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Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking

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Abstract

We developed an open-source Lagrangian particle tracking (OpenLPT) based on the Shake-the-Box (Schanz, Gesemann, and Schröder, Exp. Fluids 57.5, 2016) method. The source code of OpenLPT is available on GitHub repository (@JHU-NI-LAB). The code features a new method that removes the majority of ghost particles at a high particle image density. The resulting percentage of ghost particles drops from 110% to 26% for image density at 0.125 ppp—nearly 84% of ghost particles are removed. Extensive tests of OpenLPT using synthetic data sets show that the code produces tracks with accuracy and processing time similar to the previously-reported values. In addition, OpenLPT has been parallelized to run on high-performance computing clusters to drastically increase its processing speed. To examine the code’s capability of tracking shadows of small tracers for backlit experiments, the blurred-particle effect was also included on synthetic images and OpenLPT was tested to process these noisy images. The results show that OpenLPT can also track shadows of a high-concentration of particles reliably in 3D. Based on the test, the optimal depth of field (DoF) and particle concentration for future experiments using Lagrangian shadow tracking are provided. For example, DoF controlled by the aperture should be set at around half of the size of the view area. At this DoF, most particles in the interrogation volume can be tracked, whereas particles outside the interrogation volume become too dim to affect results. 40 experimental data sets for a wide range of particle concentrations were also used for evaluating the code, and the results show a nice agreement with the synthetic tests.

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Abbreviations

\(I^i_\mathrm{part}\) :

Intensity of the \(i\mathrm{th}\) particle

C :

Track coverage (Fig. 3)

F :

Track fragmentation (Fig. 3)

\(\mathrm{Cr}\) :

Track correctness (Fig. 3)

\(\epsilon _r\) :

Mean position error for reconstructed raw tracks

T :

Processing time per frame

\(\mathrm{DoF}\) :

Depth of field

\(\mathrm{CoC}\) :

Circle of confusion

\(d_\mathrm{o}\) :

Distance between an object and the lens

\(d_\mathrm{i}\) :

Distance between the image plane and the lens

f :

Focal length of the lens

\(D_a\) :

Aperture size

\(f_L\) :

DoF/L

L :

Size of the interrogation volume

M :

Magnification ratio of the lens

\(d_p\) :

Physical diameter of a particle

\(d_e\) :

Diameter of the projected particle image

\(d_s\) :

Diameter of a particle image due to diffraction

\(d_f\) :

Diameter of a particle image due to the defocussing effect

z :

Distance of a particle to the focal plane in the depth direction

\(L_n\) :

Size of the noise zone

\(N_{\mathrm{t}}\) :

Number of all trackable particles

\(N_{\mathrm{tf}}\) :

Number of in-focus trackable particles

\(N_{\mathrm{tb}}\) :

Number of blurred trackable particles

\(N_{\mathrm{n}}\) :

Number of out-of-focus non-trackable particles

d :

D for only trackable particles

D :

Average diameter of all particles in pixels on images

\(\phi\) :

Total image density

\(\psi\) :

Effective image density (only for trackable particles)

\(C_\phi\) :

Fraction of pixels occupied by all particles

\(C_\psi\) :

Fraction of pixels occupied by trackable particles

R :

\(C_\psi /C_\phi\)

\(\epsilon _s\) :

Mean position error for smoothed tracks

e :

Calibration error

\(\varDelta _\epsilon\) :

Mean distance between reconstructed tracks and filtered tracks

\(\epsilon _\varDelta\) :

Triangulation error

l :

Average track length

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Acknowledgements

Acknowledgment is made to the Donors of the American Chemical Society Petroleum Research Fund for partial support of this research. This study was also financially supported by National Science Foundation under the Award Numbers: 1705246 and CAREER-1653389. Partial support is provided by the Oak Ridge Institute for Science and Education (ORISE) professorship to Rui Ni. The authors also thank Daniel Schanz, from German Aerospace Center (DLR), for his valuable suggestions.

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  1. Department of Mechanical Engineering, Johns Hopkins University, Baltimore, USA

    Shiyong Tan, Ashwanth Salibindla, Ashik Ullah Mohammad Masuk & Rui Ni

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  1. Shiyong Tan
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  2. Ashwanth Salibindla
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Correspondence to Rui Ni.

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Tan, S., Salibindla, A., Masuk, A.U.M. et al. Introducing OpenLPT: new method of removing ghost particles and high-concentration particle shadow tracking. Exp Fluids 61, 47 (2020). https://doi.org/10.1007/s00348-019-2875-2

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