Optimisation of temporal averaging processes in PIV


A hybrid of correlation and vector averaging is introduced to capitalise on the advantages of each process. An extensive series of Monte Carlo simulations have been conducted to investigate hybrid averaging and evaluate it against both vector and correlation averaging. The simulations show that hybrid averaging improves the measurement accuracy over both correlation and vector averaging over a wide range of imaging conditions. The simulations are validated by applying hybrid averaging to experimental micro- and macro-flows. In pulsatile conditions, correlation averaging yields an averaged correlation function that is multi-modal, which can result in unpredictable measurements. A Monte Carlo simulation shows the benefits of hybrid averaging over correlation averaging in such conditions. This has been experimentally validated on the unsteady wake behind a shedding circular cylinder at Re = 98.

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  1. 1.

    Relative accuracy of the PIV error from hybrid averaging to the PIV error from vector averaging.


E n :

Image signal-to-noise ratio

M :

Total number of image pairs within an image series

N :

Number of image pairs in a correlation average

ppw :

Particles per sampling window

Q :

Number of image pairs in a vector average

r :

Vector validation threshold


Signal-to-noise ratio

U :

Magnitude of the velocity components, u and v

W :

Sampling window size

V :

Volumetric flow rate

ρ1 :

Average number of particle image pairs in a sampling window

ρeffective :

Apparent number of particle image pairs

σPIV :

Standard deviation of the vector error in pixels

μPIV :

Mean of the vector error in pixels


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A. F. acknowledges discussions with Dr. David Lo Jacono. The authors would like to acknowledge the support of Mr. Jordan Thurgood in conducting the experiments and Mr. Ivan Ng for the circular cylinder experimental image sequences. Support from the Australian Research Council under Discovery Grants DP0877327 and DP0987643 are gratefully acknowledged. C.R.S. is a recipient of an Australian Postgraduate Award.

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Correspondence to Andreas Fouras.

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Samarage, C.R., Carberry, J., Hourigan, K. et al. Optimisation of temporal averaging processes in PIV. Exp Fluids 52, 617–631 (2012). https://doi.org/10.1007/s00348-011-1080-8

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  • Vector Average
  • Image Noise
  • Image Pair
  • Correlation Average
  • Seeding Density