Discrete element model (DEM) simulations of cone penetration test (CPT) measurements and soil classification

2020 ◽  
Vol 57 (9) ◽  
pp. 1369-1387 ◽  
Author(s):  
A. Khosravi ◽  
A. Martinez ◽  
J.T. DeJong
Keyword(s):  
Soil Classification ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Interparticle Contact ◽  
Cone Penetration ◽  
Dem Simulations ◽  
Tip Resistance ◽  
Contact Parameters ◽  
Insight Into

This paper presents a study on the simulation of cone penetration tests (CPTs) using the discrete element model (DEM) method. This study’s main objective is to investigate the effect of different modeling parameters and simulation configurations on the ability of three-dimensional DEM simulations to replicate realistic CPT tip resistance (qc) and friction sleeve shear stress (fs) measurements. The CPT tests were simulated in virtual calibration chambers (VCCs) containing particles calibrated to model the behavior of sand. The parameters investigated included the granular assembly properties, interparticle contact parameters, particle–probe interface characteristics, and simulation configuration. Results indicate that the interparticle contact parameters, boundary conditions, and void ratio have an important role in the tip resistance and friction sleeve measurements obtained from the simulations. Particle-level interactions such as particle displacements and rotations and interparticle contact forces were analyzed throughout to provide insight into the differences in measured CPT response. Interpretation of the qc and fs measurements using soil behavior type (SBT) charts for soil classification indicates that the simulated CPT response is representative of the response of coarse-grained soils measured during field soundings. Analysis of results within the SBT framework can provide insight into the influence of soil particle properties on CPT-based soil classification.

The Importance of the Mean Elevation in Predicting Skin Friction for Flow Over Closely Packed Surface Roughness

2005 ◽  
Vol 128 (3) ◽  
pp. 579-586 ◽  
Author(s):  
Stephen T. McClain ◽  
S. Patrick Collins ◽  
B. Keith Hodge ◽  
Jeffrey P. Bons
Keyword(s):  
Surface Roughness ◽  
Skin Friction ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Roughness Model ◽  
The Mean ◽  
Insight Into ◽  
Single Relation ◽  
Grain Roughness

The discrete-element surface roughness model is used to provide insight into the importance of the mean elevation of surface roughness in predicting skin friction over rough surfaces. Comparison of experimental data and extensive computational results using the discrete-element model confirm that the appropriate surface for the imposition of the no-slip condition is the mean elevation of the surface roughness. Additionally, the use of the mean elevation in the Sigal-Danberg approach relating their parameter to the equivalent sand-grain roughness height results in replacing three different piecewise expressions with a single relation. The appropriate mean elevation for closely-packed spherical roughness is also examined.

scholarly journals Calibration and Verification Test of Lily Bulb Simulation Parameters Based on Discrete Element Method

2021 ◽  
Vol 11 (22) ◽  
pp. 10749
Author(s):  
Zhenwei Dai ◽  
Mingliang Wu ◽  
Zhichao Fang ◽  
Yongbo Qu
Keyword(s):  
Friction Coefficient ◽  
Static Friction ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Recovery Coefficient ◽  
Q235 Steel ◽  
Dynamic Friction Coefficient ◽  
Contact Parameters ◽  
Lily Bulb

In the simulation analysis of the lily harvesting process, the intrinsic parameters of the lily bulb and the contact parameters between the lily bulb and the lily mechanized harvesting equipment (Q235 steel) are deficient. Thus, the three-axis size, density, moisture content, Poisson’s ratio, elastic modulus, and other parameters of lily bulbs are measured in this paper with lily bulbs as the research object. Moreover, the discrete element model of the lily bulb was established using 3D scanning technology. The contact parameters between the lily bulb and Q235 steel were calibrated through bench test and simulation parameter test. The relative error between the measured value of the lily bulb accumulation angle and the simulated value is taken as response value to calibrate three parameters (collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulbs). A regression model of the relative error of the stacking angle and three parameters is established, and the response surface is optimized. The results demonstrate that collision recovery coefficient, static friction coefficient, and dynamic friction coefficient between lily bulb and Q235 steel are 0.301, 0.423, and 0.063, respectively; these coefficients between lily bulbs are 0.455, 0.425, and 0.158, respectively. Additionally, a better combination of parameters is adopted to perform the simulation stacking test. The measured stacking angle is 32.31°, which is 0.34% in error with the stacking angle obtained by the physical stacking test. The test results suggest that the discrete element model and contact parameters of the lily bulb can be used in the discrete element simulation test. Furthermore, these research results could provide references for simulation tests, such as mechanized harvesting and post-harvest processing, of lily.

scholarly journals Discrete element model for general polyhedra

2021 ◽  
Author(s):  
Alfredo Gay Neto ◽  
Peter Wriggers
Keyword(s):  
Frictional Contact ◽  
Virtual Work ◽  
Particle Surface ◽  
Element Model ◽  
Discrete Element ◽  
Discrete Element Model ◽  
Principle Of Virtual Work ◽  
Dynamics Model ◽  
Railway Ballast ◽  
Multibody Dynamics Model

AbstractWe present a version of the Discrete Element Method considering the particles as rigid polyhedra. The Principle of Virtual Work is employed as basis for a multibody dynamics model. Each particle surface is split into sub-regions, which are tracked for contact with other sub-regions of neighboring particles. Contact interactions are modeled pointwise, considering vertex-face, edge-edge, vertex-edge and vertex-vertex interactions. General polyhedra with triangular faces are considered as particles, permitting multiple pointwise interactions which are automatically detected along the model evolution. We propose a combined interface law composed of a penalty and a barrier approach, to fulfill the contact constraints. Numerical examples demonstrate that the model can handle normal and frictional contact effects in a robust manner. These include simulations of convex and non-convex particles, showing the potential of applicability to materials with complex shaped particles such as sand and railway ballast.

scholarly journals MFIX-Exa: A path toward exascale CFD-DEM simulations

2021 ◽  
pp. 109434202110092
Author(s):  
Jordan Musser ◽  
Ann S Almgren ◽  
William D Fullmer ◽  
Oscar Antepara ◽  
John B Bell ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Element Model ◽  
Discrete Element Model ◽  
Software Framework ◽  
Chemical Reactors ◽  
Software Infrastructure ◽  
Fundamental Physics ◽  
Dem Simulations ◽  
Algorithmic Approaches

MFIX-Exa is a computational fluid dynamics–discrete element model (CFD-DEM) code designed to run efficiently on current and next-generation supercomputing architectures. MFIX-Exa combines the CFD-DEM expertise embodied in the MFIX code—which was developed at NETL and is used widely in academia and industry—with the modern software framework, AMReX, developed at LBNL. The fundamental physics models follow those of the original MFIX, but the combination of new algorithmic approaches and a new software infrastructure will enable MFIX-Exa to leverage future exascale machines to optimize the modeling and design of multiphase chemical reactors.

Study on Machining Mechanism of Glass Using Discrete Element Method

2014 ◽  
Vol 577 ◽  
pp. 108-111 ◽  
Author(s):  
Ying Qiu ◽  
Mei Lin Gu ◽  
Feng Guang Zhang ◽  
Zhi Wei
Keyword(s):  
Discrete Element Method ◽  
Element Model ◽  
Discrete Element ◽  
Rake Angle ◽  
Milling Process ◽  
Micro Milling ◽  
Discrete Element Model ◽  
Machined Surface ◽  
Damage Depth ◽  
Element Method

The discrete element method (DEM) is applied to glass micromachining in this study. By three standard tests the discrete element model is established to match the main mechanical properties of glass. Then, indentating, cutting, micro milling process are simulated. Results show that the vertical damage depth is prevented from reaching the final machined surface in cutting process. Tool rake angle is the most remarkable factor influencing on the chip deformation and cutting force. The final machined surface is determined by the minimum cutting thickness per edge. Different cutting thickness, cutter shape and spindle speed largely effect on the mechanism of glass.

A Bayesian unsupervised learning approach for identifying soil stratification using cone penetration data

2019 ◽  
Vol 56 (8) ◽  
pp. 1184-1205 ◽  
Author(s):  
Hui Wang ◽  
Xiangrong Wang ◽  
J. Florian Wellmann ◽  
Robert Y. Liang
Keyword(s):  
Computational Cost ◽  
Physical Space ◽  
Soil Classification ◽  
Feature Space ◽  
Soil Behavior ◽  
Cone Penetration ◽  
Soil Layers ◽  
Tip Resistance ◽  
Input Dataset ◽  
Spatial Configurations

This paper presents a novel perspective to understanding the spatial and statistical patterns of a cone penetration dataset and identifying soil stratification using these patterns. Both local consistency in physical space (i.e., along depth) and statistical similarity in feature space (i.e., logQt–logFrspace, where Qtis the normalized tip resistance and Fris the normalized friction ratio, or the Robertson chart) between data points are considered simultaneously. The proposed approach, in essence, consists of two parts: (i) a pattern detection approach using the Bayesian inferential framework and (ii) a pattern interpretation protocol using the Robertson chart. The first part is the mathematical core of the proposed approach, which infers both spatial pattern in physical space and statistical pattern in feature space from the input dataset; the second part converts the abstract patterns into intuitive spatial configurations of multiple soil layers having different soil behavior types. The advantages of the proposed approach include probabilistic soil classification and identification of soil stratification in an automatic and fully unsupervised manner. The proposed approach has been implemented in MATLAB R2015b and Python 3.6, and tested using various datasets including both synthetic and real-world cone penetration test soundings. The results show that the proposed approach can accurately and automatically detect soil layers with quantified uncertainty and reasonable computational cost.

Reduced Rough-Surface Parametrization for Use With the Discrete-Element Model

2009 ◽  
Vol 131 (2) ◽  
Author(s):  
Stephen T. McClain ◽  
Jason M. Brown
Keyword(s):  
Heat Transfer ◽  
Rough Surface ◽  
Surface Characterization ◽  
Rough Surfaces ◽  
Roughness Element ◽  
Three Dimensional ◽  
Element Model ◽  
Discrete Element ◽  
Diameter Distribution ◽  
Discrete Element Model

The discrete-element model for flows over rough surfaces was recently modified to predict drag and heat transfer for flow over randomly rough surfaces. However, the current form of the discrete-element model requires a blockage fraction and a roughness-element diameter distribution as a function of height to predict the drag and heat transfer of flow over a randomly rough surface. The requirement for a roughness-element diameter distribution at each height from the reference elevation has hindered the usefulness of the discrete-element model and inhibited its incorporation into a computational fluid dynamics (CFD) solver. To incorporate the discrete-element model into a CFD solver and to enable the discrete-element model to become a more useful engineering tool, the randomly rough surface characterization must be simplified. Methods for determining characteristic diameters for drag and heat transfer using complete three-dimensional surface measurements are presented. Drag and heat transfer predictions made using the model simplifications are compared to predictions made using the complete surface characterization and to experimental measurements for two randomly rough surfaces. Methods to use statistical surface information, as opposed to the complete three-dimensional surface measurements, to evaluate the characteristic dimensions of the roughness are also explored.

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