Coupling particle image velocimetry and digital image analysis to characterize cluster dynamics in a fast fluidized bed
Coupling particle image velocimetry and digital image analysis to characterize cluster dynamics in a fast fluidized bed
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DOI：
10.1016/j.powtec.2023.119267
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发表时间：
2023-12
影响因子：
5.2
通讯作者：
Dong Xiao;Xiaoyun Dong;Shanwei Hu;Xinhua Liu
中科院分区：
文献类型：
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作者：
Dong Xiao;Xiaoyun Dong;Shanwei Hu;Xinhua Liu
颗粒团簇影响气固流化床的相间相互作用，并对流化床的整体性能起主导作用。在这项工作中，粒子图像测速仪，粒子跟踪测速仪和数字图像分析耦合来表征在实验室规模的提升管中的集群动力学。采用基于灰度分布的大津算法对颗粒群进行识别，提取颗粒群的等效粒径、颗粒浓度和速度分布等特征参数，并分析了不同操作条件下颗粒群的分布特征。结果发现，宏观操作条件影响集群特性显着，而颗粒组成的影响是次要的。一个双峰或倾斜的概率密度函数观察到不仅是时间平均的流体动力学速度，但也颗粒速度周围的集群界面，表明稀相和密相共存，打破了平衡假设。进一步讨论了颗粒温度的非均匀分布，指出颗粒温度在细观和微观尺度上均表现出明显的各向异性。
Particle clusters affect interphase interaction and dominate the overall performance of gas-solid fluidized beds. In this work, the particle image velocimetry, particle tracking velocimetry and digital image analysis were coupled to characterize clustering dynamics in a lab-scale riser. The clusters were identified by the Otsu algorithm based on grayscale distribution, and the cluster properties such as equivalent size, solids concentration and velocity distribution were extracted and analyzed under various operating conditions. It was found macroscopic operating conditions affect cluster characteristics significantly whereas the effect of particle composition is secondary. A bimodal or skewed probability density function was observed for not only the time-averaged hydrodynamic velocity but also the particle velocity around the cluster interface, indicating the coexistence of dilute and dense phases and the breaking of equilibrium assumption. The nonuniform distributions for laminar and Reynolds-stress-like granular temperatures were further discussed, which show remarkable anisotropy at both the microscopic and mesoscopic scales.