. The ensemble-averaged root mean squared errors between the RAFT-PIV and Adaptive PIV estimations generally range between 0.5–2 (px) and show a slight reduction as particle densities increase or Reynolds numbers decrease. Furthermore, findings indicate that employing image pre-processing techniques to enhance input particle image quality does not improve RAFT-PIV predictions; instead, it incurs higher computational costs and impacts estimations of small-scale structures.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Strömningsmekanik och akustik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Fluid Mechanics and Acoustics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Annan teknik -- Övrig annan teknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Other Engineering and Technologies -- Other Engineering and Technologies not elsewhere specified (hsv//eng)
Nyckelord
- particle image velocimetry
- experimental dataset
- deep learning
- optical flow
- Experimentell mekanik
- Experimental Mechanics
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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