Applicability of Markov chain-based stochastic model for bubbling fluidized beds  

基于马尔科夫链随机模型的鼓泡床适用性（英文）

作　　者：庄亚明[1] 陈晓平[1] 刘道银[1] 

机构地区：[1]东南大学能源热转换及其过程测控教育部重点实验室,南京210096

出　　处：《Journal of Southeast University(English Edition)》2015年第2期249-253,共5页东南大学学报（英文版）

基　　金：The National Science Foundation of China(No.51276036,51306035);the Fundamental Research Funds for the Central Universities(No.KYLX_0114)

摘　　要：A Markov chain-based stochastic model （MCM） is developed to simulate the movement of particles in a 2D bubbling fluidized bed （BFB）. The state spaces are determined by the discretized physical cells of the bed, and the transition probability matrix is directly calculated by the results of a discrete element method （DEM） simulation. The Markov property of the BFB is discussed by the comparison results calculated from both static and dynamic transition probability matrices. The static matrix is calculated based on the Markov chain while the dynamic matrix is calculated based on the memory property of the particle movement. Results show that the difference in the trends of particle movement between the static and dynamic matrix calculation is very small. Besides, the particle mixing curves of the MCM and DEM have the same trend and similar numerical values, and the details show the time averaged characteristic of the MCM and also expose its shortcoming in describing the instantaneous particle dynamics in the BFB.为了模拟二维鼓泡流化床(BFB)中的颗粒运动,建立了基于马尔科夫链的随机模型(MCM).用离散化的床体网格定义状态空间,并根据离散单元模型(DEM)的运算结果直接计算转移概率矩阵.通过对比静态和动态转移概率矩阵的模拟结果来讨论BFB的马尔科夫特性.基于马尔科夫链计算静态矩阵,基于颗粒运动有后效性计算动态矩阵.结果表明:静态和动态矩阵模拟的颗粒运动趋势差别很小.此外,MCM和DEM模拟的颗粒混合曲线趋势相同且数值相近,曲线细节表明了M CM的时均性特点,也暴露了其在描述BFB颗粒运动瞬时特性方面的缺陷.

关 键 词：stochastic model Markov chain discrete elementmethod （DEM） bubbling fluidized bed （BFB） 

分 类 号：TQ16[化学工程—高温制品工业]