Stochastic bubble developing model combined with Markov process of particles for bubbling fluidized beds

Abstract

This paper describes a new stochastic model for simulating particle movement in bubbling fluidized beds (BFB). The model includes a stochastic bubble developing model (SBDM) and a Markov chain based stochastic model (MCM) of particles, while current single MCM for BFB cannot afford detailed flow structure of gas and solid for further chemical reaction modeling. The bubble generating, moving and growing sub-models of SBDM are detailed introduced. SBDM is coupled with MCM by a bubble shaping sub-model. Stochastic methods and some empirical models are used in the modeling process. Samples used by the stochastic model are taken from a CFD–DEM result. Four representative cases that have different fluidized air velocities are simulated. Particle distribution and mixing calculated by CFD–DEM, MCM and SBDM–MCM are compared. Results show both MCM and SBDM–MCM can approximately reduce the computing time by 70times compared with CFD–DEM, and they can also keep the macroscopic characteristic of particle movement well from CFD–DEM. But MCM always shows a time-averaged result, and it cannot present the structure and disturbance of bubbles. While SBDM–MCM successfully simulates the development of bubbles and introduces their instantaneous disturbance to the movement of particles. Compared with MCM, the remarkable improvement of SBDM–MCM is that it can give the recurrence of bubble structure in particle distribution and the pulsating characteristics of particle mixing curves.