Denoising PIV velocity fields and improving vortex identification using spatial filters

공간 필터를 이용한 PIV 속도장의 잡음 제거 및 와류 식별 개선

Jung, Hyunkyun;Lee, Hoonsang;Hwang, Wontae

  • Received : 2019.08.05
  • Accepted : 2019.08.15
  • Published : 2019.08.31


A straightforward strategy for particle image velocimetry (PIV) interrogation and post-processing has been proposed, aiming at reducing errors and clarifying vortex structures. The interrogation window size should be kept small to reduce bias error and improve spatial resolution. A spatial filter is then applied to the velocity field to reduce random error and clarify flow structure. The performance of three popular spatial filters were assessed: box filter, median filter, and local quadratic polynomial regression filter. In order to quantify random uncertainty, the image matching (IM) method is applied to an experimental dataset of homogeneous and isotropic turbulence (HIT) obtained by 2D-PIV. We statistically analyze the uncertainty propagation through the spatial filters, and verify the reduction in random uncertainty. Moreover, we illustrate that the spatial filters help clarify vortex structures using vortex identification criteria. As a result, PIV random uncertainty was reduced and the vortex structures became clearer by spatial filtering.


PIV;Denoising;Spatial filter;Uncertainty analysis;Vortex identification


  1. Fincham, A. M. and Spedding, G. R., 1997, "Low cost, high resolution DPIV for measurement of turbulent fluid flow," Experiments in Fluids, Vol. 23, No. 6, pp. 449-462.
  2. Scharnowski, S., Hain, R. and Kahler, C. J., 2012, "Reynolds stress estimation up to single-pixel resolution using PIV-measurements," Experiments in Fluids, Vol. 52, No. 4, pp. 985-1002.
  3. Meunier, P. and Leweke, T., 2003, "Analysis and treatment of errors due to high velocity gradients in particle image velocimetry," Experiments in Fluids, Vol. 35, No. 5, pp. 408-421.
  4. Westerweel, J., 2008, "On velocity gradients in PIV interrogation," Experiments in Fluids, Vol. 44, No. 5, pp. 831-842.
  5. Sciacchitano, A., Wieneke, B. and Scarano, F., 2013, "PIV uncertainty quantification by image matching," Measurement Science and Technology, Vol. 24, No. 4, pp. 16.
  6. Wieneke, B., 2017, "PIV anisotropic denoising using uncertainty quantification," Experiments in Fluids, Vol. 58, No. 8, pp. 10.
  7. Lee, H. S., Han, K. H., Park, H. J., Jung, H. K. and Hwang, W., 2018, "Generation and characterization of homogeneous isotropic turbulence," Journal of The Korean Society of Visualization, Vol. 16, No. 1, pp. 21-29.
  8. Thielicke, W. and Stamhuis, E. J., 2014, "PIVlab - towards user-friendly, affordable and accurate digital particle image velocimetry in MATLAB," Journal of Open Research Software, Vol. 2, No. 1, pp. e30.
  9. Westerweel, J. and Scarano, F., 2005, "Universal outlier detection for PIV data," Experiments in Fluids, Vol. 39, No. 6, pp. 1096-1100.
  10. Sciacchitano, A. and Wieneke, B., 2016, "PIV uncertainty propagation," Measurement Science and Technology, Vol. 27, No. 8, pp. 16.
  11. Westerweel, J., 1994, "Efficient detection of spurious vectors in particle image velocimetry data," Experiments in Fluids, Vol. 16, No. 3-4, pp. 236-247.
  12. Adrian, R. J., Christensen, K. T. and Liu, Z. C., 2000, "Analysis and interpretation of instantaneous turbulent velocity fields," Experiments in Fluids, Vol. 29, No. 3, pp. 275-290.
  13. Hunt, J. C. R., Wray, A. A. and Moin, P., 1988, "Eddies, streams, and convergence zones in turbulent flows," Center for Turbulence Research Report CTR-S88, pp. 193-208.
  14. Chen, Q. G., Zhong, Q., Qi, M. L. and Wang, X. K., 2015, "Comparison of vortex identification criteria for planar velocity fields in wall turbulence," Physics of Fluids, Vol. 27, No. 8, pp. 13.
  15. Jeong, J. and Hussain, F., 1995, "On the identification of a vortex," Journal of Fluid Mechanics, Vol. 285, pp. 69-94.


Supported by : 한국연구재단