Denoising PIV velocity fields and improving vortex identification using spatial filters

공간 필터를 이용한 PIV 속도장의 잡음 제거 및 와류 식별 개선

  • Jung, Hyunkyun (Department of Mechanical and Aerospace Engineering, Seoul National University) ;
  • Lee, Hoonsang (Department of Mechanical and Aerospace Engineering, Seoul National University) ;
  • Hwang, Wontae (Department of Mechanical and Aerospace Engineering, Seoul National University Institute of Advanced Machines and Design, Seoul National University)
  • 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.


Supported by : 한국연구재단


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