| Abstrakt: |
Particle streak velocimetry (PSV) is a Lagrangian velocity measurement method based on streak imaging of moving particles and is regarded as the origin of particle image velocimetry (PIV) and particle tracking velocimetry (PTV). Recently, the PSV technique has undergone further developments, realizing measurements of three velocity components in three dimensions (3D3C), by combining with stereoscopic observation, defocused imaging, light field photography and /or holography. Moreover, image processing algorithms based on deep learning have been successfully applied to PSV. Compared with PIV and PTV, the PSV technique can exhibit several advantages, including extending the upper limit of the velocity measurement range under the same equipment conditions, measuring with lower illumination intensity, often an overall lower equipment complexity and cost for the same measuring requirement, as well as avoiding the particle matching problems of PTV. However, the PSV method also has obstacles to overcome, such as directional ambiguity and the difficulty in identifying streak crossings. For the directional ambiguity problem, there are currently time-coding, color-coding, brightness-coding and determination methods using additional image frames that can be employed. The main application areas of PSV currently include microfluidics, high-velocity flows and large-scale flow field measurements. This review presents the state of the art of PSV and summarizes advantages, disadvantages, accuracy, complexity and application of various configurations. The configurations discussed are focused on those measuring three velocity components and several examples are described in which PSV can be advantageously applied. The review concludes with some future developments that can be anticipated. [ABSTRACT FROM AUTHOR] |
Full text from SpringerLink