Particle agglomeration in flows: Fast data-driven spatial decomposition algorithm for CFD simulations

Abstract

Computational fluid dynamics simulations in practical industrial/environmental cases often involve non-homogeneous concentrations of particles. In Euler–Lagrange simulations, this can induce the propagation of numerical error when the number of collision/agglomeration events is computed using mean-field approaches. In fact, mean-field statistical collision models allow to sample the number of collision events using a priori information on the frequency of collisions (the collision kernel). Yet, since such methods often rely on the mesh used for the Eulerian simulation of the fluid phase, the particle number concentration within a given cell might not be homogeneous, leading to numerical errors. In this article, we apply the data-driven spatial decomposition (D2SD) algorithm to control such error in simulations of particle agglomeration. Significant improvements are made to design a fast D2SD version, minimising the additional computational cost by developing re-meshing criteria. Through the application to some practical simulation cases, we show the importance of splitting the domain when computing agglomeration events in Euler/Lagrange simulations, so that within each elementary cell there is a spatially uniform distribution of particles.