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Abstract

In the past few decades various particle image–based volumetric flow measurement techniques have been developed that have demonstrated their potential in accessing unsteady flow properties quantitatively in various experimental applications in fluid mechanics. In this review, we focus on physical properties and circumstances of 3D particle–based measurements and what knowledge can be used for advancing reconstruction accuracy and spatial and temporal resolution, as well as completeness. The natural candidate for our focus is 3D Lagrangian particle tracking (LPT), which allows for position, velocity, and acceleration to be determined alongside a large number of individual particle tracks in the investigated volume. The advent of the dense 3D LPT technique Shake-The-Box in the past decade has opened further possibilities for characterizing unsteady flows by delivering input data for powerful data assimilation techniques that use Navier–Stokes constraints. As a result, high-resolution Lagrangian and Eulerian data can be obtained, including long particle trajectories embedded in time-resolved 3D velocity and pressure fields.

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2023-01-19
2024-10-11
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Literature Cited

  1. Abu Rowin W, Ghaemi S. 2019. Streamwise and spanwise slip over a superhydrophobic surface. J. Fluid Mech. 870:1127–57
    [Google Scholar]
  2. Adrian RJ. 1997. Dynamic ranges of velocity and spatial resolution of particle image velocimetry. Meas. Sci. Technol. 8:121393
    [Google Scholar]
  3. Adrian RJ, Westerweel J. 2011. Particle Image Velocimetry Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  4. Agüera N, Cafiero G, Astarita T, Discetti S. 2016. Ensemble 3D-PTV for high resolution turbulent statistics. Meas. Sci. Technol. 27:124011
    [Google Scholar]
  5. Agüí JC, Jimenez J. 1987. On the performance of particle tracking. J. Fluid Mech. 185:447–68
    [Google Scholar]
  6. Aguirre-Pablo AA, Aljedaani AB, Xiong J, Idoughi R, Heidrich W, Thoroddsen ST. 2019. Single-camera 3D PTV using particle intensities and structured light. Exp. Fluids 60:225
    [Google Scholar]
  7. Atkinson C, Soria J. 2009. An efficient simultaneous reconstruction technique for tomographic particle image velocimetry. Exp. Fluids 47:563–78
    [Google Scholar]
  8. Bhattacharya S, Vlachos PP. 2020. Volumetric particle tracking velocimetry (PTV) uncertainty quantification. Exp. Fluids 61:9197
    [Google Scholar]
  9. Beresh SJ. 2021. Time-resolved particle image velocimetry. Meas. Sci. Technol. 32:102003
    [Google Scholar]
  10. Bosbach J, Kühn M, Wagner C. 2009. Large scale particle image velocimetry with helium filled soap bubbles. Exp. Fluids 46:539–47
    [Google Scholar]
  11. Bosbach J, Schanz D, Godbersen P, Schröder A. 2021. Spatially and temporally resolved measurements of turbulent Rayleigh-Bénard convection by Lagrangian particle tracking of long-lived helium-filled soap bubbles Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  12. Brandt L, Coletti F. 2022. Particle-laden turbulence: progress and perspectives. Annu. Rev. Fluid Mech. 54:159–89
    [Google Scholar]
  13. Bross M, Fuchs T, Kähler CJ. 2019. Interaction of coherent flow structures in adverse pressure gradient turbulent boundary layers. J. Fluid Mech. 873:287–321
    [Google Scholar]
  14. Brücker C 1997. 3D scanning PIV applied to an air flow in a motored engine using digital high-speed video. Meas. Sci. Technol. 8:1480
    [Google Scholar]
  15. Brunton SL, Noack BR, Koumoutsakos P. 2020. Machine learning for fluid mechanics. Annu. Rev. Fluid Mech. 52:477–508
    [Google Scholar]
  16. Chiu WC, Rib LN. 1956. The rate of dissipation of energy and the energy spectrum in a low-speed turbulent jet. Eos 37:113–26
    [Google Scholar]
  17. Choi YS, Seo KW, Sohn MH, Lee SJ. 2012. Advances in digital holographic micro-PTV for analyzing microscale flows. Opt. Lasers Eng. 50:139–45
    [Google Scholar]
  18. Chong MS, Perry AE, Cantwell BJ. 1990. A general classification of three-dimensional flow fields. Phys. Fluids A2:5765–77
    [Google Scholar]
  19. Cierpka C, Lütke B, Kähler CJ. 2013. Higher order multi-frame particle tracking velocimetry. Exp. Fluids 54:51533
    [Google Scholar]
  20. Cierpka C, Rossi M, Segura R, Kähler CJ. 2010. On the calibration of astigmatism particle tracking velocimetry for microflows. Meas. Sci. Technol. 22:1015401
    [Google Scholar]
  21. Cornic P, Leclaire B, Champagnat F, Le Besnerais G, Cheminet A et al. 2020. Double-frame tomographic PTV at high seeding densities. Exp. Fluids 61:223
    [Google Scholar]
  22. Dabiri D, Pecora C. 2020. Particle Tracking Velocimetry Bristol, UK: IOP
    [Google Scholar]
  23. Discetti S, Coletti F. 2018. Volumetric velocimetry for fluid flows. Meas. Sci. Technol. 29:042001
    [Google Scholar]
  24. Doh DH, Cho GR, Kim YH. 2012. Development of a tomographic PTV. J. Mech. Sci. Technol. 26:123811–19
    [Google Scholar]
  25. Duraisamy K, Iaccarino G, Xiao H 2019. Turbulence modeling in the age of data. Annu. Rev. Fluid Mech. 51:357–77
    [Google Scholar]
  26. Ebrahimian M, Sanders RS, Ghaemi S. 2019. Dynamics and wall collision of inertial particles in a solid–liquid turbulent channel flow. J. Fluid Mech. 881:872–905
    [Google Scholar]
  27. Elsinga GE, Scarano F, Wieneke B, van Oudheusden BW. 2006. Tomographic particle image velocimetry. Exp. Fluids 41:933–47
    [Google Scholar]
  28. Fahringer TW, Lynch KP, Thurow BS. 2015. Volumetric particle image velocimetry with a single plenoptic camera. Meas. Sci. Technol. 26:115201
    [Google Scholar]
  29. Fletcher R, Powell M. 1963. A rapidly convergent descent method for minimization. Comput. J. 6:2163–68
    [Google Scholar]
  30. Fuchs T, Hain R, Kähler CJ. 2016. Double-frame 3D-PTV using a tomographic predictor. Exp. Fluids 57:174
    [Google Scholar]
  31. Gao Q, Pan S, Wang H, Wei R, Wang J 2021. Particle reconstruction of volumetric particle image velocimetry with the strategy of machine learning. Adv. Aerodyn. 3:28
    [Google Scholar]
  32. Geisler R, Novara M, Schröder A. 2016. Volumetric multi-pulse particle tracking measurement for separated laminar transitional flow investigations Paper presented at 18th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Port: July 4–7
    [Google Scholar]
  33. Gesemann S. 2021. TrackFit: uncertainty quantification, optimal filtering and interpolation of tracks for time-resolved Lagrangian particle tracking Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  34. Gesemann S, Huhn F, Schanz D, Schröder A. 2016. From noisy particle tracks to velocity, acceleration and pressure fields using B-splines and penalties Paper presented at 18th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Port.: July 4–7
    [Google Scholar]
  35. Godbersen P, Bosbach J, Schanz D, Schröder A. 2020. The beauty of turbulent convection: a particle tracking endeavor Film presented at Gallery of Fluid Motion, 73rd Annual Meeting of the APS Division of Fluid Dynamics (Virtual) Nov. 22–24
    [Google Scholar]
  36. Godbersen P, Bosbach J, Schanz D, Schröder A. 2021. Beauty of turbulent convection: a particle tracking endeavor. Phys. Rev. Fluids 6:11110509
    [Google Scholar]
  37. Godbersen P, Schröder A. 2020. Functional binning: improving convergence of Eulerian statistics from Lagrangian particle tracking. Meas. Sci. Technol. 31:095304
    [Google Scholar]
  38. Godbersen P, Schröder A. 2021. Enhanced functional binning for one- and two-point statistics using a posteriori Uncertainty Quantification of LPT data Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  39. Gray C, Greated CA, McCluskey DR, Easson WJ. 1991. An analysis of the scanning beam PIV illumination system. Meas. Sci. Techn. 2:717
    [Google Scholar]
  40. Guezennec YG, Brodkey RS, Trigui N, Kent JC. 1994. Algorithms for fully automated three-dimensional particle tracking velocimetry. Exp. Fluids 17:209–19
    [Google Scholar]
  41. Haller G. 2015. Lagrangian coherent structures. Annu. Rev. Fluid Mech. 47:137–62
    [Google Scholar]
  42. Hammond A, Meng H. 2021. Particle radial distribution function and relative velocity measurement in turbulence at small particle-pair separations. J. Fluid Mech. 921:A16
    [Google Scholar]
  43. Herman GT, Lent A. 1976. Iterative reconstruction algorithms. Comput. Biol. Med. 6:273–94
    [Google Scholar]
  44. Herzog S, Schiepel D, Guido I, Barta R, Wagner C 2021. A Probabilistic particle tracking framework for guided and Brownian motion systems with high particle densities. SN Comput. Sci 2:485
    [Google Scholar]
  45. Hinsch KD. 2002. Holographic particle image velocimetry. Meas. Sci. Technol. 13:R61
    [Google Scholar]
  46. Hoyer K, Holzner M, Lüthi B, Guala M, Liberzon A, Kinzelbach W. 2005. 3D scanning particle tracking velocimetry. Exp. Fluids 39:5923–34
    [Google Scholar]
  47. 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 58:9116
    [Google Scholar]
  48. 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 59:81
    [Google Scholar]
  49. Jahn T, Schanz D, Schröder A. 2021. Advanced iterative particle reconstruction for Lagrangian Particle Tracking. Exp. Fluids 62:8179
    [Google Scholar]
  50. Janke T, Michaelis D. 2021. Uncertainty quantification for PTV/LPT data and adaptive track filtering Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  51. Jeon YJ. 2021. Eulerian time-marching in Vortex-In-Cell (VIC) method: reconstruction of multiple time-steps from a single vorticity volume and time-resolved boundary condition Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  52. Jeon YJ, Schneiders JFG, Müller M, Michaelis D, Wieneke B. 2018. 4D flow field reconstruction from particle tracks by VIC+ with additional constraints and multigrid approximation Paper presented at 18th International Symposium on Flow Visualization Zurich, Switz: June 26–29
    [Google Scholar]
  53. Jux C. 2022. Development of robotic volumetric PIV PhD Thesis T.U. Delft Delft, Neth:.
    [Google Scholar]
  54. Jux C, Sciacchitano A, Schneiders JF, Scarano F. 2018. Robotic volumetric PIV of a full-scale cyclist. Exp. Fluids 59:474
    [Google Scholar]
  55. Kähler CJ, Astarita T, Vlachos PP, Sakakibara J, Hain R et al. 2016. Main results of the 4th International PIV Challenge. Exp. Fluids 57:697
    [Google Scholar]
  56. Kähler CJ, Scharnowski S, Cierpka C. 2012. On the uncertainty of digital PIV and PTV near walls. Exp. Fluids 52:1641–56
    [Google Scholar]
  57. Katz J, Sheng J. 2010. Applications of holography in fluid mechanics and particle dynamics. Annu. Rev. Fluid Mech. 42:531–55
    [Google Scholar]
  58. Khojasteh AR, Yang Y, Heitz D, Laizet S. 2021. Lagrangian coherent track initialization. Phys. Fluids 33:9095113
    [Google Scholar]
  59. Kim D, Schanz D, Novara M, Seo H, Kim Y et al. 2022. Experimental study of turbulent bubbly jet. Part 1. Simultaneous measurement of three-dimensional velocity fields of bubbles and water. J. Fluid Mech. 941:A42
    [Google Scholar]
  60. Kim M, Schanz D, Novara M, Schröder A, Kim KC. 2021. Volumetric Lagrangian particle tracking measurements of jet impingement on convex cylinder Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  61. Kozul M, Koothur V, Worth NA, Dawson JR. 2019. A scanning particle tracking velocimetry technique for high-Reynolds number turbulent flows. Exp. Fluids 60:8137
    [Google Scholar]
  62. Kühn M, Ehrenfried K, Bosbach J, Wagner C. 2011. Large-scale tomographic particle image velocimetry using helium-filled soap bubbles. Exp. Fluids 50:929–48
    [Google Scholar]
  63. La Porta A, Voth GA, Crawford AM, Alexander J, Bodenschatz E 2001. Fluid particle accelerations in fully developed turbulence. Nature 409:68231017–19
    [Google Scholar]
  64. Lasinger K, Vogel C, Pock T, Schindler K. 2020. 3D fluid flow estimation with integrated particle reconstruction. Int. J. Comput. Vis. 128:41012–27
    [Google Scholar]
  65. Lawson JM, Bodenschatz E, Lalescu CC, Wilczek M. 2018. Bias in particle tracking acceleration measurement. Exp. Fluids 59:11172
    [Google Scholar]
  66. Lawson JM, Dawson JR. 2014. A scanning PIV method for fine-scale turbulence measurements. Exp. Fluids 55:121857
    [Google Scholar]
  67. Leclaire B, Mary I, Liauzun C, Peron S, Sciacchitano A et al. 2021. First challenge on Lagrangian Particle Tracking and Data Assimilation: datasets description and evolution to an open online benchmark Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  68. Lüthi B, Tsinober A, Kinzelbach W. 2005. Lagrangian measurement of vorticity dynamics in turbulent flow. J. Fluid Mech. 528:87–118
    [Google Scholar]
  69. Lynch KP, Scarano F. 2014. Material acceleration estimation by four-pulse tomo-PIV. Meas. Sci. Technol. 25:8084005
    [Google Scholar]
  70. Lynch KP, Scarano F. 2015. An efficient and accurate approach to MTE-MART for time-resolved tomographic PIV. Exp. Fluids 56:66
    [Google Scholar]
  71. Lynch KP, Wagner JL. 2022. Pulse-burst tomographic PIV of an impulsively started cylinder wake in a shock tube. Exp. Fluids 63:51
    [Google Scholar]
  72. Maas HG, Gruen A, Papantoniou D. 1993. Particle tracking velocimetry in three-dimensional flows. Part I: photogrammetric determination of particle coordinates. Exp. Fluids 15:133–46
    [Google Scholar]
  73. Machicoane N, Huck PD, Clark A, Aliseda A, Volk R, Bourgoin M 2019. Recent developments in particle tracking diagnostics for turbulence research. Flowing Matter F Toschi, M Sega 177–209 Cham, Switz: Springer
    [Google Scholar]
  74. Malik N, Dracos T, Papantoniou D. 1993. Particle tracking velocimetry in three dimensional flows. Part II: particle tracking. Exp. Fluids 15:279–94
    [Google Scholar]
  75. Mani M, Dorgan AJ. 2023. A perspective on the state of aerospace computational fluid dynamics technology. Annu. Rev. Fluid Mech. 55:43157
    [Google Scholar]
  76. Manovski P, Novara M, Mohan NKD, Geisler R, Schanz D et al. 2021. 3D Lagrangian particle tracking of a subsonic jet using multi-pulse Shake-The-Box. Exp. Therm. Fluid Sci. 123:110346
    [Google Scholar]
  77. Martínez Gallar B, van Oudheusden BW, Sciacchitano A, Karásek M 2020. Large-scale volumetric flow visualization of the unsteady wake of a flapping-wing micro air vehicle. Exp. Fluids 61:16
    [Google Scholar]
  78. Mendez MA, Raiola M, Masullo A, Discetti S, Ianiro A et al. 2017. POD-based background removal for particle image velocimetry. Exp. Therm. Fluid Sci. 80:181–92
    [Google Scholar]
  79. Mertens C, de Rojas Cordero T, Sodja J, Sciacchitano A, van Oudheusden BW. 2021. Aeroelastic characterization of a flexible wing using particle tracking velocimetry measurements. AIAA J 60:1276–86
    [Google Scholar]
  80. Michaelis D, Novara M, Scarano F, Wieneke B. 2010. Comparison of volume reconstruction techniques at different particle densities Paper presented at 15th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Port.: July 5–8
    [Google Scholar]
  81. Michaelis D, Wiswall J, Mychkovsky A, Prevost R, Neal D, Wieneke B. 2021. Calibration correction of arbitrary optical distortions by non-parametric 3D disparity field for planar and volumetric PIV/LPT Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  82. Nayler JL, Frazer BA. 1917. Preliminary report upon an experimental method of investigating, by the aid of kinematographic photography, the history of eddying flow past a model immersed in water Tech. Rep. Adv. Comm. Aeronaut. London:
    [Google Scholar]
  83. Nishino K, Kasagi N, Hirata M. 1989. Three-dimensional particle tracking velocimetry based on automated digital image processing. Trans ASME J. Fluid Eng. 111:384–90
    [Google Scholar]
  84. Nocedal J. 1980. Updating quasi-Newton matrices with limited storage. Math. Comput. 35:773–82
    [Google Scholar]
  85. Novara M, Batenburg KJ, Scarano F. 2010. Motion tracking enhanced MART for tomographic PIV. Meas. Sci. Technol. 21:035401
    [Google Scholar]
  86. Novara M, Scarano F. 2013. A particle-tracking approach for accurate material derivative measurements with tomographic PIV. Exp. Fluids 54:1584
    [Google Scholar]
  87. Novara M, Schanz D, Geisler R, Gesemann S, Voss C, Schröder A. 2019. Multi-exposed recordings for 3D Lagrangian particle tracking with multi-pulse Shake-The-Box. Exp. Fluids 60:344
    [Google Scholar]
  88. Novara M, Schanz D, Gesemann S, Lynch KP, Schröder A 2016a. Lagrangian 3D particle tracking for multi-pulse systems: performance assessment and application of Shake-The-Box Paper presented at 18th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics, Lisbon, Portugal, July 4–7
    [Google Scholar]
  89. Novara M, Schanz D, Reuther N, Kähler CJ, Schröder A 2016b. Lagrangian 3D particle tracking in high-speed flows: Shake-The-Box for multi-pulse systems. Exp. Fluids 57:128
    [Google Scholar]
  90. Novara M, Schanz D, Schröder A. 2022. Two-pulse 3D particle tracking with Shake-The-Box. Paper presented at 20th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Portugal: July 11–14
    [Google Scholar]
  91. Ouellette NT, Xu H, Bodenschatz E. 2006. A quantitative study of three-dimensional Lagrangian particle tracking algorithms. Exp. Fluids 40:301–13
    [Google Scholar]
  92. 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:S078–84
    [Google Scholar]
  93. Raffel M, Willert CE, Scarano F, Kähler CJ, Wereley ST, Kompenhans J. 2018. Particle Image Velocimetry: A Practical Guide Cham, Switz: Springer. , 3rd ed..
    [Google Scholar]
  94. Raiola M, Lopez-Nuñez E, Cafiero G, Discetti S. 2020. Adaptive ensemble PTV. Meas. Sci. Technol. 31:8085301
    [Google Scholar]
  95. Richardson LF. 1922. Weather Prediction by Numerical Process Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  96. Rival DE, van Oudheusden B 2017. Load-estimation techniques for unsteady incompressible flows. Exp. Fluids 58:320
    [Google Scholar]
  97. Savitzky A, Golay MJE. 1964. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36:81627–39
    [Google Scholar]
  98. Scarano F. 2013. Tomographic PIV: principles and practice. . Meas. Sci. Technol. 24:012001
    [Google Scholar]
  99. Scarano F, Ghaemi S, Caridi GCA, Bosbach J, Dierksheide U, Sciacchitano A. 2015. On the use of helium-filled soap bubbles for large-scale tomographic PIV in wind tunnel experiments. Exp. Fluids 56:42
    [Google Scholar]
  100. Scarano F, Schneiders JF, Saiz GG, Sciacchitano A. 2022. Dense velocity reconstruction with VIC-based time-segment assimilation. Exp. Fluids 63:696
    [Google Scholar]
  101. Schanz D, Gesemann S, Schröder A. 2016. Shake-The-Box: Lagrangian particle tracking at high particle image densities. Exp. Fluids 57:570
    [Google Scholar]
  102. Schanz D, Gesemann S, Schröder A, Wieneke B, Novara M. 2013a. Non-uniform optical transfer functions in particle imaging: calibration and application to tomographic reconstruction. Meas. Sci. Technol. 24:024009
    [Google Scholar]
  103. Schanz D, Jahn T, Schröder A. 2022. 3D particle position determination and correction at high particle densities Paper presented at 20th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Portugal: July 11–14
    [Google Scholar]
  104. Schanz D, Novara M, Schröder A. 2021. Shake-The-Box particle tracking with variable time-steps in flows with high velocity range (VT-STB) Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  105. Schanz D, Schröder A, Gesemann S, Michaelis D, Wieneke B. 2013b. Shake-The-Box: a highly efficient and accurate Tomographic Particle Tracking Velocimetry (TOMO-PTV) method using prediction of particle position Paper presented at 10th International Symposium on Particle Image Velocimetry Delft, Neth.: July 1–3
    [Google Scholar]
  106. Schanz D, Schröder A, Novara M, Geisler R, Agocs J et al. 2019. Large-scale volumetric characterization of a turbulent boundary layer flow Paper presented at 13th International Symposium on Particle Image Velocimetry Munich, Ger: July 22–24
    [Google Scholar]
  107. Schneiders JFG, Scarano F. 2016. Dense velocity reconstruction from tomographic PTV with material derivatives. Exp. Fluids 57:139
    [Google Scholar]
  108. Schneiders JFG, Scarano F, Jux C, Sciacchitano A. 2018. Coaxial volumetric velocimetry. Meas. Sci. Technol. 29:6065201
    [Google Scholar]
  109. Schneiders JFG, Sciacchitano A. 2017. Track benchmarking method for uncertainty quantification of particle tracking velocimetry interpolations. Meas. Sci. Technol. 28:065302
    [Google Scholar]
  110. Schröder A, Geisler R, Staak K, Wieneke B, Elsinga G et al. 2011. Eulerian and Lagrangian views into a turbulent boundary layer flow using time-resolved tomographic PIV. Exp. Fluids 50:1071–91
    [Google Scholar]
  111. Schröder A, Schanz D, Bosbach J, Novara M, Geisler R et al. 2022. Large-scale volumetric flow studies on transport of aerosol particles using a breathing human model with and without face protections. Phys. Fluids 34:035133
    [Google Scholar]
  112. Schröder A, Schanz D, Geisler R, Willert C, Michaelis D. 2013. Dual-volume and four-pulse tomo PIV using polarized laser lights. Paper presented at 10th International Symposium on Particle Image Velocimetry Delft, Neth: July 1–3
    [Google Scholar]
  113. Schröder A, Schanz D, Gesemann S, Huhn F, Buchwald T et al. 2022. Measurements of the energy dissipation rate in homogeneous turbulence using dense 3D Lagrangian Particle Tracking and FlowFit. Paper presented at 20th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Portugal: July 11–14
    [Google Scholar]
  114. 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. 95:2193–209
    [Google Scholar]
  115. Schröder A, Willert C, eds. 2008. Particle Image Velocimetry: New Developments and Recent Applications Top. Appl. Phys. 112 Berlin: Springer-Verlag
    [Google Scholar]
  116. Schröder A, Willert C, Schanz D, Geisler R, Jahn T et al. 2020. The flow around a surface mounted cube: a characterization by time-resolved PIV, 3D Shake-The-Box and LBM simulation. Exp. Fluids 61:189
    [Google Scholar]
  117. Sciacchitano A, Leclaire B, Schröder A. 2021a. Main results of the first data assimilation challenge Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  118. Sciacchitano A, Leclaire B, Schröder A. 2021b. Main results of the first Lagrangian particle tracking challenge Paper presented at 14th International Symposium on Particle Image Velocimetry Chicago: Aug. 1–4
    [Google Scholar]
  119. Shnapp R, Shapira E, Peri D, Bohbot-Raviv Y, Fattal E, Liberzon A 2019. Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel. Sci. Rep 97405
    [Google Scholar]
  120. Sellappan P, Alvi FS, Cattafesta LN. 2020. Lagrangian and Eulerian measurements in high-speed jets using Multi-Pulse Shake-The-Box and fine scale reconstruction (VIC#). Exp. Fluids 61:157
    [Google Scholar]
  121. Stasicki B, Schröder A, Boden F, Ludwikowski K 2017. High-power LED light sources for optical measurement systems operated in continuous and overdriven pulsed modes. Optical Measurement Systems for Industrial Inspection X, Vol. 10329 P Lehmann, W Osten, AA Gonçalves Jr., Pap. 103292J Bellingham, WA: SPIE
    [Google Scholar]
  122. Tan S, Salibindla A, Masuk AUM, Ni R. 2020. Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking. Exp. Fluids 61:247
    [Google Scholar]
  123. Toschi F, Bodenschatz E. 2009. Lagrangian properties of particles in turbulence. Annu. Rev. Fluid Mech. 41:375–404
    [Google Scholar]
  124. van Gent PL, Michaelis D, van Oudheusden BW, Weiss , De Kat R et al. 2017. Comparative assessment of pressure field reconstructions from PIV measurements and Lagrangian particle tracking. Exp. Fluids 58:33
    [Google Scholar]
  125. Viggiano B, Basset T, Solovitz S, Barois T, Gibert M et al. 2021. Lagrangian diffusion properties of a free shear turbulent jet. J. Fluid Mech. 918:A25
    [Google Scholar]
  126. Virant M, Dracos T. 1997. 3D PTV and its application on Lagrangian motion. Meas. Sci. Technol. 8:1539
    [Google Scholar]
  127. Wang L, Pan C, Liu J, Cai C. 2020. Ratio-cut background removal method and its application in near-wall PTV measurement of a turbulent boundary layer. Meas. Sci. Technol. 32:2025302
    [Google Scholar]
  128. Weiss S, Schanz D, Erdogdu AO, Schröder A, Bosbach J. 2022. Investigation of turbulent superstructures in Rayleigh-Benard convection by Lagrangian particle tracking of fluorescent microspheres Paper presented at 20th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics Lisbon, Portugal: July 11–14
    [Google Scholar]
  129. Westerweel J, Elsinga GE, Adrian RJ 2013. Particle image velocimetry for complex and turbulent flows. Annu. Rev. Fluid Mech. 45:409–36
    [Google Scholar]
  130. Wieneke B. 2008. Volume self-calibration for 3D particle image velocimetry. Exp. Fluids 45:549–56
    [Google Scholar]
  131. Wieneke B. 2012. Iterative reconstruction of volumetric particle distribution. Meas. Sci. Technol. 24:024008
    [Google Scholar]
  132. Wieneke B. 2018. Improvements for volume self-calibration. Meas. Sci. Technol. 29:8084002
    [Google Scholar]
  133. Wiener N. 1949. Extrapolation, Interpolation, and Smoothing of Stationary Time Series, Vol. 2 Cambridge, MA: MIT Press
    [Google Scholar]
  134. Willert CE, Gharib M. 1992. Three-dimensional particle imaging with a single camera. Exp. Fluids 12:6353–58
    [Google Scholar]
  135. Xu H. 2008. Tracking Lagrangian trajectories in position–velocity space. Meas. Sci. Technol. 19:075105
    [Google Scholar]
  136. Yang Y, Heitz D. 2021. Kernelized Lagrangian particle tracking. Exp. Fluids 62:12252
    [Google Scholar]
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