Analysis of interpolation schemes for image deformation methods in PIV

Image deformation methods in particle image velocimetry are becoming more and more accepted by the scientific community but some aspects have not been thoroughly investigated neither theoretically nor with the aid of simulations. A fundamental step in this type of algorithm is reconstruction of the deformed images that requires the use of an interpolation scheme. The aim of this paper is to examine the influence of this aspect on the accuracy of the PIV algorithm. The performance assessment has been conducted using synthetic images and the results show that both the systematic and total errors are strongly influenced by the interpolation scheme used in the reconstruction of the deformed images. Time performances and the influence of particle diameter are also analysed.

[1]  Thomas Leweke,et al.  Analysis and treatment of errors due to high velocity gradients in particle image velocimetry , 2003 .

[2]  L. Vervisch,et al.  Estimation of the accuracy of PIV treatments for turbulent flow studies by direct numerical simulation of multi-phase flow , 2001 .

[3]  D. Hart.  Super-resolution PIV by recursive local-correlation , 2000 .

[4]  Steven T. Wereley,et al.  A correlation-based continuous window-shift technique to reduce the peak-locking effect in digital PIV image evaluation , 2002 .

[5]  Fulvio Scarano,et al.  Iterative multigrid approach in PIV image processing with discrete window offset , 1999 .

[6]  Leonid P. Yaroslavsky,et al.  Signal sinc-interpolation: a fast computer algorithm , 1996 .

[7]  F. Scarano.  Iterative image deformation methods in PIV , 2002 .

[8]  Fulvio Scarano,et al.  A super-resolution particle image velocimetry interrogation approach by means of velocity second derivatives correlation , 2004 .

[9]  J. Westerweel,et al.  The effect of a discrete window offset on the accuracy of cross-correlation analysis of digital PIV recordings , 1997 .

[10]  J. Nogueira,et al.  Local field correction PIV, implemented by means of simple algorithms, and multigrid versions , 2001 .

[11]  H. E. Fiedler,et al.  Limitation and improvement of PIV , 1993 .

[12]  Markus Raffel,et al.  Particle Image Velocimetry: A Practical Guide , 2002 .

[13]  Michael Unser,et al.  B-spline signal processing. I. Theory , 1993, IEEE Trans. Signal Process..

[14]  J. Westerweel.  Theoretical analysis of the measurement precision in particle image velocimetry , 2000 .

[15]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART I-THEORY , 1993 .

[16]  T. Utami,et al.  A cross-correlation technique for velocity field extraction from particulate visualization , 1991 .

[17]  Akram Aldroubi,et al.  B-SPLINE SIGNAL PROCESSING: PART II-EFFICIENT DESIGN AND APPLICATIONS , 1993 .

[18]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[19]  Fulvio Scarano,et al.  Advances in iterative multigrid PIV image processing , 2000 .

[20]  Richard D. Keane,et al.  Theory of cross-correlation analysis of PIV images , 1992 .

[21]  J. Soria.  An investigation of the near wake of a circular cylinder using a video-based digital cross-correlation particle image velocimetry technique , 1996 .

[22]  J. Westerweel.  Digital particle image velocimetry: theory and application , 1993 .

[23]  Carl D. Meinhart,et al.  Second-order accurate particle image velocimetry , 2001 .

[24]  B. N. Dobbins,et al.  An improved cross correlation technique for particle image velocimetry , 1995 .

[25]  C. Willert,et al.  Digital particle image velocimetry , 1991 .

[26]  Akram Aldroubi,et al.  B-spline signal processing. II. Efficiency design and applications , 1993, IEEE Trans. Signal Process..

[27]  J. Nogueira,et al.  Local field correction PIV: on the increase of accuracy of digital PIV systems , 1999 .