Abstract

Particle image velocimetry (PIV) is a powerful image correlation method for measuring bulk velocity fields of flowing media. It typically uses optical images, representing quasi-two-dimensional experimental slices, to measure a single velocity value at each in-plane position. However, projection-based imaging methods, such as x-ray radiography or shadowgraph imaging, encode additional out-of-plane information that regular PIV is unable to capture. Here, we introduce a new image analysis method, named deep velocimetry, that goes beyond established PIV methods and is capable of extracting full velocity distributions from projected images. The method involves solving a deconvolution inverse problem to recover the distribution at each in-plane position, and is validated using artificial data as well as controlled laboratory x-ray experiments. The additional velocity information delivered by deep velocimetry could provide new insight into a range of fluid and granular flows where out-of-plane variation is significant.

Graphic abstract

中文翻译：

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深测速：从流动介质的投影图像中提取全速分布

摘要

粒子图像测速（PIV）是一种强大的图像相关方法，用于测量流动介质的体积速度场。它通常使用代表准二维实验切片的光学图像在每个面内位置测量单个速度值。但是，基于投影的成像方法（例如X射线射线照相术或阴影图成像）对常规PIV无法捕获的其他平面外信息进行编码。在这里，我们介绍了一种新的图像分析方法，称为深度测速法，它超越了已建立的PIV方法，并且能够从投影图像中提取全速分布。该方法涉及解决反卷积逆问题，以恢复每个面内位置的分布，并且已使用人工数据以及受控的实验室X射线实验进行了验证。

图形摘要