基于PIV图像的多路径线流体可视化
Multi-pathline flow visualization using PIV images
AI总结：
本文提出基于PIV图像的多路径线可视化方法，通过控制曝光时间、颜色编码时间和改变参考系来增强流体可视化效果。
摘要：
在流体可视化技术中，通过生成多个路径线是较古老的方法，这些路径线由在流体中空间分布的示踪颗粒运动产生。在计算机时代，粒子图像被用于定量测量，如粒子图像测速（PIV）和粒子追踪测速（PTV）。本文提出几种后处理原始粒子图像的方法，以生成增强的流体可视化，而无需进行额外实验。三种后处理方法将被展示：1）控制曝光时间，2）用颜色编码时间信息，3）改变参考系。我们展示如何通过这三种方法在三个经典流体案例中突出不同的流体特征：涡环、前缘涡和湍流边界层。除了定量流场外，多路径线可视化还预计能增强我们从多个不同视角观察流体流动的能力。
英文摘要：
One of the oldest flow visualization techniques is through multiple pathlines generated by the movement of seeding particles spatially distributed in the flow. In the computerized era, particle images are used in quantitative measurements, such as particle image and particle tracking velocimetry (PIV and PTV). Here, we present several methods for post-processing raw particle images to generate enhanced flow visualization without a need for conducting additional experiments. Three post-processing methods will be shown: 1) controlling the exposure time, 2) color-coding temporal information, and 3) changing the frame of reference. We showcase how employing these three methods can highlight different flow features in three canonical flow cases: vortex ring, leading edge vortex, and turbulent boundary layer. In addition to the quantitative flow field, the multi-pathline visualization is expected to augment our ability to observe fluid flow from many different perspectives.
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