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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References
Schmidt B E, Sutton J A. Improvements in the accuracy of wavelet-based optical flow velocimetry (wOFV) using an efficient and physically based implementation of velocity regularization. Exp Fluids, 2020, 61: 32
Raffel M, Willert C E, Scarano F, et al. Particle Image Velocimetry: A Practical Guide. Berlin: Springer-Verlag, 2018
Peterson S D, Porfiri M, Rovardi A. A particle image velocimetry study of vibrating ionic polymer metal composites in aqueous environments. IEEE ASME Trans Mechatron, 2009, 14: 474–483
Lee Y, Yang H, Yin Z P. Outlier detection for particle image velocimetry data using a locally estimated noise variance. Meas Sci Technol, 2017, 28: 035301
Liu T, Salazar D M, Fagehi H, et al. Hybrid optical-flow-cross-correlation method for particle image velocimetry. J Fluids Eng, 2020, 142
Wang H P, Wu P, Gao Q, et al. Spatial pyramidal cross correlation for particle image velocimetry. Sci China Tech Sci, 2018, 61: 867–878
Wang H, He G, Wang S. Globally optimized cross-correlation for particle image velocimetry. Exp Fluids, 2020, 61: 228
Pan C, Xue D, Xu Y, et al. Evaluating the accuracy performance of Lucas-Kanade algorithm in the circumstance of PIV application. Sci China-Phys Mech Astron, 2015, 58: 104704
Edwards M, Theunissen R. Adaptive incremental stippling for sample distribution in spatially adaptive PIV image analysis. Meas Sci Technol, 2019, 30: 065301
Theunissen R, Scarano F, Riethmuller M L. Spatially adaptive PIV interrogation based on data ensemble. Exp Fluids, 2010, 48: 875–887
Seong J H, Song M S, Nunez D, et al. Velocity refinement of PIV using global optical flow. Exp Fluids, 2019, 60: 174
Zhou L, Shi W D, Cao W D, et al. CFD investigation and PIV validation of flow field in a compact return diffuser under strong part-load conditions. Sci China Tech Sci, 2015, 58: 405–414
Zhang L R, Xing J K, Wang J W, et al. Experimental study of the wake characteristics of a two-blade horizontal axis wind turbine by time-resolved PIV. Sci China Tech Sci, 2017, 60: 593–601
Kreizer M, Ratner D, Liberzon A. Real-time image processing for particle tracking velocimetry. Exp Fluids, 2010, 48: 105–110
Drazen D, Lichtsteiner P, Häfliger P, et al. Toward real-time particle tracking using an event-based dynamic vision sensor. Exp Fluids, 2011, 51: 1465–1469
Kobatake M, Aoyama T, Takaki T, et al. A real-time microscopic PIV system using frame straddling high-frame-rate vision. J Robot Mechatron, 2013, 25: 586–595
Akbaridoust F, Philip J, Hill D R A, et al. Simultaneous micro-PIV measurements and real-time control trapping in a cross-slot channel. Exp Fluids, 2018, 59: 183
Varon E, Aider J L, Eulalie Y, et al. Adaptive control of the dynamics of a fully turbulent bimodal wake using real-time PIV. Exp Fluids, 2019, 60: 124
Takehara K, Adrian R J, Etoh G T, et al. A Kalman tracker for super-resolution PIV. Exp Fluids, 2000, 29: S034–S041
Shi S, Chen D. Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter. Flow Measurement Instrum, 2011, 22: 190–200
Leroux R, Chatellier L, David L. Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM. Exp Fluids, 2018, 59: 16
Henriques J F, Caseiro R, Martins P, et al. High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 2015, 37: 583–596
Ouyang Z, Yang H, Huang Y, et al. A circulant-matrix-based hybrid optical flow method for PIV measurement with large displacement. Exp Fluids, 2021, 62: 233
Okamoto K, Nishio S, Saga T, et al. Standard images for particle-image velocimetry. Meas Sci Technol, 2000, 11: 685–691
Scarano F. Theory of non-isotropic spatial resolution in PIV Exp Fluids, 2003, 35: 268–277
Wieneke B, Pfeiffer K. Adaptive PIV with variable interrogation window size and shape. In: Proceedings of the International Symposium on Applications of Laser Techniques to Fluid Mechanics. Lisbon, 2010
Ruhnau P, Kohlberger T, Schnörr C, et al. Variational optical flow estimation for particle image velocimetry. Exp Fluids, 2005, 38: 21–32
Corpetti T, Heitz D, Arroyo G, et al. Fluid experimental flow estimation based on an optical-flow scheme. Exp Fluids, 2006, 40: 80–97
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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