Experimental investigation into segregation behavior of spherical/non-spherical granular mixtures in a thin rotating drum

被引:5
作者
Chung, Yun-Chi [1 ]
Hunt, Melany L. [2 ]
Huang, Jia-Non [3 ]
Liao, Chun-Chung [2 ,3 ]
机构
[1] Natl Cent Univ, Dept Mech Engn, 300 Zhongda Rd, Taoyuan 320317, Taiwan
[2] CALTECH, Dept Mech & Civil Engn, 1200 E Calif Blvd MC 104-44, Pasadena, CA 91125 USA
[3] Natl Kaohsiung Univ Sci & Technol, Dept Mold & Die Engn, 415 Jiangong Rd, Kaohsiung 807618, Taiwan
关键词
UNIVERSAL APPROXIMATION; NEURAL-NETWORKS; NONLINEAR OPERATORS;
D O I
10.1063/5.0190892
中图分类号
O3 [力学];
学科分类号
08 ; 0801 ;
摘要
This paper uses physical experiments to investigate the segregation behavior of binary granular mixtures in a quasi-two-dimensional rotating drum. Spherical polyformaldehyde (POM) beads and cylindrical red beans constitute the granular mixtures. The effects of particle size, particle density, and particle shape interplay during the segregation process in the spherical/non-spherical particulate system. A long-axis ratio (LAR), the ratio of the spherical POM beads' diameter to the red beans' primary dimension, was defined to explore the particle shape effect. The experimental results show that the long-axis ratio and the rotation speed play substantial roles in the granular segregation behavior. As the long-axis ratio increases, the steady-state segregation intensity decreases. An increase in the rotation speed enhances the segregation of the binary granular mixtures for each long-axis ratio studied here. In addition, the average velocity and granular temperature of spherical POM beads increase as the long-axis ratio increases. Both properties also increase as the rotation speed increases. The dynamic angle of repose for the binary mixtures increases with the increase in the long-axis ratio. Most interestingly, reverse granular segregation does occur at a long-axis ratio of 0.70 with the cylindrical red beans in the core and the spherical POM beads at the periphery for each rotation speed studied here. This reverse segregation has not been observed in previous studies. This highlights the substantial impact of particle shape on the granular segregation in binary granular mixtures.
引用
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页数:12
相关论文
共 55 条
[1]   UNIVERSAL APPROXIMATION BOUNDS FOR SUPERPOSITIONS OF A SIGMOIDAL FUNCTION [J].
BARRON, AR .
IEEE TRANSACTIONS ON INFORMATION THEORY, 1993, 39 (03) :930-945
[2]   Prediction of aerodynamic flow fields using convolutional neural networks [J].
Bhatnagar, Saakaar ;
Afshar, Yaser ;
Pan, Shaowu ;
Duraisamy, Karthik ;
Kaushik, Shailendra .
COMPUTATIONAL MECHANICS, 2019, 64 (02) :525-545
[3]   Machine Learning for Fluid Mechanics [J].
Brunton, Steven L. ;
Noack, Bernd R. ;
Koumoutsakos, Petros .
ANNUAL REVIEW OF FLUID MECHANICS, VOL 52, 2020, 52 :477-508
[4]   DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks [J].
Cai, Shengze ;
Wang, Zhicheng ;
Lu, Lu ;
Zaki, Tamer A. ;
Karniadakis, George Em .
JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 436
[5]  
Chen GT, 2019, 2019 INTERNATIONAL CONFERENCE ON MICROWAVE AND MILLIMETER WAVE TECHNOLOGY (ICMMT 2019), DOI [10.1109/icmmt45702.2019.8992852, 10.1007/s00779-019-01291-4]
[6]   UNIVERSAL APPROXIMATION TO NONLINEAR OPERATORS BY NEURAL NETWORKS WITH ARBITRARY ACTIVATION FUNCTIONS AND ITS APPLICATION TO DYNAMICAL-SYSTEMS [J].
CHEN, TP ;
CHEN, H .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 1995, 6 (04) :911-917
[7]   Physics-informed machine learning for reduced-order modeling of nonlinear problems [J].
Chen, Wenqian ;
Wang, Qian ;
Hesthaven, Jan S. ;
Zhang, Chuhua .
JOURNAL OF COMPUTATIONAL PHYSICS, 2021, 446
[8]  
Di Leoni PC, 2021, Arxiv, DOI arXiv:2105.08697
[9]  
Cook P.H., 1979, Experimental Data Base for Computer Program Assessment
[10]  
Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274