Abstract
A novel technique is described for pressure extraction from Lagrangian particle-tracking data. The technique uses a Poisson solver to extract the pressure field on a network of data nodes, which is constructed using the Voronoi tessellation and the Delaunay triangulation. The technique is demonstrated on two cases: synthetic Lagrangian data generated for the analytical case of Hill’s spherical vortex, and the flow in the wake behind a NACA 0012 which was impulsively accelerated to \(Re = 7{,}500\). The experimental data were collected using four-camera, three-dimensional particle-tracking velocimetry. For both the analytical case and the experimental case, the dependence of pressure-field error or sensitivity on the normalized spatial particle density was found to follow similar power-law relationships. It was shown that in order to resolve the salient flow structures from experimental data, the required particle density was an order of magnitude greater than for the analytical case. Furthermore, additional sub-structures continued to be identified in the experimental data as the particle density was increased. The increased density requirements of the experimental data were assumed to be due to a combination of phase-averaging error and the presence of turbulent coherent structures in the flow. Additionally, the computational requirements of the technique were assessed. It was found that in the current implementation, the computational requirements are slightly nonlinear with respect to the number of particles. However, the technique will remain feasible even as advancements in particle-tracking techniques in the future increase the density of Lagrangian data.
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References
Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv 23(3):345–404
Baur X, Kongeter J (1999) PIV with high temporal resolution for the determination of local pressure reductions from coherent turbulent phenomena. In: PIV’99, Santa Barbara, CA
Feng Y, Goree J, Liu B (2011) Errors in particle tracking velocimetry with high speed cameras. Rev Sci Instrum 82(5):053707
Gurka R, Liberzon A, Hefetz D, Rubinstein D, Shavit U (1999) Computation of pressure distribution using PIV velocity data. In: PIV’99, Santa Barbara, CA
Hill MJM (1894) On a spherical vortex. Philos Trans R Soc Lond 185:213–245
Hosokawa S, Moriyama S, Tomiyama A, Takada N (2003) PIV measurement of pressure distributions about single bubbles. J Nucl Sci Technol 40(10):754–762
Imaichi K, Ohmi K (1983) Numerical processing of flow-visualization pictures—measurement of two-dimensional vortex flow. J Fluid Mech 129:283–311
Jakobsen ML, Dewhirst TP, Greated CA (1997) Particle image velocimetry for predictions of acceleration fields and force within fluid flows. Meas Sci Technol 8:1502–1516
Kähler CJ, Scharnowski S, Cierpka C (2012) On the uncertainty of digital PIV and PTV near walls. Exp Fluids 52:1641–1656
Liu X, Katz J (2006) Instantaneous pressure and material acceleration measurements using a four-exposure PIV system. Exp Fluids 41:227–240
Lüthi B, Tsinober A, Kinzelbach W (2005) Lagrangian measurement of vorticity dynamics in turbulent flow. J Fluid Mech 528:87–118
Novara M, Scarano F (2013) A particle-tracking approach for accurate material derivative measurements with tomographic PIV. Exp Fluids 54:1584
Raffel M, Willert CE, Wereley ST, Kompenhans J (2007) Particle image velocimetry: a practical guide, 2nd edn. Springer, Berlin
Schanz D, Schröder A, Gesemann S, Michaelis D, Wieneke B (2013) Shake the box: a highly efficient and accurate tomographic particle tracking velocimetry (TOMO-PTV) method using prediction of particle positions. In: 10th international symposium on particle image velocimetry, Lisbon, Portugal
Schanz D, Schröder A, Gesemann S (2014) Shake the box: a 4D PTV algorithm—accurate and ghostless reconstruction of Lagrangian tracks in densely seeded flows. In: 17th international symposium on applications of laser techniques to fluid mechanics, Lisbon, Portugal
Schwabe M (1935) Uber druckermittlung in der nichtstationiiren ebenen stromung. Ing Arch 6:34–50
Sukumar N, Bolander JE (2003) Numerical computation of discrete differential operators on non-uniform grids. Comput Model Eng Sci 4:691–706
Tropea C, Yarin AL, Moss JF (2007) Springer handbook of experimental fluid mechanics. Springer, Berlin
van Oudheusden BW (2013) PIV-based pressure measurement. Meas Sci Technol 24(032):001
Villegas A, Diez FJ (2014) Evaluation of unsteady pressure fields and forces in rotating airfoils from time-resolved PIV. Exp Fluids 55:1697
Violato D, Moore P, Scarano F (2011) Lagrangian and Eulerian pressure field evaluation of rod–airfoil flow from time-resolved tomographic PIV. Exp Fluids 50:1057–1070
Young DM Jr (1950) Iterative methods for solving partial difference equations of elliptic type. PhD thesis, Harvard University
Acknowledgments
The authors would like to thank Alberta Innovates Technology Futures for their financial backing, and the members of the NIOPLEX research consortium (7th Framework Programme of the European Commission under Grant Agreement 605151) for their valuable feedback.
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Neeteson, N.J., Rival, D.E. Pressure-field extraction on unstructured flow data using a Voronoi tessellation-based networking algorithm: a proof-of-principle study. Exp Fluids 56, 44 (2015). https://doi.org/10.1007/s00348-015-1911-0
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DOI: https://doi.org/10.1007/s00348-015-1911-0