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Real-time adaptive particle image velocimetry for accurate unsteady flow field measurements

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Abstract

Almost all conventional open-loop particle image velocimetry (PIV) methods employ fixed-interval-time optical imaging technology and the time-consuming cross-correlation-based PIV measurement algorithm to calculate the velocity field. In this study, a novel real-time adaptive particle image velocity (RTA-PIV) method is proposed to accurately measure the instantaneous velocity field of an unsteady flow field. In the proposed closed-loop RTA-PIV method, a new correlation-filter-based PIV measurement algorithm is introduced to calculate the velocity field in real time. Then, a Kalman predictor model is established to predict the velocity of the next time instant and a suitable interval time can be determined. To adaptively adjust the interval time for capturing two particle images, a new high-speed frame-straddling vision system is developed for the proposed RTA-PIV method. To fully analyze the performance of the RTA-PIV method, we conducted a series of numerical experiments on ground-truth image pairs and on real-world image sequences.

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Authors and Affiliations

  1. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    ZhenXing Ouyang, Hua Yang, Jin Lu, YongAn Huang & ZhouPing Yin

Authors
  1. ZhenXing Ouyang
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  2. Hua Yang
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  3. Jin Lu
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  4. YongAn Huang
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  5. ZhouPing Yin
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Corresponding author

Correspondence to Hua Yang.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant No. 51875228), the National Key R&D Program of China (Grant No. 2020YFA0405700), and the National Defense Science and Technology Innovation Special Zone Project (Grant No. 193-A14-202-01-23).

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Ouyang, Z., Yang, H., Lu, J. et al. Real-time adaptive particle image velocimetry for accurate unsteady flow field measurements. Sci. China Technol. Sci. 65, 2143–2155 (2022). https://doi.org/10.1007/s11431-022-2082-4

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  • DOI: https://doi.org/10.1007/s11431-022-2082-4

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