Theory of the Formation of Arches in Bins

1969 ◽  
Vol 91 (2) ◽  
pp. 423-433 ◽  
Author(s):  
I. A. S. Z. Peschl

The flowing of the granular materials in bins is governed, particularly in the case of small ratios of aperture diameter to particle size, by the constant formation and breaking down of arches, known as dynamical arches. In unfavorable circumstances the arches may become stable and the aperture clogged. By building up a mechanical model of the arch the fields have been found in which a stable and a dynamical arch, respectively, may be formed, enabling a bin to be judged with respect to the danger of stable arch formation. A stable field allows of studying the interaction of arch stresses and deformation of materials starting from the stress curve of the arch as a function of the curvature, and from the curve of deformation of the material as a function of the stress. It is, therefore, possible that the elasticity of the material diminishes the stable field or even reduces it to zero. In the case of nonelastic materials, the collapse of the arch may be introduced by making the lower part of the aperture wall elastic. Stable arches can now be prevented from forming by choosing the elasticity of the resilient aperture wall in such a way that if the stress increases the wall expands sufficiently to cause the required collapse. The theory underlying this solution enables the elasticity constant and the required expansion to be calculated.

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.


2018 ◽  
Vol 183 ◽  
pp. 01054
Author(s):  
Elisha Rejovitzky

The design of protective structures often requires numerical modeling of shock-wave propagation in the surrounding soils. Properties of the soil such as grain-grading and water-fraction may vary spatially around a structure and among different sites. To better understand how these properties affect wave propagation we study how the meso-structure of soils affects their equation of state (EOS). In this work we present a meso-mechanical model for granular materials based on a simple representation of the grains as solid spheres. Grain-grading is prescribed, and a packing algorithm is used to obtain periodic grain morphologies of tightly packed randomly distributed spheres. The model is calibrated by using experimental data of sand compaction and sound-speed measurements from the literature. We study the effects of graingrading and show that the pressures at low strains exhibit high sensitivity to the level of connectivity between grains. At high strains, the EOS of the bulk material of the grains dominates the behavior of the EOS of the granular material.


2021 ◽  
Vol 377 ◽  
pp. 666-675
Author(s):  
Ragunanth Venkatesh ◽  
Miha Brojan ◽  
Igor Emri ◽  
Arkady Voloshin ◽  
Edvard Govekar

2017 ◽  
Vol 17 (9) ◽  
pp. 04017077 ◽  
Author(s):  
Samaneh Amirpour Harehdasht ◽  
Mourad Karray ◽  
Mahmoud N. Hussien ◽  
Mohamed Chekired

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