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Multi-pathline flow visualization using PIV images

Yukun Sun, Elijah James, Frank Fang, Jasper Agrawal, Christopher Dougherty, Cong Wang, Chris Roh

TL;DR

The paper revisits multi-pathline visualization as a post-processing approach for raw PIV/PTV images and proposes three concrete methods—exposure-time stacking, time-color encoding, and frame-of-reference shifting—to extract rich flow insights without additional experiments. By applying these methods to three canonical flows (vortex ring, leading-edge vortex, and turbulent boundary layer), it demonstrates how varying time integration, temporal color coding, and reference frames reveal features that may be missed in conventional analyses. The work contributes practical, software-centric tools that augment qualitative understanding, offer baseline visualization for quantitative fields, and enable easy generation of multi-perspective visuals or videos from existing data. Collectively, these techniques enhance interpretability and communication of complex fluid dynamics phenomena while maintaining compatibility with standard PIV/PTV datasets, with potential for broader adoption in research and education. $x(t) = x_0 + \\int_0^t \underline{u}(\underline{x},t')\,dt'$ is used to denote pathlines, highlighting the Lagrangian nature and non-Galilean invariance that motivates frame-of-reference analysis, and $I_{\text{shifted}}(x_0+\Delta x,y_0+\Delta y)=I(x_0,y_0)(1-\Delta x)(1-\Delta y)+\ldots$ illustrates subpixel image shifting for retrospective visualization.$

Abstract

One of the oldest flow visualization techniques is through multiple pathlines generated by the movement of seeding particles spatially distributed in the flow. In the computerized era, particle images are used in quantitative measurements, such as particle image and particle tracking velocimetry (PIV and PTV). Here, we present several methods for post-processing raw particle images to generate enhanced flow visualization without a need for conducting additional experiments. Three post-processing methods will be shown: 1) controlling the exposure time, 2) color-coding temporal information, and 3) changing the frame of reference. We showcase how employing these three methods can highlight different flow features in three canonical flow cases: vortex ring, leading edge vortex, and turbulent boundary layer. In addition to the quantitative flow field, the multi-pathline visualization is expected to augment our ability to observe fluid flow from many different perspectives.

Multi-pathline flow visualization using PIV images

TL;DR

The paper revisits multi-pathline visualization as a post-processing approach for raw PIV/PTV images and proposes three concrete methods—exposure-time stacking, time-color encoding, and frame-of-reference shifting—to extract rich flow insights without additional experiments. By applying these methods to three canonical flows (vortex ring, leading-edge vortex, and turbulent boundary layer), it demonstrates how varying time integration, temporal color coding, and reference frames reveal features that may be missed in conventional analyses. The work contributes practical, software-centric tools that augment qualitative understanding, offer baseline visualization for quantitative fields, and enable easy generation of multi-perspective visuals or videos from existing data. Collectively, these techniques enhance interpretability and communication of complex fluid dynamics phenomena while maintaining compatibility with standard PIV/PTV datasets, with potential for broader adoption in research and education. is used to denote pathlines, highlighting the Lagrangian nature and non-Galilean invariance that motivates frame-of-reference analysis, and illustrates subpixel image shifting for retrospective visualization.$

Abstract

One of the oldest flow visualization techniques is through multiple pathlines generated by the movement of seeding particles spatially distributed in the flow. In the computerized era, particle images are used in quantitative measurements, such as particle image and particle tracking velocimetry (PIV and PTV). Here, we present several methods for post-processing raw particle images to generate enhanced flow visualization without a need for conducting additional experiments. Three post-processing methods will be shown: 1) controlling the exposure time, 2) color-coding temporal information, and 3) changing the frame of reference. We showcase how employing these three methods can highlight different flow features in three canonical flow cases: vortex ring, leading edge vortex, and turbulent boundary layer. In addition to the quantitative flow field, the multi-pathline visualization is expected to augment our ability to observe fluid flow from many different perspectives.
Paper Structure (16 sections, 3 equations, 7 figures)

This paper contains 16 sections, 3 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Flow generation and raw particle image acquisition
  3. Vortex ring
  4. Impulsively started airfoil at $30^\circ$ AoA
  5. Turbulent boundary layer
  6. Post-processing exposure control
  7. Vortex ring
  8. Impulsively started airfoil at $30^\circ$ AoA
  9. Turbulent boundary layer
  10. Encoding time
  11. Time-lapsed color coded multi-pathline
  12. Changing frame of reference
  13. Vortex ring
  14. Impulsively started airfoil at $30^\circ$ AoA
  15. Turbulent boundary layer
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Vortex ring exposure time control. a) Single image, b) 5-Image stack, c) 25-Image stack, d) 50-Image stack, e) 100-Image stack, f) 200-Image stack. The presence of vortex begins to be visible around 25-image stacking. However, the long integration of particle paths leads to different images and highlights the path of individual particles that loop like a pig tail.
  • Figure 2: Impulsively started airfoil ($30^\circ$ AoA) exposure time control. a) Single image, b) 5-image stack, C) 25-image stack, D) 50-image stack, e) 100-images stack, f) 200-images stack. Here, too, the presence of LEV begins to be visible around 25-image stacking. Top of the trailing edge vortex is also visible at the bottom right corner in the 50-image stack.
  • Figure 3: Turbulent boundary layer (TBL) exposure time control. a) Single image, b) 5-image stack, c) 25-image stack, d) 50-image stack, e) 100-image stack, f) 200-image stack. The most informative visualization here is the 5-image stack, showing variation in flow speed due to the turbulent boundary layer. Near the wall, the pathlines are short, and away from the wall, much longer pathlines are observed. Some fluctuation inherent to the TBL can be gleaned from non-parallel pathlines.
  • Figure 4: Color-coded multi-pathline. a) Vortex ring. 50-Image stack. b) Leading edge vortex. 50-Image stack. c) Turbulent boundary layer. 5-Image stack. In all images, the direction of the flow is encoded by the color gradient.
  • Figure 5: Frame shifted : Vortex ring. a) Frame shifted, at the speed of the vortex ring. 50-Image stack. b) Frame shifted, at the speed of the vortex ring. 200-Image stack. c) Frame shifted, at 1.5 times the speed of the vortex ring. 50-Image stack.
  • ...and 2 more figures