相机数目和处理过程对厚体积层析PIV的影响

To apply Tomographic PIV to industrial flow, ex. flow around rain gutter, 50 mm volume
thickness and 0.1 ppp particle density, at least, are necessary. In such experimental conditions, signal to
ghost ratio of object images and accuracy of vectors are problems. To tackle these problems, influence
of number of cameras, image pre-processing and vector post-processing to the accuracy of Tomographic
PIV are experimentally investigated at different measurement volume thickness and particle densities.
This investigation reveals that time-series minimum subtraction at each pixel without any spatial filter is
suitable for image pre-processing for such conditions. Although this filter results in lower signal to ghost
ratio, better vector field without less spurious vectors than a traditional one. This signal to ghost ratio is
improved linearly by increasing the number of camera up to 8 and accuracy of velocity vectors also
increasing up to 8 at any particle density and volume thickness conditions. This improvement by
increasing the number of camera is experimentally proved as a first time. Obtained velocity vectors are
filtered by spatial filter in physical domain and frequency domain to reduce measurement noise. Filtered
velocity profile of thick volume measurable domain, 135 x 230 x 50 mm3 in air, is well coincident with
previous experiments even in velocity fluctuation and Reynolds stress. To resolve turbulent fine scale
vortex and large scale vortex simultaneously, more than 6-camera are needed for the case of 0.12 ppp
particle density and 50 mm volume thickness. For the case of 0.45 ppp and 50 mm volume thickness
with 8-camera has also enough accuracy to access velocity fluctuation and Reynolds stress. This paper
achieves the large measurable domain, 160 x 220 x 80 mm3, at the particle density of 0.53 ppp. 自适应粒子成像测速场仪（PIV） 德国LaVision PIV/PLIF粒子成像测速场仪 体视层析粒子成像测速系统(Tomo-PIV)