1 research outputs found
Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis
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 model (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.Comment: 7 pages, 7 figure