Modeling and simulation of particle agglomeration in turbulent flows using a hard-sphere model with deterministic collision detection and enhanced structure models

摘要

The present paper is concerned with the modeling and simulation of particle agglomeration of rigid, dry and electrostatically neutral particles in turbulent gas flows. Based on a deterministic collision model in the framework of an Euler-Lagrange approach for the description of disperse particle-laden flows, the original agglomeration model of Kosinski and Hoffmann (2010) is improved regarding the determination of the collision time. The application area of the agglomeration model is extended towards fully three-dimensional turbulent flows simulated by the large-eddy simulation technique. Additionally, the model is enhanced by introducing three different concepts to model the structure of the arising agglomerate, namely the volume-equivalent sphere model, the inertia-equivalent sphere model and the closely-packed sphere model. Furthermore, the resulting simulation strategy for turbulent particle-laden shear flows is first analyzed and evaluated concerning the three structure models. Then, the performance of the extended agglomeration model is tested by investigating the influence of various simulation parameters such as the restitution and friction coefficients of the particles, the inclusion of the two-way coupling, the subgrid-scale model for the particles, the lift forces, the wall roughness and the particle mass loading on the agglomeration process in a turbulent particle-laden vertical channel flow. In the parameter study solely the closely-packed sphere model is adopted, since it takes the interstitial space between the agglomerated particles into account and additionally satisfies the conservation of mass and angular momentum. Based on the results, it can be concluded that the enhanced agglomeration model using the closely-packed sphere model for the arising agglomerate realistically predicts the physical behavior of the agglomeration process within particle-laden flows. (C) 2015 Elsevier Ltd. All rights reserved.