A method to provide statistical measures of large-scale instantaneous particle clusters from planar images

Abstract

Particle clusters are preferential accumulations of a solid, secondary phase that can be caused by turbulence. It is well known that particle clusters can influence the performance of systems employing suspension flows, such as pulverised fuel combustion systems. However, statistical analysis of clusters is limited by available methods to quantify them. In the current study, a method to identify planar slices of large-scale particle clusters from planar images of instantaneous particle distributions is presented. The method employs smoothing of instantaneous particle scatter images by a length scale, L S , to produce pseudo-scalar fields of particle distributions. The scalar fields are compared with mean (not smoothed) images to produce cluster masks that are then multiplied by the original instantaneous image to produce a map of the locations of cluster slices. The sensitivity to the smoothing length scale is assessed parametrically for its influence on the statistical measures of the following parameters characterising slices of large-scale clusters in four representative flows: the physical locations of the cluster slice centroids; the area of the cluster slice; and the number of cluster slices per image. While the results are influenced by the selected value of smoothing length scale, L S , the sensitivity is low in a physically reasonable range and the method performs well in this range for the four different flow cases. The method could be extended to provide volumetric measurements with suitable volumetric imaging systems.