Enhancement of GJK-EPA Algorithm for Improved Contact Detection in Discrete Element Method Simulations

The numerical modelling of particulate processes in environmental science increasingly requires an ability to represent the properties of individual natural particles. Considerable advances have been made in discontinuum modelling using spheres to represent particles. In this paper, we discuss recent developments that illustrate a way forward for tackling the complexity of realistically shaped bodies such as those exhibited by rock fragments. To address the validation of such approaches, we present a comparison of cube-packing experiments and their equivalent numerical simulation. Sensitivity to initial conditions, highlighted for non-spherical bodies, enters the discussion of problems with validation of numerical simulation. The algorithmic details behind these advances in modelling large systems of realistically shaped particles are summarized in our companion paper in this volume.

Discrete element method simulations of Mars Exploration Rover wheel performance

Given the lack of accurate parameter values in discrete element simulation of sweet potato seedling mechanized recovery, a direct measurement and virtual calibration method was used to study the Discrete Element Method (DEM) simulation parameters of broken sweet potato stems and leaves. The intrinsic parameters, collision recovery coefficient and static friction coefficient of broken seedlings were acquired by physical experiments. Different parameter combinations were designed for DEM simulation. The intrinsic parameters of sweet potato leaves and other unmeasurable DEM simulation parameters were determined by stacking angle optimization simulation. Plackett-Burman test shows that the static friction coefficients of stem-stem and stem-steel and the rolling friction coefficients of stem-stem and stem-leaf all significantly influence the accumulation angle. The parameters that significantly influence the stacking angle of broken seedlings were sent to steepest ascent test and Box-Behnken test. The average stacking angle of 40.51 and the relative error of 0.972% indicate it feasible to calibrate DEM parameters by physical test and optimization simulation and the calibrated parameters can be used in DEM simulation of broken sweet potato stems and leaves.

Discrete element modelling and experimental validation for the falling process of dry granular steps

Experimental validation study of 3D direct simple shear DEM simulations

Contact based void partitioning to assess filtration properties in DEM simulations

Granular rheology and phase transition: DEM simulations and order-parameter based constitutive model

Modelling and simulation of fruit drop tests by discrete element method

SummaryIn the discrete element method (DEM) simulation for wear prediction, structural boundary is now represented extensively by triangular meshes with high resolution, which brings a huge computational cost. A DEM‐based method for predicting the wear evolution of structural boundary has been developed for computational efficiency. The structural boundary subjected to wear is represented by the spherical boundary elements in the DEM simulation in combination with the inside triangles and fitting curved surface in wear prediction. Wear prediction is performed through a series of evolution steps. In each evolution step, the collision energies by particles at structural boundary are collected via the DEM simulation and assigned to the boundary elements. Then, the volume losses of structural boundary are predicted across each relevant boundary element. Finally, the new geometry of structural boundary in response to wear is described by moving the boundary elements along the depths of wear individually. Through converting the contact detection between structural boundary and particles into between spherical boundary elements and particles, our method greatly reduces the computational cost in the DEM simulation. Through two numerical tests, our method has been verified to be an efficient and accurate method for the wear prediction of structural boundaries with different resolutions.

In this study, the discrete element method (DEM) simulations were employed to assess the asphalt mixture’s splitting strength to investigate the mechanical aspects of asphalt mixtures under various loading conditions, including the stress transmission characteristics of individual aggregate and the contribution or response to the load-bearing capacity. The study involved an analysis of splitting tests on fine-graded AC-13 and medium-graded AC-20 asphalt mixtures at different temperatures. The mechanical responses of aggregates at different sizes were examined, and the relationships between aggregate loading contribution and mechanical interactions were explored by combining laboratory experiments with DEM simulations. The research shows that the load-displacement patterns in DEM simulations and lab experiments are very similar. This demonstrates that DEM simulations are applicable for conducting splitting tests on asphalt mixtures and the results are valid. The splitting strength decreases with increasing temperature under identical gradation conditions. At the constant temperature level, splitting strength is enhanced with large-sized aggregates/gradations in asphalt mixtures. As temperature rises, smaller-sized aggregates in both gradations bear a greater loading proportion, while the load-bearing ratio of intermediate-sized aggregates decreases. Comparing the two gradations of asphalt mixtures, it is evident that, at the same temperature, an increase in the gradation leads to a gradual increase in the load-bearing ratio of aggregates in the intermediate size range. In the case of the AC-20 gradation asphalt mixture, the largest-sized aggregates also exhibit an increasing load-bearing ratio compared to the AC-13 gradation, indicating that larger gradations result in a greater proportion of coarse aggregates carrying the loading. These research findings provide valuable insights and references for the cracking resistance performance of the asphalt pavement.

Dynamic coefficient of friction and granular drag force in dense particle flows: Experiments and DEM simulations

This paper aims to bridge the soil properties and the soil response in the discrete element method (DEM) simulation using the artificial neural network (ANN). The network was designed to output the stress-strain-volumetric response from inputting the soil properties. 31 biaxial shearing tests with varying soil parameters were generated using the DEM simulations. Based on these 31 training samples, a three-layer neural network was established. 2 extra samples were generated to examine the validity of the network, and the predicted curves using the ANN were well matched with those from the DEM simulations. Overall, the ANN was found promising in effectively modelling the soil behaviour.

Packing of cylindrical particles: DEM simulations and experimental measurements

Unwanted grain commingling impedes new quality-based grain handling systems and has proven to be an expensive and time-consuming issue to study experimentally. Experimentally validated models may reduce the time and expense of studying grain commingling while providing additional insight into details of the particle flow. In this study, grain commingling in a pilot-scale bucket elevator boot was first modeled in three-dimensional (3-D) discrete element method (DEM) simulations. Experiments on the pilot-scale boot were performed using red-dyed and clear (uncolored) soybeans to validate the 3-D DEM model. Predicted results from the 3-D boot model generally followed the experimental data but tended to underpredict commingling early in the process. To reduce computational time, quasi-two-dimensional (quasi-2-D) DEM simulations were also evaluated. Comparison of predicted average commingling of five quasi-2-D boot models with reduced control volumes (i.e., with boot widths from four to seven times the mean particle diameter) led to the selection of the quasi-2-D model with a boot width of 5.6 times the mean particle diameter (i.e., 5.6d) to reduce computational time. In addition, the 3-D and quasi-2-D (5.6d) models were refined by accounting for the initial surge of particles at the beginning of each test and correcting for the effective dynamic gap between the bucket cups and the boot wall. The quasi-2-D (5.6d) models reduced simulation run time by approximately 70% compared to the 3-D model of the pilot-scale boot. Results of this study can be used to accurately predict commingling levels and improve grain handling, which can help farmers and grain handlers reduce costs and maintain grain purity during transport and export of grain.

On the adhesive JKR contact and rolling models for reduced particle stiffness discrete element simulations