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Dense particle tracking using a learned predictive model
Experiments in Fluids ( IF 2.4 ) Pub Date : 2020-10-01 , DOI: 10.1007/s00348-020-03061-y
Kevin Mallery , Siyao Shao , Jiarong Hong

The velocity resolution for particle tracking velocimetry is limited by the ability to link multiple instances of the same particle captured over time to form trajectories. This becomes increasingly difficult as the particle speed and concentration increase. To address these concerns, we propose a data-driven approach to generate a learned predictive model capable of accurately estimating future particle behavior. The model uses the long short-term memory (LSTM) recurrent neural network architecture to predict a particle’s velocity from its past positions. The model achieves increased linking performance with a negligible increase in the computational cost. Historical trajectories demonstrating the range of expected behaviors for a given application are used to train the model and can be collected using either of two methods. Manual filtering and selection can be used to select exemplary trajectories produced by an incomplete or inadequate method. Supplemental experiments with reduced tracer concentration can also produce training data. Both methods are demonstrated through experimental validation. The ability of the learned predictor to accurately link particles at tracer concentrations and flow speeds where traditional methods fail is demonstrated using two simulated flow cases. Three experimental cases are presented to demonstrate the performance of the proposed method under challenging conditions. Each case tracks the 3D positions of particles captured using microscopic digital inline holography although the approach is generalizable and can be applied to data obtained with other 2D and 3D PTV techniques. The selected cases—turbulent channel flow, flow through a T-junction, and swimming microorganisms—demonstrate the broad applicability of the proposed method to different fields with unique challenges.

中文翻译:

使用学习的预测模型进行密集粒子跟踪

粒子跟踪测速的速度分辨率受到将随时间捕获的同一粒子的多个实例连接起来以形成轨迹的能力的限制。随着粒子速度和浓度的增加,这变得越来越困难。为了解决这些问题,我们提出了一种数据驱动的方法来生成能够准确估计未来粒子行为的学习预测模型。该模型使用长短期记忆 (LSTM) 递归神经网络架构来预测粒子从过去位置的速度。该模型实现了更高的链接性能,而计算成本的增加可以忽略不计。展示给定应用程序的预期行为范围的历史轨迹用于训练模型,并且可以使用两种方法之一收集。手动过滤和选择可用于选择由不完整或不适当的方法产生的示例性轨迹。减少示踪剂浓度的补充实验也可以产生训练数据。这两种方法都通过实验验证得到证明。使用两个模拟流动案例证明了学习预测器在传统方法失败的情况下以示踪剂浓度和流速准确连接粒子的能力。提出了三个实验案例来证明所提出的方法在具有挑战性的条件下的性能。每个案例都跟踪使用显微数字在线全息术捕获的粒子的 3D 位置,尽管该方法是可推广的,并且可以应用于其他 2D 和 3D PTV 技术获得的数据。选定的案例——湍流通道流,
更新日期:2020-10-01
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