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 volumethickness and 0.1 ppp particle density, at least, are necessary. In such experimental conditions, signal toghost ratio of object images and accuracy of vectors are problems. To tackle these problems, influenceof number of cameras, image pre-processing and vector post-processing to the accuracy of TomographicPIV 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 issuitable for image pre-processing for such conditions. Although this filter results in lower signal to ghostratio, better vector field without less spurious vectors than a traditional one. This signal to ghost ratio isimproved linearly by increasing the number of camera up to 8 and accuracy of velocity vectors alsoincreasing up to 8 at any particle density and volume thickness conditions. This improvement byincreasing the number of camera is experimentally proved as a first time. Obtained velocity vectors arefiltered by spatial filter in physical domain and frequency domain to reduce measurement noise. Filteredvelocity profile of thick volume measurable domain, 135 x 230 x 50 mm3 in air, is well coincident withprevious experiments even in velocity fluctuation and Reynolds stress. To resolve turbulent fine scalevortex and large scale vortex simultaneously, more than 6-camera are needed for the case of 0.12 pppparticle density and 50 mm volume thickness. For the case of 0.45 ppp and 50 mm volume thicknesswith 8-camera has also enough accuracy to access velocity fluctuation and Reynolds stress. This paperachieves the large measurable domain, 160 x 220 x 80 mm3, at the particle density of 0.53 ppp.