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A scanning particle tracking velocimetry technique for high-Reynolds number turbulent flows

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Abstract

We propose a novel robust 3D particle tracking technique based on a scanning laser setup. The method yields Lagrangian statistics in densely seeded turbulent flows with good spatial and temporal resolution, overcoming some of the inherent difficulty with line-of-sight-based volumetric methods. To do this, we have developed an effective triangulation method greatly reducing ghost particle reconstruction using images from two cameras. A laser sheet is rapidly traversed (‘scanned’) across a measurement volume illuminating only a thin slice of the flow at a time. Particle images are taken at closely spaced, overlapping nominal laser sheet locations giving multiple intensity recordings for each individual particle. The laser-sheet intensity varies as a Gaussian across its thickness, which is here exploited to deduce the particle’s probable location along the scan direction to sub-sheet number resolution by fitting a similar Gaussian profile to the particle’s multiple intensity recordings. The method is presently verified via numerical experiment using a DNS database. Following successful reconstruction of a time series of 3D particle fields, particle tracks are formed from which all components of Lagrangian velocity and acceleration are calculated.

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References

  • Adrian RJ (1991) Particle-imaging techniques for experimental fluid mechanics. Ann Rev Fluid Mech 23:261–304

    Article  Google Scholar 

  • Brücker C (1995) Digital-particle-image-velocimetry (DPIV) in a scanning light-sheet: 3D starting flow around a short cylinder. Exp Fluids 19:255–263

    Article  Google Scholar 

  • Cierpka C, Lütke B, Kähler CJ (2013) Higher order multi-frame particle tracking velocimetry. Exp Fluids 54:1533

    Article  Google Scholar 

  • Elsinga GE, Scarano F, Wieneke B, Van Oudheusden BW (2006) Tomographic particle image velocimetry. Exp Fluids 41:933–947

    Article  Google Scholar 

  • Garcia V, Debreuve E, Nielsen F, Barlaud M (2010) K-nearest neighbor search: fast GPU-based implementations and application to high-dimensional feature matching. In: 17th IEEE international conference on image processing (ICIP), Hong Kong

  • Gesemann S, Huhn F, Schanz D, Schröder A (2016) From noisy particle tracks to velocity, acceleration and pressure fields using B-splines and penalties. In: 18th international symposium on the application of laser and imaging techniques to fluid mechanics, Lisbon, Portugal

  • Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  • Hoyer K, Holzner M, Lüthi B, Guala M, Liberzon A, Kinzelbach W (2005) 3D scanning particle tracking velocimetry. Exp Fluids 39:923

    Article  Google Scholar 

  • Knutsen AN, Lawson JM, Dawson JR, Worth NA (2017) A laser sheet self-calibration method for scanning PIV. Exp Fluids 58:145

    Article  Google Scholar 

  • Lawson JM, Dawson JR (2014) A scanning PIV method for fine-scale turbulence measurements. Exp Fluids 55:1857

    Article  Google Scholar 

  • Lawson JM, Dawson JR (2015) On velocity gradient dynamics and turbulent structure. J Fluid Mech 780:60–98

    Article  MathSciNet  Google Scholar 

  • Lawson JM, Bodenschatz E, Knutsen AN, Dawson JR, Worth NA (2019) Direct assessment of Kolmogorov’s first refined similarity hypothesis. Phys Rev Fluids 4:022,601

    Article  Google Scholar 

  • Lecordier B, Westerweel J (2004) The EUROPIV synthetic image generator (SIG). In: Particle image velocimetry: recent improvements, Springer, New York, pp 145–161

    Chapter  Google Scholar 

  • Li Y, Perlman E, Wan M, Yang Y, Meneveau C, Burns R, Chen S, Szalay A, Eyink G (2008) A public turbulence database cluster and applications to study Lagrangian evolution of velocity increments in turbulence. J Turbul 9:1–29

    Article  Google Scholar 

  • Lüthi B, Tsinober A, Kinzelbach W (2005) Lagrangian measurement of vorticity dynamics in turbulent flow. J Fluid Mech 528:87–118

    Article  Google Scholar 

  • Lynch KP, Scarano F (2015) An efficient and accurate approach to MTE-MART for time-resolved tomographic PIV. Exp Fluids 56:66

    Article  Google Scholar 

  • Maas HG, Gruen A, Papantoniou D (1993) Particle tracking velocimetry in three-dimensional flows. Exp Fluids 15:133–146

    Article  Google Scholar 

  • Malik NA, Dracos T, Papantoniou DA (1993) Particle tracking velocimetry in three-dimensional flows. Exp Fluids 15:279–294

    Article  Google Scholar 

  • Nishino K, Kasagi N, Hirata M (1989) Three-dimensional particle tracking velocimetry based on automated digital image processing. Trans ASME J Fluid Eng 111:384–391

    Article  Google Scholar 

  • Novara M, Scarano F (2013) A particle-tracking approach for accurate material derivative measurements with tomographic PIV. Exp Fluids 54:1584

    Article  Google Scholar 

  • Ouellette NT, Xu H, Bodenschatz E (2006) A quantitative study of three-dimensional lagrangian particle tracking algorithms. Exp Fluids 40:301–313

    Article  Google Scholar 

  • Schanz D, Gesemann S, Schröder A, Wieneke B, Novara M (2013) Non-uniform optical transfer functions in particle imaging: calibration and application to tomographic reconstruction. Meas Sci Technol 24:024009

    Article  Google Scholar 

  • Schanz D, Gesemann S, Schröder A (2016) Shake-The-Box: Lagrangian particle tracking at high particle image densities. Exp Fluids 57:70

    Article  Google Scholar 

  • Scharnowski S, Kähler CJ (2016) Estimation and optimization of loss-of-pair uncertainties based on PIV correlation functions. Exp Fluids 57:23

    Article  Google Scholar 

  • Schneiders JFG, Scarano F (2016) Dense velocity reconstruction from tomographic PTV with material derivatives. Exp Fluids 57:139

    Article  Google Scholar 

  • Virant M, Dracos T (1997) 3D PTV and its application on lagrangian motion. Meas Sci Technol 8:1539

    Article  Google Scholar 

  • Voth GA, La Porta A, Crawford AM, Alexander J, Bodenschatz E (2002) Measurement of particle accelerations in fully developed turbulence. J Fluid Mech 469:121–160

    Article  Google Scholar 

  • Wieneke B (2013) Iterative reconstruction of volumetric particle distribution. Meas Sci Technol 24:024,008

    Article  Google Scholar 

  • Yu H, Kanov K, Perlman E, Graham J, Frederix E, Burns R, Szalay A, Eyink G, Meneveau C (2012) Studying lagrangian dynamics of turbulence using on-demand fluid particle tracking in a public turbulence database. J Turbul 13:1–29

    Article  MathSciNet  Google Scholar 

  • Zhang W, Hain R, Kähler CJ (2008) Scanning PIV investigation of the laminar separation bubble on a SD7003 airfoil. Exp Fluids 45:725–743

    Article  Google Scholar 

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Correspondence to Melissa Kozul.

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Kozul, M., Koothur, V., Worth, N.A. et al. A scanning particle tracking velocimetry technique for high-Reynolds number turbulent flows. Exp Fluids 60, 137 (2019). https://doi.org/10.1007/s00348-019-2777-3

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  • DOI: https://doi.org/10.1007/s00348-019-2777-3

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