Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis

Yeonghyeon Gim, Dong Kyu Jang, Dong Kee Sohn, Hyoungsoo Kim, Han Seo Ko
<span title="2020-01-14">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ncungmpb5jbnnfoxaixzvrbygy" style="color: black;">Experiments in Fluids</a> </i> &nbsp;
Three-dimensional particle tracking velocimetry (3D-PTV) technique is widely used to acquire the complicated trajectories of particles and flow fields. It is known that the accuracy of 3D-PTV depends on the mapping function to reconstruct three-dimensional particles locations. The mapping function becomes more complicated if the number of cameras is increased and there is a liquid-vapor interface, which crucially affect the total computation time. In this paper, using a shallow neural network
more &raquo; ... del (SNN), we dramatically decrease the computation time with a high accuracy to successfully reconstruct the three-dimensional particle positions, which can be used for real-time particle detection for 3D-PTV. The developed technique is verified by numerical simulations and applied to measure a complex solutal Marangoni flow patterns inside a binary mixture droplet.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s00348-019-2861-8">doi:10.1007/s00348-019-2861-8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qymipuhehrgylbxo2idlfv2yai">fatcat:qymipuhehrgylbxo2idlfv2yai</a> </span>
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