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Three-dimensional particle tracking velocimetry (3D-PTV) technique is widelyused to acquire the complicated trajectories of particles and flow fields. Itis known that the accuracy of 3D-PTV depends on the mapping function toreconstruct three-dimensional particles locations. The mapping function becomesmore complicated if the number of cameras is increased and there is aliquid-vapor interface, which crucially affect the total computation time. Inthis paper, using a shallow neural network model (SNN), we dramaticallydecrease the computation time with a high accuracy to successfully reconstructthe three-dimensional particle positions, which can be used for real-timeparticle detection for 3D-PTV. The developed technique is verified by numericalsimulations and applied to measure a complex solutal Marangoni flow patternsinside a binary mixture droplet.

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