In the past few decades various particle image–based volumetric flow measurement techniques have been developed that have demonstrated their potential in accessing unsteady flow properties quantitatively in various experimental applications in fluid mechanics. In this review, we focus on physical properties and circumstances of 3D particle–based measurements and what knowledge can be used for advancing reconstruction accuracy and spatial and temporal resolution, as well as completeness. The natural candidate for our focus is 3D Lagrangian particle tracking (LPT), which allows for position, velocity, and acceleration to be determined alongside a large number of individual particle tracks in the investigated volume. The advent of the dense 3D LPT technique Shake-The-Box in the past decade has opened further possibilities for characterizing unsteady flows by delivering input data for powerful data assimilation techniques that use Navier–Stokes constraints. As a result, high-resolution Lagrangian and Eulerian data can be obtained, including long particle trajectories embedded in time-resolved 3D velocity and pressure fields.

中文翻译：

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流体力学中的 3D 拉格朗日粒子追踪


在过去的几十年里，各种基于粒子图像的体积流量测量技术得到了发展，这些技术已经证明了它们在流体力学的各种实验应用中定量获取非定常流动特性的潜力。在这篇综述中，我们重点关注基于 3D 粒子的测量的物理特性和环境，以及哪些知识可用于提高重建精度、空间和时间分辨率以及完整性。我们关注的自然候选者是 3D 拉格朗日粒子跟踪 (LPT)，它允许在研究体积中与大量单个粒子轨迹一起确定位置、速度和加速度。过去十年中，密集 3D LPT 技术 Shake-The-Box 的出现，通过为使用纳维-斯托克斯约束的强大数据同化技术提供输入数据，为表征不稳定流提供了更多可能性。因此，可以获得高分辨率拉格朗日和欧拉数据，包括嵌入时间分辨 3D 速度和压力场中的长粒子轨迹。