基于聚类算法的提升管内颗粒聚团演化行为研究

The images of instantaneous particle distribution in a riser are segmented by K-means clustering algorithm, and the inner structure and evolution process of particle agglomeration are captured. The flow characteristics of cluster core in different regions were explored according to the gray histogram of cluster region. This study found that when the solid holdup is high, a dense core is formed inside the clusters, which are more stable. They are composed of the cluster core and the surrounding cluster clouds. When the solid holdup is low, the cluster core will disappear.