Experiments in Fluids

, Volume 48, Issue 4, pp 577–587 | Cite as

Interfacial PIV to resolve flows in the vicinity of curved surfaces

  • Chuong V. Nguyen
  • Thien D. Nguyen
  • John C. Wells
  • Akihiko Nakayama
Research Article
First Online:
Received:
Revised:
Accepted:

Abstract

PIV measurements near a wall are generally difficult due to low seeding density, low velocity, high velocity gradient, and strong reflections. Such problems are often compounded by curved boundaries, which are commonly found in many industrial and medical applications. To systematically solve these problems, this paper presents two novel techniques for near-wall measurement, together named Interfacial PIV, which extracts both wall-shear gradient and near-wall tangential velocity profiles at one-pixel resolution. To deal with curved walls, image strips at a curved wall are stretched into rectangles by means of conformal transformation. To extract the maximal spatial information on the near-wall tangential velocity field, a novel 1D correlation function is performed on each horizontal pixel line of the transformed image template to form a “correlation stack”. This 1D correlation function requires that the wall-normal displacement component of the particles be smaller than the particle image diameter in order to produce a correlation signal. Within the image regions satisfying this condition, the correlation function yields peaks that form a tangential velocity profile. To determine this profile robustly, we propose to integrate gradients of tangential velocity outward from the wall, wherein the gradient at each wall-normal position is measured by fitting a straight line to the correlation peaks. The capability of Interfacial PIV was validated against Particle Image Distortion using synthetic image pairs generated from a DNS velocity field over a sinusoidal bed. Different velocity measurement schemes performed on the same correlation stacks were also demonstrated. The results suggest that Interfacial PIV using line fitting and gradient integration provides the best accuracy of all cases in the measurements of velocity gradient and velocity profile near wall surfaces.

Keywords

Conformal Transformation Image Template Wall Unit Tangential Velocity Profile Gaussian Interpolation 
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.
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Notes

Acknowledgments

The first author would like to express appreciation to Dr. Frédéric Plourde, Laboratoire d’Etudes Thermiques—ENSMA, Poitiers, France, for his challenging question about how to deal with curved walls. We also express our special thanks to Nicolas Buchmann, Department of Mechanical Engineering, University of Canterbury, New Zealand, for his valuable discussion on our technique. We acknowledge Charles Denham’s generosity for making SeaGrid toolbox freely available.

References

  1. Buchmann NA, Nguyen CV, Wells JC, Jermy M (2008) In-vitro wall-shear stress measurements using interfacial particle image velocimetry (IPIV). In: 14th International symposium on applications of laser techniques to fluid mechanics, Lisbon, 07–10 JulyGoogle Scholar
  2. Buchmann NA, Jermy MC, Nguyen CV (2009) Experimental investigation of carotid artery haemodynamics in an anatomically realistic model. Int J Exp Comput Biomech 1(2):172–192CrossRefGoogle Scholar
  3. Denham CR (2000) SeaGrid orthogonal grid maker for Matlab. Woods Hole Science Center, U.S. Geological Survey. http://woodshole.er.usgs.gov/operations/modeling/seagrid/
  4. Gui L, Merzkirch W, Fei R (2000) A digital mask technique for reducing the bias error of the correlation-based PIV interrogation algorithm. Exp Fluids 29(1):30–35CrossRefGoogle Scholar
  5. Gui L, Longo J, Stern F (2001) Biases of PIV measurement of turbulent flow and the masked correlation-based interrogation algorithm. Exp Fluids 30:27–35CrossRefGoogle Scholar
  6. Hochareon MB, Fontaine A (2004) Wall-shear-rate estimation within the 50cc Penn State artificial heart using particle image velocimetry. J Biomech Eng 126:430–437CrossRefGoogle Scholar
  7. Huang HT, Fiedler HE, Wang JJ (1993) Limitation and Improvement of PIV. Exp Fluids 15:263–273Google Scholar
  8. Ives DD, Zacharias RM (1987) Conformal mapping and orthogonal grid generation. AIAA/SAE/ASME/ASEE 23rd joint propulsion conference, Paper No. 87-2057, San DiegoGoogle Scholar
  9. Lecordier B, Westerweel J (2004) The EUROPIV synthetic image generator (S.I.G.). In: Particle image velocimetry: recent improvements. Proceedings of the EUROPIV 2 workshop held in Zaragoza, March 31 April 1, 2003. Springer, HeidelbergGoogle Scholar
  10. Marr D, Hildreth E (1980) Theory of edge detection. In: Proceedings of the Royal Society of London, Series B, Biological Sciences 2007, pp 187–217Google Scholar
  11. Nakayama A, Sakio K (2002) Simulation of flows over wavy rough boundaries. Center for Turbulent Research, Annual Research Briefs: pp 313–324Google Scholar
  12. Nguyen TD (2007) Development of Stereo PIV: application to turbulent flow over a backward-facing step. Masters thesis, Ritsumeikan University, ShigaGoogle Scholar
  13. Nguyen CV, Wells JC (2006a) Development of PIV/interface gradiometry to handle low tracer density and curved walls. In: Proceedings of FEDSM2006 European fluids engineering summer meeting, MiamiGoogle Scholar
  14. Nguyen CV, Wells JC (2006b) Direct measurement of fluid velocity gradients at a wall by PIV image processing with stereo reconstruction. J Vis 45:5–27; adapted from Nguyen CV, Phan NMT, Wells JC (2004) Proceedings of international conference on advanced optical diagnostics in fluids, solids and combustion, TokyoGoogle Scholar
  15. Nguyen CV, Nguyen TD, Wells JC (2006) Sensitivity of PIV/interface gradiometry to estimated wall position. J Vis Soc Jpn 26(2):203–206Google Scholar
  16. Nobach H, Damaschke N, Tropea C (2005) High-precision sub-pixel interpolation in particle image velocimetry image processing. Exp Fluids 39:299–304CrossRefGoogle Scholar
  17. Theunissen R, Scarano F, Riethmuller ML (2008) On improvement of PIV image interrogation near stationary interfaces, Exp Fluids (online)Google Scholar
  18. Wereley ST, Meinhart CD (2001) Second-order accurate particle image velocimetry. Exp Fluids 31(3):258–268CrossRefGoogle Scholar
  19. Westerweel J, Geelhoed P, Lindken R (2004) Single-pixel resolution ensemble correlation for micro-PIV applications. Exp Fluids 37:375–384CrossRefGoogle Scholar
  20. Wilkin J, Hedström KS (1998) User’s manual for an orthogonal curvilinear grid-generation package. Institute of Marine and Coastal Sciences, Rutgers University http://www.marine.rutgers.edu/po/tools/gridpak/grid_manual.ps.gz
  21. Yokojima S (2002) Modeling and simulation of turbulent open-channel flows emphasizing free-surface effects. PhD thesis, Kobe University, KobeGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Chuong V. Nguyen
    • 1
  • Thien D. Nguyen
    • 2
  • John C. Wells
    • 2
  • Akihiko Nakayama
    • 3
  1. 1.Fluid Laboratory for Aeronautical and Industrial Research, Department of Mechanical and Aerospace EngineeringMonash UniversityClayton, MelbourneAustralia
  2. 2.Department of Civil & Environmental EngineeringRitsumeikan UniversityShigaJapan
  3. 3.Department of Civil EngineeringKobe UniversityHyogoJapan

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