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Rod-like particles matching algorithm based on SOM neural network in dispersed two-phase flow measurements
KTH, School of Engineering Sciences (SCI), Mechanics. KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, School of Chemical Science and Engineering (CHE), Centres, Wallenberg Wood Science Center.
2014 (English)In: Experiments in Fluids, ISSN 0723-4864, E-ISSN 1432-1114, Vol. 55, no 4, 1705- p.Article in journal (Refereed) Published
Abstract [en]

A matching algorithm based on self-organizing map (SOM) neural network is proposed for tracking rodlike 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.

Place, publisher, year, edition, pages
2014. Vol. 55, no 4, 1705- p.
Keyword [en]
Algorithms, Conformal mapping, Feature extraction, Optical data processing, Suspensions (fluids), Two phase flow, Velocimeters, Dispersed two-phase flows, Experimental approaches, Nonspherical particle, Particle image velocimetries, Particle tracking velocimetry, Position and orientations, Self-organizing map neural network, Turbulent channel flows
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-145596DOI: 10.1007/s00348-014-1705-9ISI: 000334687800021ScopusID: 2-s2.0-84898813331OAI: diva2:723600

QC 20140611

Available from: 2014-06-11 Created: 2014-05-23 Last updated: 2014-06-11Bibliographically approved

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Lundell, Fredrik
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MechanicsLinné Flow Center, FLOWWallenberg Wood Science Center
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