Three-dimensional analysis of trickle flow hydrodynamics: Computed tomography image acquisition and processing

Computational Modeling of Trickle Bed Reactors

“…In general, the observed porosity distribution is dependent upon the length scale (resolution) at which the measurements are performed. Typically the high-resolution (finer than the particle size) imaging yields the bimodal distribution, i.e., each pixel in the image is either occupied by solid phase or represents a void (see images obtained in a study of Baldwin et al or van der Merwe et al). Axial averaging of such data (or equivalently, the use of techniques with coarser resolution) yields the typical oscillatory pattern of porosity in the near wall region that extends about 5–10 particle diameters toward the interior of the bed.…”

A new algorithm for segmentation of ultrafast X-ray tomographed gas–liquid flows

“…However, this method needs clear gradient courses; gradient values caused by noisy effects lead on segmentation errors. Advanced gradient applying methods like presented in van der Merwe et al [28], which unified the Cannyalgorithm and the Hough-transformation, aren't useful for real two-phase flow application, where bubbles have mostly non spherical shapes.…”

Pore-network modeling of trickle bed reactors: Pressure drop analysis

“…This lack of knowledge is due to the difficulty to investigate experimentally these ''meso''-scales of several hundred to several thousand of particles. Powerful experimental tools consisting of non-intrusive techniques as magnetic resonance imaging (MRI) or (non-)ionizing tomographies are being currently used with relative success to describe the flow at different scales [17][18][19]. As an alternative to such sophisticated experiments, CFD models based on reconstruction of fluid interfaces may be an efficient approach in the future to investigate meso-scales though at present they are limited to cells of a few number of particles [3,20].…”