Skip to main content
SpringerLink
Log in

Adaptive space resolution for PIV

  • Research Article
  • Published:
Experiments in Fluids Aims and scope Submit manuscript

Abstract

By using standard particle image velocimetry algorithms the interrogation windows are placed on a structured grid and the spatial resolution is manually chosen. Clearly, a better approach is to choose automatically the processing parameters and to adapt them locally both to the seeding density and flow conditions. An adaptive space resolution method is introduced herein and the performance assessment performed by using both synthetic and real images clearly shows the advantages of the technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

IDM:

Image deformation method

MTF:

Modulation transfer function

PIV:

Particle image velocimetry

WW:

Weighting window

a :

Modulation factor associated to the correlation step of the algorithm, dimensionless

b :

Modulation factor associated to the weighted average step of the algorithm, dimensionless

c :

Modulation factor associated to the dense predictor step of the algorithm, dimensionless

k :

Iteration number, dimensionless

m k :

Modulation factor at iteration k, dimensionless

m ks,kf :

Combined modulation factor, dimensionless

N :

Number of samples, dimensionless

p :

Percentage of particles lost between the images, dimensionless

r :

Maximum value of the correlation coefficient map, dimensionless

r c :

Corrector displacement field, pixels

r p :

Dense predictor displacement field, pixels

r k :

Displacement field at iteration k, pixels

\( \overline{{{\user2{r}}_{p}^{k - 1} }} \) :

Weighted average of the dense predictor displacement field, pixels

u :

Horizontal displacement, pixels

\( \bar{u} \) :

Mean, over the interrogation window, horizontal displacement, pixels

v :

Vertical displacement, pixels

W a :

Interrogation window linear dimension, pixels

W b :

Linear dimension of the weighting window used in the weighted average step of the algorithm, pixels

W bo :

Optimal linear dimension of the weighting window used in the weighted average step of the algorithm, pixels

x :

Horizontal image coordinate, pixels

y :

Vertical image coordinate, pixels

δ :

Total error, pixels

δ a :

Weighted average of the total error (all wavelengths), pixels

δ l :

Weighted average of the total error (larger wavelengths), pixels

ζ :

Adaptive parameter, \( r{\sqrt[4]{\sigma}} \)

λ :

Spatial wavelength, pixels

σ :

Standard deviation of the even local part, pixels

Θ:

Standard deviation of the vorticity, dimensionless

s :

Starting phase

f :

Final phase

References

  • Astarita T (2006) Analysis of interpolation schemes for image deformation methods in PIV: effect of noise on the accuracy and spatial resolution. Exp Fluids 40:977–987

    Article  Google Scholar 

  • Astarita T (2007) Analysis of weighting windows for image deformation methods in PIV. Exp Fluids 43:859–872

    Article  Google Scholar 

  • Astarita T (2008) Analysis of velocity interpolation schemes for image deformation methods in PIV. Exp Fluids 45:257–266

    Article  Google Scholar 

  • Carlomagno GM, Nese FG, Cardone G, Astarita T (2004) Thermo-fluid-dynamics of a complex fluid flow. Infrared Phys Technol 46:31–39

    Article  Google Scholar 

  • Nogueira J, Lecuona A, Rodríguez PA (2005) Limits on the resolution of correlation PIV iterative methods. Fundamentals Exp Fluids 39:305–313

    Article  Google Scholar 

  • Rohàly J, Frigerio F, Hart DP (2002) Reverse hierarchical PIV processing. Meas Sci Technol 13:984–996

    Article  Google Scholar 

  • Scarano F (2002) Iterative image deformation methods in PIV. Meas Sci Technol 13:R1–R19

    Article  Google Scholar 

  • Scarano F (2003) Theory of non-isotropic spatial resolution in PIV. Exp Fluids 35:268–277

    Article  Google Scholar 

  • Schrijer FFJ, Scarano F (2008) Effect of predictor–corrector filtering on the stability and spatial resolution of iterative PIV interrogation. Exp Fluids 45:927–941

    Article  Google Scholar 

  • Susset A, Most JM, Honoré D (2006) A novel architecture for a super-resolution PIV algorithm developed for the improvement of the resolution of large velocity gradient measurements. Exp Fluids 40:70–79

    Article  Google Scholar 

  • Theunissen R, Scarano F, Riethmuller ML (2007) An adaptive sampling and windowing interrogation method in PIV. Meas Sci Technol 18:275–287

    Article  Google Scholar 

  • Utami T, Blackwelder RF, Ueno T (1991) A cross-correlation technique for velocity field extraction from particulate visualization. Exp Fluids 10:213–223

    Article  Google Scholar 

  • Willert CE, Gharib M (1991) Digital particle image velocimetry. Exp Fluids 10:181–193

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. DIAS, University of Naples Federico II, P.le Tecchio 80, 80125, Naples, Italy

    T. Astarita

Authors
  1. T. Astarita
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Astarita.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Astarita, T. Adaptive space resolution for PIV. Exp Fluids 46, 1115–1123 (2009). https://doi.org/10.1007/s00348-009-0618-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00348-009-0618-5

Keywords

Search

Navigation