粒子(生态学)
流量(数学)
障碍物
机械
颗粒流
材料科学
物理
地质学
离散元法
法学
海洋学
政治学
作者
Xiandong Liu,Hai Zhang,Hairui Yang,Yang Zhang,Junfu Lyu
标识
DOI:10.1016/j.cej.2022.139719
摘要
• A SPIV technique for dense particle flow field measurement was proposed. • CPFD model was capable of accurately simulating the dense particle flow field. • Simulated dense particle velocity was very sensitive to ε cp values. • Multiple parameter choices predicted the same mass flux but different flow fields. The computational particle fluid dynamic (CPFD) method is a common approach to simulating dense particle flow, but the direct validation of the CPFD model using the particle velocity distribution is lacking. Taking particle flow around an obstacle as an example, the accuracy of the CPFD method in the simulation of the dense particle velocity field was validated and evaluated in the present study. The particle flow field was experimentally visualized using a quasi-2D flow channel and the particle velocity distribution was measured by a newly-proposed stained-particle image velocimetry technique. The simulated particle velocity using the CPFD method was compared with the measured data and the results indicated that the CPFD method was capable of accurately predicting the particle flow velocity distribution as well as the overall parameters such as the apparent mass flux if the CPFD model parameters were properly adopted. The sensitivity analysis of the simulated particle velocity to the key CPFD parameters was conducted and the results showed that the simulated particle velocity distribution was quite sensitive to the particle pack volume fraction, while the particle stress model parameters and particle-to-wall restitution coefficients show less and close sensitivity in the dense particle flow situations. The recommended model parameters were given in the present study. It is also found that multiple choices of the model parameter combinations could all give accurate overall mass flow predictions but might lead to very different flow velocity distributions with a prediction error even higher than 45%. One should directly verify the CPFD simulated flow velocity using the experimental data if the flow field study was desired.
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