Experiments in Fluids

, Volume 50, Issue 1, pp 135–147 | Cite as

Integrating cross-correlation and relaxation algorithms for particle tracking velocimetry

Research Article

Abstract

An integrated cross-correlation/relaxation algorithm for particle tracking velocimetry is presented. The aim of this integration is to provide a flexible methodology able to analyze images with different seeding and flow conditions. The method is based on the improvement of the individual performance of both matching methods by combining their characteristics in a two-stage process. Analogous to the hybrid particle image velocimetry method, the combined algorithm starts with a solution obtained by the cross-correlation algorithm, which is further refined by the application of the relaxation algorithm in the zones where the cross-correlation method shows low reliability. The performance of the three algorithms, cross-correlation, relaxation method and the integrated cross-correlation/relaxation algorithm, is compared and analyzed using synthetic and large-scale experimental images. The results show that in case of high velocity gradients and heterogeneous seeding, the integrated algorithm improves the overall performance of the individual algorithms on which it is based, in terms of number of valid recovered vectors, with a lower sensitivity to the individual control parameters.

Notes

Acknowledgments

The first two authors are indebted to their coauthor, the late Prof. Gerhard H. Jirka, for his enthusiastic support and wise guidance during the time we worked together. His memory will always be with us.

The University of Chile and the Karlsruhe Institute of Technology supported this work. The authors gratefully acknowledge the support provided by the German Science Foundation (DFG Grant JI 18/18-1), the scholarship program of the National (Chilean) Commission of Science and Technology research, CONICYT, and the support from Fondecyt Project 1080617. The authors are grateful to Prof. W.S.J. Uijttewaal, who allowed the use of his experimental database for the analysis of the ICCRM algorithm. Finally, we would like to thank the contribution of three anonymous reviewers who contributed importantly to improve the quality of the present work.

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Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Institute for HydromechanicsKarlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.Department of Civil Engineering and Advanced Mining Technology CenterUniversidad de ChileSantiagoChile

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