scholarly journals Particle Size Segregation during Hand Packing of Coarse Granular Materials and Impacts on Local Pore-Scale Structure

2003 ◽  
Vol 2 (3) ◽  
pp. 330-337 ◽  
Author(s):  
I. Lebron ◽  
D. A. Robinson
2017 ◽  
Vol 140 ◽  
pp. 05004
Author(s):  
Pawarut Jongchansitto ◽  
Thuwachit Kanyalert ◽  
Itthichai Preechawuttipong

1993 ◽  
Vol 07 (09n10) ◽  
pp. 1865-1872 ◽  
Author(s):  
Toshiya OHTSUKI ◽  
Yoshikazu TAKEMOTO ◽  
Tatsuo HATA ◽  
Shigeki KAWAI ◽  
Akihisa HAYASHI

The Molecular Dynamics technique is used to investigate size segregation by shaking in cohesionless granular materials. Temporal evolution of the height h of the tagged particle with different size and mass is measured for various values of the particle radius and specific gravity. It becomes evident that h approaches the steady state value h∞ independent of initial positions. There exists a threshold of the specific gravity of the particle. Below the threshold, h∞ is an increasing function of the particle size, whereas above it, h∞ decreases with increasing the particle radius. The relaxation time τ towards the steady state is calculated and its dependence on the particle radius and specific gravity is clarified. The pressure gradient of pure systems is also measured and turned out to be almost constant. This suggests that the buoyancy force due to the pressure gradient is not responsible to h∞.


2021 ◽  
Vol 11 (14) ◽  
pp. 6278
Author(s):  
Mengmeng Wu ◽  
Jianfeng Wang

The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.


2014 ◽  
Vol 264 ◽  
pp. 133-139 ◽  
Author(s):  
Patrick S.M. Dougherty ◽  
Martin C. Marinack ◽  
Cecily M. Sunday ◽  
C. Fred Higgs

2008 ◽  
Vol 48 (12) ◽  
pp. 1696-1703 ◽  
Author(s):  
Hiroshi Mio ◽  
Satoshi Komatsuki ◽  
Masatoshi Akashi ◽  
Atsuko Shimosaka ◽  
Yoshiyuki Shirakawa ◽  
...  

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