Advertisement

Efficiency Analysis of Particle Tracking with Synthetic PIV Using SOM

  • Rubén Hernández-Pérez
  • Ruslan Gabbasov
  • Joel Suárez-Cansino
  • Virgilio López-Morales
  • Anilú Franco-Árcega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

To identify the field of velocities of a fluid, the postprocessing stage in the analysis of fluids using PIV images associates tracers in two consecutive images. Statistical methods have been used to perform this task and investigations have reported models of artificial neural networks, as well. The Self-Organized Map (SOM) model stands out for its simplicity and effectiveness, additionally to presenting areas of opportunity for exploring. The SOM model is efficient in the correlation of tracers detected in consecutive PIV images; however, the necessary operations are computationally expensive. This paper discusses the implementation of these operations on GPU to reduce the time complexity. Furthermore, the function calculating the learning factor of the original network model is too simple, and it is advisable to use one that can better adapt to the characteristics of the fluid’s motion. Thus, a proposed 3PL learning factor function modifies the original model for good, because of its greater flexibility due to the presence of three parameters. The results show that this 3PL modification overcomes the efficiency of the original model and one of its variants, in addition to decreasing the computational cost.

Keywords

GPU Parallel algorithms PIV technique SOM Artificial neural networks 

References

  1. 1.
    Westerweel, J.: Digital Particle Velocimetry, Theory and Application. Delft University Press, Netherlands (1993)Google Scholar
  2. 2.
    Grant, I., Pan, X.: An investigation of the performance of multilayer neural networks applied to the analysis of PIV images. Exp. Fluids 19, 159–166 (1995)CrossRefGoogle Scholar
  3. 3.
    Grant, I., Pan, X.: The use of neural techniques in PIV and PTV. Meas. Sci. Technol. 8, 1399–1405 (1997)CrossRefGoogle Scholar
  4. 4.
    Labonte, G.: A SOM neural network that reveals continuous displacement fields. In: IEEE World Congress on Computational Intelligence, Neural Networks Proceedings, vol. 2, pp. 880–884 (1998)Google Scholar
  5. 5.
    Labonte, G.: A new neural network for particle tracking velocimetry. Exp. Fluids 26, 340–346 (1999)CrossRefGoogle Scholar
  6. 6.
    Labonte, G.: New neural network reconstruction of fluid flows from tracer-particle displacements. Exp. Fluids 30, 399–409 (2001)CrossRefGoogle Scholar
  7. 7.
    Ohmi, K.: Neural network PIV using a self-organizing maps method. In: Proceedings of 4th Pacific Symposium Flow Visualization and Image Processing, F-4006 (2003)Google Scholar
  8. 8.
    Joshi, S.R.: Improvement of algorithm in the particle tracking velocimetry using self-organizing maps. J. Inst. Eng. 7, 6–23 (2009)CrossRefGoogle Scholar
  9. 9.
    Verber, D.: Chapter 13. Implementation of massive artificial neural networks with CUDA. In: Volosencu, C. (ed.) Cutting Edge Research in New Technologies, INTECH, pp. 277–302 (2012)Google Scholar
  10. 10.
    Hernández-Pérez, R., Gabbasov, R., Suárez-Cansino, J.: Improving performance of particle tracking velocimetry analysis with artificial neural networks and graphics processing units. Res. Comput. Sci. 104, 71–79 (2015)Google Scholar
  11. 11.
    Gottschalk, P.G., Dunn, J.R.: The five-parameter logistic: a characterization and comparison with the four-parameter logistic. Anal. Biochem. 343, 54–65 (2005)CrossRefGoogle Scholar
  12. 12.
    Gabbasov, R.F., Klapp, J., Suárez-Cansino, J., Sigalotti, L.D.G.: Numerical simulations of the Kelvin-Helmholtz instability with the gadget-2 SPH code. In: Experimental and Computational Fluid Mechanics with Applications to Physics, Enginnering and the Environment (2014). arXiv:1310.3859. [astro-ph.IM]
  13. 13.
    Monaghan, J.J.: Smoothed particle hidrodynamics. Rep. Prog. Phys. 68(8), 1703–1759 (2005)CrossRefGoogle Scholar
  14. 14.
    Jiang, M., Machiraju, R., Thompson, D.: Detection and visualization of vortices. The Visualization Handbook, pp. 296–309. Academic Press, Cambridge (2005)Google Scholar
  15. 15.
    Stewart, R.W.: Turbulence. In: Encyclopaedia Britannica Educational Corporation Film (1969)Google Scholar
  16. 16.
    Unsworth, C.A.: Chapter 3. Section 4. Particle Imaging velocimetry. In: Geomorphological Techniques, British Society for Geomorphology (2015)Google Scholar
  17. 17.
    Shi, B., Wei, J., Pang, M.: A modified cross-correlation algorithm for PIV image processing of particle-fluid two-phase flow. In: Flow Measurement and Instrumentation, October 2015, vol. 45, pp. 105–117 (2015)CrossRefGoogle Scholar
  18. 18.
    Rabault, J., Kolaas, J., Jensen, A.: Performing particle image velocimetry using artificial neural networks: a proof-of-concept. In: Measurement Science and Technology, vol. 28, no. 12 (2017)CrossRefGoogle Scholar
  19. 19.
    Rossi, R., Malizia, A., Poggi, L.A., Ciparisse, J.-F., Peluso, E., Gaudio, P.: Flow motion and dust tracking software for PIV and dust PTV. J. Fail. Anal. Prev. 16(6), 951–962 (2016)CrossRefGoogle Scholar
  20. 20.
    Jiang, C., Dong, Z., Wang, X.: An improved particle tracking velocimetry (PTV) technique to evaluate the velocity field of saltating particles. J. Arid Land 9(5), 727–742 (2017)CrossRefGoogle Scholar
  21. 21.
    Elhimer, M., Praud, O., Marchal, M., Cazin, S., Bazile, R.: Simultaneous PIV/PTV velocimetry technique in a turbulent particle-laden flow. J. Vis. 20(2), 289–304 (2017)CrossRefGoogle Scholar
  22. 22.
    Dal Sasso, S.F., Pizarro, A., Samela, C., Mita, L., Manfreda, S.: Exploring the optimal experimental setup for surface flow velocity measurements using PTV. Environ. Monit. Assess. 190, 460 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Rubén Hernández-Pérez
    • 1
  • Ruslan Gabbasov
    • 1
  • Joel Suárez-Cansino
    • 1
  • Virgilio López-Morales
    • 1
  • Anilú Franco-Árcega
    • 1
  1. 1.Intelligent Computing Research Group, Information and Systems Technologies Research Center, Engineering and Basic Sciences InstituteAutonomous University of the State of HidalgoMineral de la ReformaMexico

Personalised recommendations