Modeling total drag force exerted on particles in dense swarm from experimental measurements using an inline image-based method
Modeling total drag force exerted on particles in dense swarm from experimental measurements using an inline image-based method
复制标题
使用基于内联图像的方法根据实验测量对密集群中的颗粒施加的总阻力进行建模
DOI：
10.1016/j.cej.2021.133485
复制
发表时间：
2021-11
影响因子：
15.1
通讯作者：
Chao Yang
中科院分区：
文献类型：
--
作者：
Haoliang Wang;Biyu Zhang;Xiangyang Li;Yiting Xiao;Chao Yang
It is of significance but still a great challenge to model drag force exerted on particles in dense systems directly from experimental measurements. In this work, a new procedure using inline experimental measurements is developed to establish a total drag model. Specifically, local flow characteristics including slip velocity, particle holdup and particle acceleration are measured simultaneously using an inline vision probe. Then, the total drag coefficient exerted on particles in suspension is calculated using the inline data with the unsteady motion of particles taken into account. At last, the total drag coefficient versus five dimensionless numbers including all important factors are correlated as C D= 3.55× 10-2 ρ r-1.82 A r 0.53 Re l 0.45 Re-1.79 α P-0.49. Systematically evaluated using the image-based results in this work and PEPT data in the reference as well as Tang model, Gidaspow model and Brucato model popularly used in suspensions, the newly developed model shows some excellent characteristics.(i) It can predict flow field and solid holdup distributions with sufficient accuracy in a wider range of holdups from dilute to dense systems.(ii) Especially, the prediction precision is significantly higher (with deviation of 5.6%) in the holdup distribution compared to Gidaspow model, Brucato model and Tang model.(iii) Furthermore, better mass conservation is always kept during the simulation process compared to the other three models. It is preliminarily inferred that some particles are not fully suspended due to the smaller drag force in the case of Gidaspow model. More studies are still needed to explain quantitatively the above evaluation results.
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影响因子：
5.2
作者：
Z. Huang;H. Wang;Q. Zhou;T. Li
通讯作者：
T. Li
影响因子：
4.7
作者：
Th.N. Zwietering
通讯作者：
Th.N. Zwietering
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
Alan D. Burns;Thomas Frank;Ian Hamill;Jun-Mei Shi
通讯作者：
Alan D. Burns;Thomas Frank;Ian Hamill;Jun-Mei Shi
影响因子：
2.1
作者：
Arun Kumar;S. Hartland
通讯作者：
Arun Kumar;S. Hartland
影响因子：
2.2
作者：
M. Lichti;H. Bart
通讯作者：
M. Lichti;H. Bart