Experiments in Fluids

, 56:25 | Cite as

Tomo-PIV measurement of flow around an arbitrarily moving body with surface reconstruction

Research Article


A three-dimensional surface of an arbitrarily moving body in a flow field was reconstructed using the DAISY descriptor and epipolar geometry constraints. The surface shape of a moving body was reconstructed with tomographic PIV flow measurement. Experimental images were captured using the tomographic PIV system, which consisted of four high-speed cameras and a laser. The originally captured images, which contained the shape of the arbitrary moving body and the tracer particles, were separated into the particle and surface images using a Gaussian smoothing filter. The weak contrast of the surface images was enhanced using a local histogram equalization method. The histogram-equalized surface images were used to reconstruct the surface shape of the moving body. The surface reconstruction method required a sufficiently detailed surface pattern to obtain the intensity gradient profile of the local descriptor. The separated particle images were used to reconstruct the particle volume intensity via tomographic reconstruction approaches. Voxels behind the reconstructed body surface were neglected during the tomographic reconstruction and velocity calculation. The three-dimensional three-component flow vectors were calculated based on the cross-correlation functions between the reconstructed particle volumes. Three-dimensional experiments that modeled the flows around a flapping flag, a rotating cylinder, and a flapping robot fish tail were conducted to validate the present technique.


Particle Image Velocimetry Surface Reconstruction Epipolar Line Tomographic Particle Image Velocimetry Epipolar Constraint 
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.



This study was supported by the Creative Research Initiatives (No. 2014-001493) program through the National Research Foundation of Korea.


  1. Adhikari D, Longmire E (2012) Visual hull method for tomographic PIV measurement of flow around moving objects. Exp Fluids 53:943–964CrossRefGoogle Scholar
  2. Arroyo MP, Greated CA (1991) Stereoscopic particle image velocimetry. Meas Sci Technol 2:1181CrossRefGoogle Scholar
  3. Arroyo MP, Hinsch KD (2008) Recent developments of PIV towards 3D measurements particle image velocimetry. Springer, Berlin, pp 127–154Google Scholar
  4. Atkinson C, Soria J (2009) An efficient simultaneous reconstruction technique for tomographic particle image velocimetry. Exp Fluids 47:553–568CrossRefGoogle Scholar
  5. Barnhart DH, Adrian RJ, Menhart C, Papen GC (1995) Phase-conjugate holographic system for high-resolution particle image velocimetry through thick-walled curved windows. In: SPIE’s 1995 international symposium on optical science, engineering, and instrumentation. International Society for Optics and Photonics, pp 165–175Google Scholar
  6. Belden J, Truscott TT, Axiak MC, Techet AH (2010) Three-dimensional synthetic aperture particle image velocimetry. Meas Sci Technol 21:125403CrossRefGoogle Scholar
  7. Brücker C (1995) Digital-particle-image-velocimetry (DPIV) in a scanning light-sheet: 3D starting flow around a short cylinder. Exp Fluids 19:255–263CrossRefGoogle Scholar
  8. Campbell RL, Paterson EG (2011) Fluid–structure interaction analysis of flexible turbomachinery. J Fluids Struct 27:1376–1391. doi: 10.1016/j.jfluidstructs.2011.08.010 CrossRefGoogle Scholar
  9. Elsinga G, Scarano F, Wieneke B, van Oudheusden B (2006) Tomographic particle image velocimetry. Exp Fluids 41:933–947CrossRefGoogle Scholar
  10. Gomes JP, Yigit S, Lienhart H, Schäfer M (2011) Experimental and numerical study on a laminar fluid–structure interaction reference test case. J Fluids Struct 27:43–61. doi: 10.1016/j.jfluidstructs.2010.09.004 CrossRefGoogle Scholar
  11. Hartley RI, Sturm P (1997) Triangulation. Comput Vis Image Underst 68:146–157CrossRefGoogle Scholar
  12. Hou G, Wang J, Layton A (2012) Numerical methods for fluid–structure interaction—a review. Commun Comput Phys 12:337–377MathSciNetGoogle Scholar
  13. Huang W-X, Sung HJ (2009) An immersed boundary method for fluid–flexible structure interaction. Comput Methods Appl Mech Eng 198:2650–2661CrossRefMATHGoogle Scholar
  14. Hwang TG, Doh DH, Jo HJ et al (2007) Analysis of fluid–elastic–structure interactions in an impinging jet with a dynamic 3D-PTV and non-contact 6D-motion tracking system. Chem Eng J 130:153–164. doi: 10.1016/j.cej.2006.06.018 CrossRefGoogle Scholar
  15. Jeon YJ, Sung HJ (2011) PIV measurement of flow around an arbitrarily moving body. Exp Fluids 50:787–798CrossRefGoogle Scholar
  16. Jeon YJ, Sung HJ (2012) Three-dimensional PIV measurement of flow around an arbitrarily moving body. Exp Fluids 53:1057–1071CrossRefGoogle Scholar
  17. Lee D-T, Schachter BJ (1980) Two algorithms for constructing a Delaunay triangulation. Int J Comput Inform Sci 9:219–242CrossRefMathSciNetMATHGoogle Scholar
  18. Maas H, Gruen A, Papantoniou D (1993) Particle tracking velocimetry in three-dimensional flows. Exp Fluids 15:133–146CrossRefGoogle Scholar
  19. Mishra D, Muralidhar K, Munshi P (1999) A robust MART algorithm for tomographic applications. Numer Heat Trans Part B 35:485–506CrossRefGoogle Scholar
  20. Pereira F, Gharib M, Dabiri D, Modarress D (2000) Defocusing digital particle image velocimetry: a 3-component 3-dimensional DPIV measurement technique. Application to bubbly flows. Exp Fluids 29:S078–S084CrossRefGoogle Scholar
  21. Scarano F (2013) Tomographic PIV: principles and practice. Meas Sci Technol 24:012001CrossRefGoogle Scholar
  22. Shelley MJ, Zhang J (2011) Flapping and bending bodies interacting with fluid flows. Annu Rev Fluid Mech 43:449–465. doi: 10.1146/annurev-fluid-121108-145456 CrossRefMathSciNetGoogle Scholar
  23. Siddiqui MHK (2007) Velocity measurements around a freely swimming fish using PIV. Meas Sci Technol 18:96–105. doi: 10.1088/0957-0233/18/1/012 CrossRefGoogle Scholar
  24. Sonka M, Hlavac V, Boyle R (1999) Image processing, analysis, and machine vision. PWS Publishing, Pacific GroveGoogle Scholar
  25. Tola E, Lepetit V, Fua P (2008) A fast local descriptor for dense matching. In: IEEE conference on computer vision and pattern recognition, 2008 (CVPR 2008), IEEE, pp 1–8Google Scholar
  26. Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32:815–830CrossRefGoogle Scholar
  27. Westerweel J, Scarano F (2005) Universal outlier detection for PIV data. Exp Fluids 39:1096–1100. doi: 10.1007/s00348-005-0016-6 CrossRefGoogle Scholar
  28. Wieneke B (2008) Volume self-calibration for 3D particle image velocimetry. Exp Fluids 45:549–556CrossRefGoogle Scholar
  29. Wu TY (2011) Fish swimming and bird/insect flight. Annu Rev Fluid Mech 43:25–58. doi: 10.1146/annurev-fluid-122109-160648 CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringKAISTDaejeonKorea
  2. 2.Institut P PRIME, UPR3346CNRS – Université de Poitiers – ISAE-ENSMAFuturoscope CedexFrance

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