Abstract
We developed an open-source Lagrangian particle tracking (OpenLPT) based on the Shake-the-Box (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) method. The source code of OpenLPT is available on GitHub repository (@JHU-NI-LAB). The code features a new method that removes the majority of ghost particles at a high particle image density. The resulting percentage of ghost particles drops from 110% to 26% for image density at 0.125 ppp—nearly 84% of ghost particles are removed. Extensive tests of OpenLPT using synthetic data sets show that the code produces tracks with accuracy and processing time similar to the previously-reported values. In addition, OpenLPT has been parallelized to run on high-performance computing clusters to drastically increase its processing speed. To examine the code’s capability of tracking shadows of small tracers for backlit experiments, the blurred-particle effect was also included on synthetic images and OpenLPT was tested to process these noisy images. The results show that OpenLPT can also track shadows of a high-concentration of particles reliably in 3D. Based on the test, the optimal depth of field (DoF) and particle concentration for future experiments using Lagrangian shadow tracking are provided. For example, DoF controlled by the aperture should be set at around half of the size of the view area. At this DoF, most particles in the interrogation volume can be tracked, whereas particles outside the interrogation volume become too dim to affect results. 40 experimental data sets for a wide range of particle concentrations were also used for evaluating the code, and the results show a nice agreement with the synthetic tests.
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Abbreviations
- \(I^i_\mathrm{part}\) :
-
Intensity of the \(i\mathrm{th}\) particle
- C :
-
Track coverage (Fig. 3)
- F :
-
Track fragmentation (Fig. 3)
- \(\mathrm{Cr}\) :
-
Track correctness (Fig. 3)
- \(\epsilon _r\) :
-
Mean position error for reconstructed raw tracks
- T :
-
Processing time per frame
- \(\mathrm{DoF}\) :
-
Depth of field
- \(\mathrm{CoC}\) :
-
Circle of confusion
- \(d_\mathrm{o}\) :
-
Distance between an object and the lens
- \(d_\mathrm{i}\) :
-
Distance between the image plane and the lens
- f :
-
Focal length of the lens
- \(D_a\) :
-
Aperture size
- \(f_L\) :
-
DoF/L
- L :
-
Size of the interrogation volume
- M :
-
Magnification ratio of the lens
- \(d_p\) :
-
Physical diameter of a particle
- \(d_e\) :
-
Diameter of the projected particle image
- \(d_s\) :
-
Diameter of a particle image due to diffraction
- \(d_f\) :
-
Diameter of a particle image due to the defocussing effect
- z :
-
Distance of a particle to the focal plane in the depth direction
- \(L_n\) :
-
Size of the noise zone
- \(N_{\mathrm{t}}\) :
-
Number of all trackable particles
- \(N_{\mathrm{tf}}\) :
-
Number of in-focus trackable particles
- \(N_{\mathrm{tb}}\) :
-
Number of blurred trackable particles
- \(N_{\mathrm{n}}\) :
-
Number of out-of-focus non-trackable particles
- d :
-
D for only trackable particles
- D :
-
Average diameter of all particles in pixels on images
- \(\phi\) :
-
Total image density
- \(\psi\) :
-
Effective image density (only for trackable particles)
- \(C_\phi\) :
-
Fraction of pixels occupied by all particles
- \(C_\psi\) :
-
Fraction of pixels occupied by trackable particles
- R :
-
\(C_\psi /C_\phi\)
- \(\epsilon _s\) :
-
Mean position error for smoothed tracks
- e :
-
Calibration error
- \(\varDelta _\epsilon\) :
-
Mean distance between reconstructed tracks and filtered tracks
- \(\epsilon _\varDelta\) :
-
Triangulation error
- l :
-
Average track length
References
Allen E, Triantaphillidou S (2012) The manual of photography and digital imaging. Focal Press
Attanasi A, Cavagna A, Del Castello L, Giardina I, Jelić A, Melillo S, Parisi L, Pellacini F, Shen E, Silvestri E et al (2015) Greta-a novel global and recursive tracking algorithm in three dimensions. IEEE Trans Pattern Anal Mach Intell 37(12):2451–2463
Budwig R (1994) Refractive index matching methods for liquid flow investigations. Exp Fluids 17(5):350–355
Dovichi NJ, Martin JC, Jett JH, Trkula M, Keller RA (1984) Laser-induced fluorescence of flowing samples as an approach to single-molecule detection in liquids. Anal Chem 56(3):348–354
Estevadeordal J, Goss L (2005) PIV with LED: particle shadow velocimetry (PSV) technique. In: 43rd AIAA aerospace sciences meeting and exhibit, p 37
Gesemann S (2015) From particle tracks to velocity and acceleration fields using b-splines and penalties. arXiv preprint arXiv:151009034
Goss L, Estevadeordal J, Crafton J (2007) Velocity measurements near walls, cavities, and model surfaces using particle shadow velocimetry (PSV). In: 2007 22nd International Congress on Instrumentation in Aerospace Simulation Facilities, IEEE, pp 1–8
Hessenkemper H, Ziegenhein T (2018) Particle shadow velocimetry (PSV) in bubbly flows. Int J Multiph Flow 106:268–279
Huhn F, Schanz D, Gesemann S, Dierksheide U, van de Meerendonk R, Schröder A (2017) Large-scale volumetric flow measurement in a pure thermal plume by dense tracking of helium-filled soap bubbles. Exp Fluids 58(9):116
Huhn F, Schanz D, Manovski P, Gesemann S, Schröder A (2018) Time-resolved large-scale volumetric pressure fields of an impinging jet from dense lagrangian particle tracking. Exp Fluids 59(5):81
Jordt A, Zelenka C, von Deimling JS, Koch R, Köser K (2015) The bubble box: Towards an automated visual sensor for 3d analysis and characterization of marine gas release sites. Sensors 15(12):30716–30735
Kähler CJ, Astarita T, Vlachos PP, Sakakibara J, Hain R, Discetti S, La Foy R, Cierpka C (2016) Main results of the 4th international PIV challenge. Exp Fluids 57(6):97
Khodaparast S, Borhani N, Thome J (2014) Application of micro particle shadow velocimetry \(\mu\)PSV to two-phase flows in microchannels. Int J Multiph Flow 62:123–133
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:N31
Lindken R, Merzkirch W (2002) A novel PIV technique for measurements in multiphase flows and its application to two-phase bubbly flows. Exp Fluids 33(6):814–825
Malik N, Dracos T, Papantoniou D (1993) Particle tracking velocimetry in three-dimensional flows. Exp Fluids 15(4–5):279–294
Masuk AUM, Salibindla A, Tan S, Ni R (2019) V-ONSET (vertical octagonal noncorrosive stirred energetic turbulence): a vertical water tunnel with a large energy dissipation rate to study bubble/droplet deformation and breakup in strong turbulence. Rev Sci Instrum 90(8):085105
Meinhart C, Wereley S, Gray M (2000) Volume illumination for two-dimensional particle image velocimetry. Measur Sci Technol 11(6):809
Mordant N, Crawford AM, Bodenschatz E (2004) Experimental Lagrangian acceleration probability density function measurement. Physica D Nonlinear Phenomena 193(1–4):245–251
Ni R, Huang SD, Xia KQ (2012) Lagrangian acceleration measurements in convective thermal turbulence. J Fluid Mech 692:395–419
Nishino K, Kasagi N, Hirata M (1989) Three-dimensional particle tracking velocimetry based on automated digital image processing. J Fluids Eng 111(4):384–391
Nishino K, Kato H, Torii K (2000) Stereo imaging for simultaneous measurement of size and velocity of particles in dispersed two-phase flow. Measur Sci Technol 11(6):633
Novara M, Schanz D, Gesemann S, Lynch K, Schröder A (2016) Lagrangian 3D particle tracking for multi-pulse systems: performance assessment and application of Shake-The-Box. In: 18th international symposium on applications of laser techniques to fluid mechanics, pp 4–7
Olsen M, Adrian R (2000) Out-of-focus effects on particle image visibility and correlation in microscopic particle image velocimetry. Exp Fluids 29(1):S166–S174
Ouellette NT, Xu H, Bodenschatz E (2006) A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp Fluids 40(2):301–313
Papantoniou D, Dracos T (1989) Analyzing 3-D turbulent motions in open channel flow by use of stereoscopy and particle tracking. In: Fernholz HH, Fiedler HE (eds) Advances in Turbulence 2. Springer, Berlin, Heidelberg, pp 278–285
Rossi M, Segura R, Cierpka C, Kähler CJ (2012) On the effect of particle image intensity and image preprocessing on the depth of correlation in micro-PIV. Exp Fluids 52(4):1063–1075
Schanz D, Gesemann S, Schröder A (2016) Shake-The-Box: Lagrangian particle tracking at high particle image densities. Exp Fluids 57(5):70
Schlueter-Kuck KL, Dabiri JO (2017) Coherent structure colouring: identification of coherent structures from sparse data using graph theory. J Fluid Mech 811:468–486
Schneiders JF, Scarano F (2016) Dense velocity reconstruction from tomographic PTV with material derivatives. Exp Fluids 57(9):139
Schneiders JF, Scarano F, Elsinga GE (2017) Resolving vorticity and dissipation in a turbulent boundary layer by tomographic PTV and VIC+. Exp Fluids 58(4):27
Schröder A, Schanz D, Michaelis D, Cierpka C, Scharnowski S, Kähler CJ (2015) Advances of piv and 4d-ptv shake-the-box for turbulent flow analysis-the flow over periodic hills. Flow Turbul Combust 95(2–3):193–209
Tan S, Salibindla A, Masuk AUM, Ni R (2019) An open-source Shake-the-Box method and its performance evaluation. In: 13th international symposium on particle image velocimetry
Tsai R (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom 3(4):323–344
Van Gent P, Michaelis D, Van Oudheusden B, Weiss PÉ, de Kat R, Laskari A, Jeon YJ, David L, Schanz D, Huhn F et al (2017) Comparative assessment of pressure field reconstructions from particle image velocimetry measurements and Lagrangian particle tracking. Exp Fluids 58(4):33
Wieneke B (2008) Volume self-calibration for 3D particle image velocimetry. Exp Fluids 45(4):549–556
Wieneke B (2012) Iterative reconstruction of volumetric particle distribution. Measur Sci Technol 24(2):024008
Wieneke B (2018) Improvements for volume self-calibration. Measur Sci Technol 29(8):084002
Wiener N (1949) Extrapolation, interpolation, and smoothing of stationary time series, vol 2. MIT Press, Cambridge
Xu H (2008) Tracking Lagrangian trajectories in position-velocity space. Measur Sci Technol 19(7):075105
Zaruba A, Lucas D, Prasser HM, Höhne T (2007) Bubble-wall interactions in a vertical gas-liquid flow: Bouncing, sliding and bubble deformations. Chem Eng Sci 62(6):1591–1605
Acknowledgements
Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research. This study was also financially supported by National Science Foundation under the Award Numbers: 1705246 and CAREER-1653389. Partial support is provided by the Oak Ridge Institute for Science and Education (ORISE) professorship to Rui Ni. The authors also thank Daniel Schanz, from German Aerospace Center (DLR), for his valuable suggestions.
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Tan, S., Salibindla, A., Masuk, A.U.M. et al. Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking. Exp Fluids 61, 47 (2020). https://doi.org/10.1007/s00348-019-2875-2
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DOI: https://doi.org/10.1007/s00348-019-2875-2