Processing techniques for particle-based optical flow measurement data such as 3D particle tracking velocimetry (PTV) or the novel dense Lagrangian particle tracking method ‘Shake-the-Box’ (STB) can provide time-series of velocity and acceleration information scattered in space. The following post-processing is key to the quality of space-filling velocity and pressure field reconstruction from the scattered particle data. In this work we describe a straight-forward extension of the recently developed data assimilation scheme FlowFit, which applies physical constraints from the Navier–Stokes equations in order to simultaneously determine velocity and pressure fields as solutions to an inverse problem. We propose the use of additional artificial Lagrangian tracers (virtual particles), which are advected between the flow fields at single time instants to achieve meaningful temporal coupling. This is the most natural way of a temporal constraint in the Lagrangian data framework. Flo...

中文翻译：

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使用虚拟示踪剂粒子通过FlowFit增强物理约束流场重建中的时间一致性

基于粒子的光流测量数据的处理技术，例如3D粒子跟踪测速（PTV）或新颖的密集拉格朗日粒子跟踪方法“摇一摇”（STB），可以提供在空间中散布的速度和加速度信息的时间序列。接下来的后期处理对于从散射粒子数据重建空间填充速度和压力场的质量至关重要。在这项工作中，我们描述了最近开发的数据同化方案FlowFit的直接扩展，该方案应用了Navier–Stokes方程的物理约束，以便同时确定速度和压力场作为反问题的解决方案。我们建议使用其他人工拉格朗日示踪剂（虚拟粒子），它们在单个时刻在流场之间平移，以实现有意义的时间耦合。这是拉格朗日数据框架中最自然的时间约束方式。飘...