Lattice Boltzmann based discrete simulation for gas-solid fluidization

TL;DR

This paper introduces a modified lattice Boltzmann method combined with a hard sphere algorithm and EMMS drag to simulate gas-solid fluidization, capturing detailed particle-fluid interactions with good agreement to experimental data.

Contribution

It presents a novel lattice Boltzmann based discrete simulation approach incorporating local volume fraction and relative velocity effects for gas-solid systems.

Findings

Accurately simulates bubble injection and particle clustering.

Shows good agreement with experimental data and correlations.

Captures detailed particle-fluid interaction characteristics.

Abstract

Discrete particle simulation, a combined approach of computational fluid dynamics and discrete methods such as DEM (Discrete Element Method), DSMC (Direct Simulation Monte Carlo), SPH (Smoothed Particle Hydrodynamics), PIC (Particle-In-Cell), etc., is becoming a practical tool for exploring lab-scale gas-solid systems owing to the fast development of parallel computation. However, gas-solid coupling and the corresponding fluid flow solver remain immature. In this work, we propose a modified lattice Boltzmann approach to consider the effect of both the local solid volume fraction and the local relative velocity between particles and fluid, which is different from the traditional volume-averaged Navier-Stokes equations. A time-driven hard sphere algorithm is combined to simulate the motion of individual particles, in which particles interact with each other via hard-sphere collisions, the collision detection and motion of particles are performed at constant time intervals. The EMMS (energy minimization multi-scale) drag is coupled with the lattice Boltzmann based discrete particle simulation to improve the accuracy. Two typical fluidization processes, namely, a single bubble injection at incipient fluidization and particle clustering in a fast fluidized bed riser, are simulated with this approach, with the results showing a good agreement with published correlations and experimental data. The capability of the approach to capture more detailed and intrinsic characteristics of particle-fluid systems is demonstrated. The method can also be used straightforward with other solid phase solvers.