Gas-solid flow characteristics of fluidized bed with binary particles

被引:12
|
作者
Bai, Ling [1 ,2 ]
Zhao, Zhenjiang [2 ]
Lv, Wanning [2 ]
Zhou, Ling [2 ]
机构
[1] Jiangsu Univ, Sch Energy & Power Engn, Zhenjiang 212013, Peoples R China
[2] Jiangsu Univ, Res Ctr Fluid Machinery Engn & Technol, Zhenjiang 212013, Peoples R China
基金
中国国家自然科学基金;
关键词
CFD; DEM; Binary particles; Fluidized bed; Gas -solid flow; High-speed photography; CFD-DEM SIMULATION; HEAT-TRANSFER; NUMERICAL-SIMULATION; ASSEMBLIES; MIXTURES; FIDELITY; REACTOR; MODEL;
D O I
10.1016/j.powtec.2022.118206
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
The fluidization behavior of binary mixed particles is investigated, which is important for improving the performance of dense gas-solid fluidized bed systems. Based on the coupling of computational fluid dynamics and the discrete element method (CFD-DEM), analysis of the changes in bubble shape and particle motion characteristics under the condition of mixing different ratios of coarse particles in the fluidization process. The reliability of the numerical simulation results was verified by high-speed photographic experiments. The results show that an increase in the coarse particle mixing ratio leads to a delay at the moment when oscillatory delamination of particles starts to occur during fluidization. The oscillation of the particles started at 500 ms in both the experiment and simulation with a 5:5 mixing ratio of 5 mm and 3.5 mm particles. The difference in the diameter of fine particles and the percentage of coarse particles in the mixed particles both affect the formation of C-shaped bubbles. The inlet mass flow rate is 0.007 kg/s, while the overall bed height gradually decreases. Under different blending ratios, the intersection of the average velocity curves along the Z-axis intersects around zero at 400 ms, and the average velocity value is negatively correlated with the blending ratio. This investigation can provide a reference for the optimal design of fluidized beds, especially for biomass applications with binary particles.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Segregation and mixing behavior of geldart D binary particles in pulsed gas-solid fluidized bed
    Li, Yanjiao
    Du, Lintao
    Zhao, Yuemin
    Wang, Ziming
    Zhu, Fenglong
    Lu, Zhaolin
    Duan, Chenlong
    Dong, Liang
    Zhou, Chenyang
    PARTICULATE SCIENCE AND TECHNOLOGY, 2022, 40 (04) : 434 - 444
  • [42] Dispersion characteristics of slurry particles in a gas-solid fluidized bed with continuous slurry injection
    Zhang, Peng
    Cheng, Jianan
    Li, Xinke
    Sun, Jingyuan
    Ren, Congjing
    Huang, Zhengliang
    Wang, Jingdai
    Yang, Yongrong
    CHEMICAL ENGINEERING SCIENCE, 2025, 305
  • [43] COMPARISON OF MIXING INDEX FOR BINARY AND TERNARY MIXTURES OF IRREGULAR PARTICLES IN A GAS-SOLID FLUIDIZED BED
    Sahoo, A.
    Garg, R. K.
    Roy, G. K.
    CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2011, 89 (04): : 825 - 832
  • [44] Dynamics of jetsam layer in continuous segregation of binary heterogeneous particles in gas-solid fluidized bed
    Babu, AP
    Krishnaiah, K
    POWDER TECHNOLOGY, 2005, 160 (02) : 114 - 120
  • [45] Effects of geometric structure of circulating fluidized bed walls on gas-solid flow characteristics
    Huazhong Univ of Science and, Technology, Wuhan, China
    Huagong Xuebao, 6 (706-711):
  • [47] CFD-DEM investigation of the gas-solid flow characteristics in a fluidized bed dryer
    Ma, Zhiyang
    Tu, Qiuya
    Liu, Zaixing
    Xu, Yi
    Ge, Ruihuan
    Wang, Haigang
    CHEMICAL ENGINEERING RESEARCH & DESIGN, 2023, 196 : 235 - 253
  • [48] FLOW CHARACTERISTICS OF BUBBLES IN A SINGLE-LAYER GAS-SOLID FLUIDIZED-BED
    HATANO, H
    YANG, R
    ISHIDA, M
    KAGAKU KOGAKU RONBUNSHU, 1992, 18 (01) : 133 - 135
  • [49] Numerical Simulation of Gas-Solid Flow in a Wurster Fluidized Bed
    Zhou, Hang
    Wang, Haigang
    Tu, Qiuya
    PROCEEDINGS OF THE 7TH INTERNATIONAL CONFERENCE ON DISCRETE ELEMENT METHODS, 2017, 188 : 1005 - 1012
  • [50] Particle flow characteristics in a gas-solid separation fluidized bed based on machine learningY
    Fu, Yanhong
    Wang, Song
    Xu, Xuan
    Zhao, Yuemin
    Dong, Liang
    Chen, Zengqiang
    FUEL, 2022, 314