Science China Technological Sciences

, Volume 61, Issue 6, pp 867–878 | Cite as

Spatial pyramidal cross correlation for particle image velocimetry

  • HongPing Wang
  • Peng Wu
  • Qi Gao
  • JianJie Wang
  • JinJun Wang


A spatial pyramidal cross-correlation based on interrogation area sub-division is introduced to improve the measurement resolution in particle image velocimetry (PIV). The high-resolution velocity can be achieved with a velocity prediction model via coarse cross-correlation. The prediction formula is deduced from the frequency response of the moving average (MA). The performance of this method was assessed using synthetically generated images of sinusoidal shear flow, two-dimensional vortical cellular flow, and homogeneous turbulence. A real PIV experiment of turbulent boundary layer was used to evaluate the new method. The results indicate that the spatial pyramid cross-correlation can robustly increase the spatial resolution.


particle image velocimetry (PIV) cross correlation spatial resolution moving average 


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  1. 1.
    Adrian R J. Particle-imaging techniques for experimental fluid mechanics. Annu Rev Fluid Mech, 1991, 23: 261–304CrossRefGoogle Scholar
  2. 2.
    Nogueira J, Lecuona A, Rodríguez P A. Local field correction PIV: On the increase of accuracy of digital PIV systems. Exp Fluids, 1999, 27: 107–116CrossRefGoogle Scholar
  3. 3.
    Scarano F. Iterative image deformation methods in PIV. Meas Sci Technol, 2002, 13: R1–R19CrossRefGoogle Scholar
  4. 4.
    Huang H T, Fiedler H E. Deformed particle image pattern matching in particle image velocimetry. Appl Sci Res, 1993, 51: 179–183CrossRefGoogle Scholar
  5. 5.
    Scarano F, Riethmuller M L. Advances in iterative multigrid PIV image processing. Exp Fluids, 2000, 29: S051–S060CrossRefGoogle Scholar
  6. 6.
    Schrijer F F J, Scarano F. Effect of predictor-corrector filtering on the stability and spatial resolution of iterative PIV interrogation. Exp Fluids, 2008, 45: 927–941CrossRefGoogle Scholar
  7. 7.
    Stitou A, Riethmuller M L. Extension of PIV to super resolution using PTV. Meas Sci Technol, 2001, 12: 1398–1403CrossRefGoogle Scholar
  8. 8.
    Scarano F. A super-resolution particle image velocimetry interrogation approach by means of velocity second derivatives correlation. Meas Sci Technol, 2004, 15: 475–486CrossRefGoogle Scholar
  9. 9.
    Theunissen R, Scarano F, Riethmuller M L. An adaptive sampling and windowing interrogation method in PIV. Meas Sci Technol, 2007, 18: 275–287CrossRefGoogle Scholar
  10. 10.
    Novara M, Ianiro A, Scarano F. Adaptive interrogation for 3D-PIV. Meas Sci Technol, 2013, 24: 024012CrossRefGoogle Scholar
  11. 11.
    Stanislas M, Okamoto K, Kähler C J, et al. Main results of the second international PIV challenge. Exp Fluids, 2005, 39: 170–191CrossRefGoogle Scholar
  12. 12.
    Theunissen R, Scarano F, Riethmuller M L. Spatially adaptive PIV interrogation based on data ensemble. Exp Fluids, 2010, 48: 875–887CrossRefGoogle Scholar
  13. 13.
    Westerweel J, Geelhoed P F, Lindken R. Single-pixel resolution ensemble correlation for micro-PIV applications. Exp Fluids, 2004, 37: 375–384CrossRefGoogle Scholar
  14. 14.
    Meinhart C D, Wereley S T, Santiago J G. A PIV algorithm for estimating time-averaged velocity fields. J Fluids Eng, 2000, 122: 285–289CrossRefGoogle Scholar
  15. 15.
    Sciacchitano A, Scarano F, Wieneke B. Multi-frame pyramid correlation for time-resolved PIV. Exp Fluids, 2012, 53: 1087–1105CrossRefGoogle Scholar
  16. 16.
    Hart D P. PIV error correction. Exp Fluids, 2000, 29: 13–22CrossRefGoogle Scholar
  17. 17.
    Shi S, Chen D. Enhancing particle image tracking performance with a sequential Monte Carlo method: The bootstrap filter. Flow Meas Instrum, 2011, 22: 190–200CrossRefGoogle Scholar
  18. 18.
    Wang H P, Gao Q, Feng L H, et al. Proper orthogonal decomposition based outlier correction for PIV data. Exp Fluids, 2015, 56: 1–15CrossRefGoogle Scholar
  19. 19.
    Westerweel J, Scarano F. Universal outlier detection for PIV data. Exp Fluids, 2005, 39: 1096–1100CrossRefGoogle Scholar
  20. 20.
    Lecordier B, Westerweel J. The EUROPIV synthetic image generator (S.I.G.). In: Stanislas M, Westerweel J, Kompenhans J, Eds. Particle Image Velocimetry: Recent Improvements. Berlin Heidelberg: Springer, 2004. 145–161CrossRefGoogle Scholar
  21. 21.
    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: 104704CrossRefGoogle Scholar
  22. 22.
    Sciacchitano A, Wieneke B, Scarano F. PIV uncertainty quantification by image matching. Meas Sci Technol, 2013, 24: 045302CrossRefGoogle Scholar
  23. 23.
    Astarita T. Analysis of interpolation schemes for image deformation methods in PIV: Effect of noise on the accuracy and spatial resolution. Exp Fluids, 2006, 40: 977–987CrossRefGoogle Scholar
  24. 24.
    Garcia D. A fast all-in-one method for automated post-processing of PIV data. Exp Fluids, 2011, 50: 1247–1259CrossRefGoogle Scholar
  25. 25.
    Carlier B J, Wieneke B. Report 1 on production and diffusion of fluid mechanics images and data. Fluid project deliverable 1.2., 2010Google Scholar
  26. 26.
    Kendall A, Koochesfahani M. A method for estimating wall friction in turbulent wall-bounded flows. Exp Fluids, 2008, 44: 773–780CrossRefGoogle Scholar
  27. 27.
    He G W, Zhang J B. Elliptic model for space-time correlations in turbulent shear flows. Phys Rev E, 2006, 73: 055303CrossRefGoogle Scholar
  28. 28.
    Zhao X, He G W. Space-time correlations of fluctuating velocities in turbulent shear flows. Phys Rev E, 2009, 79: 046316CrossRefGoogle Scholar
  29. 29.
    de Kat R, Ganapathisubramani B. Frequency-wavenumber mapping in turbulent shear flows. J Fluid Mech, 2015, 783: 166–190MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    He G, Jin G, Yang Y. Space-time correlations and dynamic coupling in turbulent flows. Annu Rev Fluid Mech, 2017, 49: 51–70CrossRefMATHGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • HongPing Wang
    • 1
    • 2
  • Peng Wu
    • 3
  • Qi Gao
    • 1
  • JianJie Wang
    • 1
  • JinJun Wang
    • 1
  1. 1.Key Laboratory of Fluid Mechanics, Ministry of EducationBeihang UniversityBeijingChina
  2. 2.State Key Laboratory of Nonlinear Mechanics, Institute of MechanicsChinese Academy of SciencesBeijingChina
  3. 3.Artificial Organ Technology Laboratory, Biomanufacturing Centre, School of Mechanical and Electrical EngineeringSoochow UniversitySuzhouChina

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