Experiments in Fluids

, Volume 45, Issue 5, pp 857–868 | Cite as

An automated vortex detection scheme using the wavelet transform of the d 2 field

  • A. V. Varun
  • K. Balasubramanian
  • R. I. Sujith
Research Article


A vortex detection scheme using the wavelet transform of the discriminant of the eigenvalues of the velocity gradient matrix (d 2) is presented in this paper. The use of d 2 field results in better eduction capability over the previously used test fields such as enstrophy since it automatically distinguishes shear layers from vortices. Level sets are used to refine the shape of the vortex without causing a huge computational penalty. Further, the algorithm is easily automated to aid batch processing. The detection scheme was applied to swirl flow fields, successfully estimating the vortex location, the core radius and the vortex shape.


Vortex Vorticity Particle Image Velocimetry Shear Layer Particle Image Velocimetry Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Mr. R. Kumara Gurubaran, Mr. E. Arunraj and A. Panduranga Reddy for providing the PIV data for the swirling jets.


  1. Chakraborty P, Balachandar S, Adrian RJ (2005) On the relationships, between local vortex identification schemes. J Fluid Mech 535:189–214zbMATHCrossRefMathSciNetGoogle Scholar
  2. Elsinga GE, Scarano F, Wieneke B, van Oudheusden BW (2006) Tomographic particle image velocimetry. Exp Fluids 41:933–947CrossRefGoogle Scholar
  3. Elsinga GE, Adrian RJ, van Oudheusden BW, Scarano F (2007) Tomographic-PIV investigation of a high Reynolds number turbulent boundary layer. In: Proceedings of 7th international symposium on particle image velocimetry, Rome, Italy, 11–14 SeptemberGoogle Scholar
  4. Farge M (1992) Wavelet transforms and their applications to turbulence. Annu Rev Fluid Mech 24:395–457CrossRefMathSciNetGoogle Scholar
  5. Jeong J, Hussain F (1995) On the identification of a vortex. J Fluid Mech 285:69–94zbMATHCrossRefMathSciNetGoogle Scholar
  6. Kailas SV, Narasimha R (1999) The eduction of structures from flow imagery using wavelets—Part I, The mixing layer, Exp Fluids 27:167–174CrossRefGoogle Scholar
  7. Malladi R, Sethian JA (1995) Image processing via level set curvature flow. Proc Natl Acad Sci USA 92:7046–7050zbMATHCrossRefMathSciNetGoogle Scholar
  8. Pullin DI (1992) Contour dynamics methods. Ann Rev Fluid Mech 24:89–115CrossRefMathSciNetGoogle Scholar
  9. Raffel M, Willert C, Kompenhans J (1998) Particle image velocimetry—a practical guide. Springer, BerlinGoogle Scholar
  10. Ramamurthi K, Patnaik RK, Radhakrishnan A, Reddy PA, Govardhan RN (2005) Flow visualization studies of jets in the presence of loud pure tones. J Flow Vis Image Process 12:197–214CrossRefGoogle Scholar
  11. Reddy PA, Sujith RI, Chakravarthy SR (2006) Swirler flow field characteristics in a sudden expansion combustor geometry. J Propulsion Power 22:800–808CrossRefGoogle Scholar
  12. Schram C (2003) Aeroacoustics of subsonic jets: prediction of the sound produced by vortex pairing based on particle image velocimetry. PhD thesis, Physics Department, Technical University of Eindhoven, The Netherlands Google Scholar
  13. Schram C, Rambaud P, Riethmuller ML (2004) Wavelet based eddy structure eduction from a backward facing step flow investigated using particle image velocimetry. Exp Fluids 36:233–245CrossRefGoogle Scholar
  14. Sethian JA (1996) A fast marching level set method for monotonically advancing fronts. Proc Natl Acad Sci USA 93(4):1591–1595zbMATHCrossRefMathSciNetGoogle Scholar
  15. Vollmers H (2001) Detection of vortices and quantitative evaluation of their main parameters from experimental velocity data. Meas Sci Technol 12:1199–1207CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • A. V. Varun
    • 1
  • K. Balasubramanian
    • 1
  • R. I. Sujith
    • 1
  1. 1.Department of Aerospace EngineeringIndian Institute of Technology MadrasChennaiIndia

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