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Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements

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Abstract

A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rod-like particles in 2D optical measurements of dispersed two-phase flows. It is verified by both synthetic images of elongated particles mimicking 2D suspension flows and direct numerical simulations-based results of prolate particles dispersed in a turbulent channel flow. Furthermore, the potential benefit of this algorithm is evaluated by applying it to the experimental data of rod-like fibers tracking in wall turbulence. The study of the behavior of elongated particles suspended in turbulent flows has a practical importance and covers a wide range of applications in engineering and science. In experimental approach, particle tracking velocimetry of the dispersed phase has a key role together with particle image velocimetry of the carrier phase to obtain the velocities of both phases. The essential parts of particle tracking are to identify and match corresponding particles correctly in consecutive images. The present study is focused on the development of an algorithm for pairing non-spherical particles that have one major symmetry axis. The novel idea in the algorithm is to take the orientation of the particles into account for matching in addition to their positions. The method used is based on the SOM neural network that finds the most likely matching link in images on the basis of feature extraction and clustering. The fundamental concept is finding corresponding particles in the images with the nearest characteristics: position and orientation. The most effective aspect of this two-frame matching algorithm is that it does not require any preliminary knowledge of neither the flow field nor the particle behavior. Furthermore, using one additional characteristic of the non-spherical particles, namely their orientation, in addition to its coordinate vector, the pairing is improved both for more reliable matching at higher concentrations of dispersed particles and for higher robustness against loss of particle pairs between image frames.

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Acknowledgments

The first author has been supported by The Research Council of Norway through a research fellowship on “Non-spherical particles in fluid turbulence” (Project No 191201/F20).

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Authors and Affiliations

  1. Division of Fluids Engineering, Department of Energy and Process Engineering, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway

    Afshin Abbasi Hoseini & Helge I. Anderson

  2. Faculty of Natural Sciences and Technology, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway

    Zahra Zavareh

  3. Linné Flow Centre, KTH Mechanics and Wallenberg Wood Science Centre, Royal Institute of Technology, 100 44, Stockholm, Sweden

    Fredrik Lundell

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  1. Afshin Abbasi Hoseini
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  2. Zahra Zavareh
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  3. Fredrik Lundell
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  4. Helge I. Anderson
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Correspondence to Afshin Abbasi Hoseini.

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Abbasi Hoseini, A., Zavareh, Z., Lundell, F. et al. Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements. Exp Fluids 55, 1705 (2014). https://doi.org/10.1007/s00348-014-1705-9

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  • DOI: https://doi.org/10.1007/s00348-014-1705-9

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