3D multiscale modeling of strain localization in granular media

Guo, Ning; Zhao, Jidong

doi:10.1016/j.compgeo.2016.01.020

Cited by 103 publications

(55 citation statements)

References 45 publications

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“…The rotation averaging method presented in Guo and Zhao [15] and Zhao and Guo [41], which is similar to the methods used in many other literatures [13,20,21], is applied on the DEM models above for comparison. Their method to obtain the accumulated average particle rotation θ for a RVE packing is defined as: where N p is the number of particles, θ p is the accumulated rotation of an individual particle.…”

Section: Comparison With Another Methodsmentioning

confidence: 99%

A general rotation averaging method for granular materials

Lin

2017

Granular Matter

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In order to study the rotational behavior of granular materials, rotation averaging methods are required. Most of the existing rotation averaging methods are only for 2D cases and limited by the shape, position and size of the averaging volumes. Hence, a general rotation averaging method is proposed in this paper. Our approach is simple yet works for both 2D and 3D cases with very little restriction. The performance of this method is shown by both hypothetical examples and DEM simulations of plane strain tests with different boundary conditions.

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Section: Comparison With Another Methodsmentioning

confidence: 99%

A general rotation averaging method for granular materials

Lin

2017

Granular Matter

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“…The micromechanical force and contact networks calculated with DEM are typically averaged over the repeated representative volumes to extract macroscopic variables such as stress and fabric tensors [5]. Alternatively, these packings can be embedded at the Gauss integration points of a FEM mesh to derive the local material responses in a hierarchical FEM×DEM multiscale model [15,30]. It is well known that the microstructure of a granular material (e.g., coordination number and anisotropy [28,29]) plays a key role in the macroscopic constitutive behavior.…”

Section: Scale and Resolution Of Dem Granular Packingsmentioning

confidence: 99%

“…From just a few micromechanical parameters, DEM can provide comprehensive cross-scale insights [2,6,15,42] that are difficult to obtain in either stateof-the-art experiments or sophisticated continuum models. However, automated and systematic calibration of these parameters against macroscopic experimental measurements is still lacking, and often takes significant effort from DEM analysts.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

et al. 2018

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The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress-strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes' rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro-macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.

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“…Later works have enhanced and extended this approach to the study of anisotropy [7,18], granular cohesion [19], material heterogeneity [25], real scale engineering applications [18,8], more realistic constitutive behaviors using 3D DEM [12,26], macroscale hydro-mechanical coupling [26,10]. More recently, [12] have embedded non-local regularization at the macro-scale and [9] has developed a full micromacro 3D approach.…”

Section: Introductionmentioning

confidence: 99%

Restoring Mesh Independency in FEM-DEM Multi-scale Modelling of Strain Localization Using Second Gradient Regularization

Desrues

Argilaga

Pont

et al. 2017

Springer Series in Geomechanics and Geoengineering

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Continuum media from classical mechanics cannot appropriately reproduce the evolution of materials exhibiting strong heterogeneities in the strain field, e.g. strain localization. Models without a microscale representation cannot properly reproduce the microscale mechanisms that trigger the strain localization, in addition, first gradient relations don't present any length parameter in the formulation. This results in a model without a characteristic length that cannot exhibit any objective band width. In this paper, techniques to introduce an internal length will be enumerated. Microstuctured materials will be retained and in particular Second Gradient model will be exposed and used along with a FEMxDEM approach. Numerical results showing the abilities of the enriched model will conclude the text.

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3D multiscale modeling of strain localization in granular media

Cited by 103 publications

References 45 publications

A general rotation averaging method for granular materials

A general rotation averaging method for granular materials

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

Restoring Mesh Independency in FEM-DEM Multi-scale Modelling of Strain Localization Using Second Gradient Regularization

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