Advertisement

Experiments in Fluids

, 61:26 | Cite as

Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis

  • Yeonghyeon Gim
  • Dong Kyu Jang
  • Dong Kee Sohn
  • Hyoungsoo KimEmail author
  • Han Seo KoEmail author
Research Article
  • 28 Downloads

Abstract

Three-dimensional particle tracking velocimetry (3D-PTV) technique is widely used to acquire the complicated trajectories of particles and flow fields. It is known that the accuracy of 3D-PTV depends on the mapping function to reconstruct three-dimensional particles locations. The mapping function becomes more complicated if the number of cameras is increased and there is a liquid–vapor interface, which crucially affect the total computation time. In this paper, using a shallow neural network model, we dramatically decrease the computation time with a high accuracy to successfully reconstruct the three-dimensional particle positions, which can be used for real-time particle detection for 3D-PTV. The developed technique is verified by numerical simulations and applied to measure a complex solutal Marangoni flow patterns inside a binary mixture droplet.

Graphic abstract

Notes

Acknowledgements

National Research Foundation of Korea (NRF) (NRF-2019R1A2C2003176 and NRF-2018R1C1B6004190)

References

  1. Adrian RJ, Westerweel J (2011) Particle image velocimetry. Cambridge University Press, CambridgezbMATHGoogle Scholar
  2. Cai S, Zhou S, Xu C, Gao Q (2019a) Dense motion estimation of particle images via a convolutional neural network. Exp Fluids 60(4):73CrossRefGoogle Scholar
  3. Cai S, Liang J, Gao Q, Xu C, Wei R (2019b) Particle image velocimetry based on a deep learning motion estimator. IEEE Trans Instrum Meas.  https://doi.org/10.1109/TIM.2019.2932649 CrossRefGoogle Scholar
  4. Chen C, Kim YJ, Ko HS (2011) Three-dimensional tomographic reconstruction of unstable ejection phenomena of droplets for electrohydrodynamic jet. Exp Therm Fluid Sci 35(3):433–441CrossRefGoogle Scholar
  5. Christy JR, Hamamoto Y, Sefiane K (2011) Flow transition within an evaporating binary mixture sessile drop. Phys Rev Lett 106(20):205701CrossRefGoogle Scholar
  6. de Dios M, Bombardelli FA, García CM, Liscia SO, Lopardo RA, Parravicini JA (2017) Experimental characterization of three-dimensional flow vortical structures in submerged hydraulic jumps. J Hydro-environ Res 15:1–12CrossRefGoogle Scholar
  7. Gavin H (2011) The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems. Department of Civil and Environmental Engineering, Duke University, DurhamGoogle Scholar
  8. Géron A (2017) Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Inc, NewtonGoogle Scholar
  9. Gim Y, Ko HS (2016) Development of a three-dimensional correction method for optical distortion of flow field inside a liquid droplet. Opt Lett 41(8):1801–1804CrossRefGoogle Scholar
  10. Gim Y, Shin DH, Ko HS (2017) Development of limited-view and three-dimensional reconstruction method for analysis of electrohydrodynamic jetting behavior. Opt Express 25(8):9244–9251CrossRefGoogle Scholar
  11. Horstmann GM, Schiepel D, Wagner C (2018) Experimental study of the global flow-state transformation in a rectangular Rayleigh-Benard sample. Int J Heat Mass Transf 126:1333–1346CrossRefGoogle Scholar
  12. Kang KH, Lee SJ, Lee CM, Kang IS (2004) Quantitative visualization of flow inside an evaporating droplet using the ray tracing method. Meas Sci Technol 15(6):1104CrossRefGoogle Scholar
  13. Kim H, Stone HA (2018) Direct measurement of selective evaporation of binary mixture droplets by dissolving materials. J Fluid Mech 850:769–783CrossRefGoogle Scholar
  14. Kim H, Große S, Elsinga GE, Westerweel J (2011) Full 3D-3C velocity measurement inside a liquid immersion droplet. Exp Fluids 51(2):395–405CrossRefGoogle Scholar
  15. Kim H, Boulogne F, Um E, Jacobi I, Button E, Stone HA (2016) Controlled uniform coating from the interplay of Marangoni flows and surface-adsorbed macromolecules. Phys Rev Lett 116(12):124501CrossRefGoogle Scholar
  16. Leonarda C, Vitoantonio B, Lucia C, Giuseppe M (2009) Retinal vessel extraction by a combined neural network–wavelet enhancement method. In: International conference on intelligent computing. Springer, BerlinGoogle Scholar
  17. Martins FJ, Foucaut JM, Thomas L, Azevedo LF, Stanislas M (2015) Volume reconstruction optimization for tomo-PIV algorithms applied to experimental data. Meas Sci Technol 26(8):085202CrossRefGoogle Scholar
  18. Minor G, Djilali N, Sinton D, Oshkai P (2009) Flow within a water droplet subjected to an air stream in a hydrophobic microchannel. Fluid Dyn Res 41(4):045506CrossRefGoogle Scholar
  19. Nguyen XH, Lee SH, Ko HS (2012) Comparative study on basis functions for projection matrix of three-dimensional tomographic reconstruction for analysis of droplet behavior from electrohydrodynamic jet. Appl Opt 51(24):5834–5844CrossRefGoogle Scholar
  20. Nguyen XH, Lee SH, Ko HS (2013) Analysis of electrohydrodynamic jetting behaviors using three-dimensional shadowgraphic tomography. Appl Opt 52(19):4494–4504CrossRefGoogle Scholar
  21. Nicolas F, Todoroff V, Plyer A, Le Besnerais G, Donjat D, Micheli F, Champagnat F, Cornic P, Le Sant Y (2016) A direct approach for instantaneous 3D density field reconstruction from background-oriented schlieren (BOS) measurements. Exp Fluids 57(1):13CrossRefGoogle Scholar
  22. Ohmi K, Joshi B, Panday SP (2009) A SOM based stereo pair matching algorithm for 3-D particle tracking velocimetry. In: International conference on intelligent computing. Springer, Berlin, pp 11–20CrossRefGoogle Scholar
  23. Panday SP (2016) Stereoscopic correspondence of particles for 3-dimensional particle tracking velocimetry by using genetic algorithm. J Inst Eng 12(1):10–26CrossRefGoogle Scholar
  24. Rabault J, Kolaas J, Jensen A (2017) Performing particle image velocimetry using artificial neural networks: a proof-of-concept. Meas Sci Technol 28(12):125301CrossRefGoogle Scholar
  25. Scarano F (2012) Tomographic PIV: principles and practice. Meas Sci Technol 24(1):012001CrossRefGoogle Scholar
  26. Scharnowski S, Bross M, Kähler CJ (2019) Accurate turbulence level estimations using PIV/PTV. Exp Fluids 60(1):1CrossRefGoogle Scholar
  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):193–209CrossRefGoogle Scholar
  28. Soloff SM, Adrian RJ, Liu ZC (1997) Distortion compensation for generalized stereoscopic particle image velocimetry. Meas Sci Technol 8(12):1441CrossRefGoogle Scholar
  29. Zhang Y, Wang Y, Yang B, He W (2015) A particle tracking velocimetry algorithm based on the Voronoi diagram. Meas Sci Technol 26(7):075302CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringSungkyunkwan UniversitySuwonSouth Korea
  2. 2.School of Mechanical EngineeringKAISTDaejeonSouth Korea

Personalised recommendations