scholarly journals Dilatancy of frictional granular materials under oscillatory shear with constant pressure

2021 ◽  
Vol 249 ◽  
pp. 02011
Author(s):  
Daisuke Ishima ◽  
Hisao Hayakawa
Keyword(s):  
Friction Coefficient ◽  
Numerical Simulations ◽  
Granular Materials ◽  
Strain Amplitude ◽  
Constant Pressure ◽  
Oscillatory Shear ◽  
Granular System ◽  
Mutual Friction ◽  
Two Dimensional ◽  
Confinement Pressure

We perform numerical simulations of a two-dimensional frictional granular system under oscillatory shear confined by constant pressure. We found that the system undergoes dilatancy as the strain increases. We confirmed that compaction also takes place at an intermediate strain amplitude for a small mutual friction coefficient between particles. We also found that compaction depends on the confinement pressure while dilatancy little depends on the pressure.

scholarly journals Sidewall friction in confined surface flows of granular materials

2021 ◽  
Vol 249 ◽  
pp. 03024
Author(s):  
Patrick Richard ◽  
Alexandre Valance ◽  
Renaud Delannay
Keyword(s):  
Friction Coefficient ◽  
Boundary Conditions ◽  
Numerical Simulations ◽  
Granular Materials ◽  
Granular Flows ◽  
Granular Systems ◽  
Surface Flows ◽  
Continuum Theories ◽  
Shed Light ◽  
Effective Friction

We report numerical simulations of surface granular flows confined between two sidewalls. These systems exhibit both very slow and very energetic flows. Zhu et al. [1] have shown that in energetic confined systems, the Froude number at sidewalls and the sidewall effective friction coefficient are linked through a unique relation. We show that this relation is also valid for creep flows. It is independent of the angle of the flow but depends on the sidewall-grain friction coefficient. Our results shed light on boundary conditions that have to be used at sidewalls in continuum theories aiming to capture the behavior of granular systems from creeping to energetic flows.

scholarly journals Angular Dependence of Highly Nonlinear Pulse Splitting in a Two Dimensional Granular Network

2010 ◽  
Author(s):  
D. Ngo ◽  
F. Fraternali ◽  
C. Daraio
Keyword(s):  
Experimental Data ◽  
Stainless Steel ◽  
Numerical Simulations ◽  
Angular Dependence ◽  
Granular System ◽  
Two Dimensional ◽  
Highly Nonlinear ◽  
Solitary Pulse ◽  
First Time ◽  
Pulse Splitting

We investigate experimentally and numerically the propagation of highly nonlinear signals in a branched two-dimensional granular system composed by chains of uniform spherical beads. The system consists of a Y-shaped guide with various branch angles in which stainless steel spheres are arranged. We study the dynamic behavior of a solitary pulse crossing the bifurcated interface, and splitting between the two branches. We report for the first time the dependence of the split pulses’ speed on the branch angles. Numerical simulations based on Hertzian interaction between the particles are found in agreement with the experimental data.

scholarly journals Towards explicit regulating-ion-transport: nanochannels with only function-elements at outer-surface

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qun Ma ◽  
Yu Li ◽  
Rongsheng Wang ◽  
Hongquan Xu ◽  
Qiujiao Du ◽  
...  
Keyword(s):  
Ion Transport ◽  
Numerical Simulations ◽  
Power Density ◽  
Outer Surface ◽  
Internal Resistance ◽  
Porous Membranes ◽  
Two Dimensional ◽  
Ionic Selectivity ◽  
Key Features ◽  
Energy Conversion Devices

AbstractFunction elements (FE) are vital components of nanochannel-systems for artificially regulating ion transport. Conventionally, the FE at inner wall (FEIW) of nanochannel−systems are of concern owing to their recognized effect on the compression of ionic passageways. However, their properties are inexplicit or generally presumed from the properties of the FE at outer surface (FEOS), which will bring potential errors. Here, we show that the FEOS independently regulate ion transport in a nanochannel−system without FEIW. The numerical simulations, assigned the measured parameters of FEOS to the Poisson and Nernst-Planck (PNP) equations, are well fitted with the experiments, indicating the generally explicit regulating-ion-transport accomplished by FEOS without FEIW. Meanwhile, the FEOS fulfill the key features of the pervious nanochannel systems on regulating-ion-transport in osmotic energy conversion devices and biosensors, and show advantages to (1) promote power density through concentrating FE at outer surface, bringing increase of ionic selectivity but no obvious change in internal resistance; (2) accommodate probes or targets with size beyond the diameter of nanochannels. Nanochannel-systems with only FEOS of explicit properties provide a quantitative platform for studying substrate transport phenomena through nanoconfined space, including nanopores, nanochannels, nanopipettes, porous membranes and two-dimensional channels.

scholarly journals Estimating Contact Force Chains Using Artificial Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6278
Author(s):  
Mengmeng Wu ◽  
Jianfeng Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Particle Size ◽  
Granular Materials ◽  
Coordination Number ◽  
Contact Force ◽  
Force Chains ◽  
Granular System ◽  
Biaxial Test ◽  
Artificial Neural

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.

The Best Perturbation Method for the Parameter Inversion of Two-Dimension Convection-Diffusion Equation

2013 ◽  
Vol 380-384 ◽  
pp. 1143-1146
Author(s):  
Xiang Guo Liu
Keyword(s):  
Inverse Problem ◽  
Diffusion Equation ◽  
Perturbation Method ◽  
Numerical Simulations ◽  
Numerical Solution ◽  
Two Dimensional ◽  
Convection Diffusion ◽  
Convection Diffusion Equation ◽  
Parameter Inversion ◽  
Parametric Inversion

The paper researches the parametric inversion of the two-dimensional convection-diffusion equation by means of best perturbation method, draw a Numerical Solution for such inverse problem. It is shown by numerical simulations that the method is feasible and effective.

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