论文标题
使用浅神经网络进行实时分析的三维粒子跟踪速度法
Three-dimensional particle tracking velocimetry using shallow neural network for real-time analysis
论文作者
论文摘要
三维粒子跟踪速度法（3D-PTV）技术被广泛用于获取颗粒和流场的复杂轨迹。众所周知，3D-PTV的精度取决于映射函数重建三维粒子位置。如果相机数量增加并且存在液态蒸气界面，则映射函数将变得更加复杂，这对总计算时间至关重要。在本文中，使用浅层神经网络模型（SNN），我们以高精度大大减少了计算时间，以成功地重建三维粒子位置，该位置可用于3D-PTV的实时粒子检测。通过数值模拟验证了开发的技术，并应用于测量二元混合物液滴内部复杂的溶质马龙尼流动模式。
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.
