Discrete Element Method Model Research Articles

Crack evolution mechanism of stratified rock mass under different strength ratios and soft layer thickness: Insights from DEM modeling

Research on stratified rock masses, which are common geological formations, has primarily focused on their mechanical properties, while studies on crack evolution and microscopic damage mechanisms remain limited. This study addresses this gap by investigating the combined effects of strength ratios and soft layer thicknesses on the microcrack evolution mechanism of stratified rocks using the discrete element method (DEM). Through FISH language programming in the particle flow code (PFC), this study reveals the acoustic emission (AE) characteristics, crack initiation and propagation, damage degree, and final failure characteristics. The key findings are: (1) Higher strength ratios between the hard and soft components of stratified rocks make specimens more sensitive to increases in soft layer thickness. (2) Three types of AE events were identified: continuous active, intermittent active, and silent. (3) Cracks initiate at the interface between components and propagate along the interface into the rock matrix. The strength ratios determine the crack propagation path and the damage extent of the components. (4) The failure of stratified rocks is primarily controlled by the soft component. Crack connections typically form vertical and sub-vertical tensile failure planes in the hard component, and a shear failure surface with a “V”-shaped intersection in the soft component.

Micro-Scale Fracture Characteristics of Emulsified Asphalt Cold Recycled Mixture Based on Discrete Element Method

Asphalt pavement often experiences structural failure due to repeated vehicle loading. The discrete element method (DEM) model was established based on the semicircle bending test (SCB) to investigate the fracture damage mechanism of emulsified asphalt cold recycled mixture (CRME) under loading. The micro-mechanical parameters of CRME were determined through a reliable validation process using the uniaxial compression static creep test. The microscopic fracture characteristics of CRME were investigated through the load-displacement curve, stress distribution, and force chain distribution. The fracture energy was used as the evaluation index to analyze the influence of prefabricated notch length and aggregate gradation on the fracture performance of CRME. The results indicate that the emulsified asphalt mortar-aggregate interface was the critical weak position of the mixture fracture; the failure of the tension chain was the main destructional form of the SCB test. The development of cracks affected the stress concentration phenomenon and stress concentration level of the mixture. Fine-grained mixture exhibited crack resistance. The number and length of cracks were affected by gradation. As the prefabricated notch length increased, the influence gradually diminished. The research results could provide theoretical and data support for the design of CRME.

Adaptive AI-based surrogate modelling via transfer learning for DEM simulation of multi-component segregation

Segregation of granular materials is a critical challenge in many industries, often aimed at being controlled or minimised. The discrete element method (DEM) offers valuable insights into this phenomenon. However, calibrating DEM models is a crucial, albeit time-consuming, step. Recently, using machine learning (ML)-based surrogate models (SMs) in the calibration process has emerged as a promising solution. Nevertheless, developing such SMs is challenging due to the high number of DEM simulations required for training. Additionally, choosing a suitable ML model is not trivial. This study aims to develop SMs that effectively link particle-particle and particle-wall DEM interaction parameters to segregation of a multi-component mixture. We evaluate several ML models, ranging from artificial neural networks to ensemble learning, that are trained on a very cost-effective dataset, employing Bayesian optimisation with cross-validation to tune their hyperparameters. Next, we introduce a novel transfer learning (TL)-based approach that leverages knowledge from a few scenarios to handle new “unseen” ones. This method enables the construction of adaptive SMs for unseen scenarios, such as a new initial configuration (IC) of granular mixtures, without the need for a full-sized dataset. Our findings indicate that Gaussian process regression (GPR) efficiently builds accurate SMs on a very small dataset. We also demonstrate that only a few samples are required to build an accurate SM for the unseen IC, which significantly reduces the data preparation burden. By incorporating one and five samples from unseen scenarios to update the TL-GPR-based surrogate model, the SM’s performance (based on \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{R}^{2}$$\\end{document}) on unseen scenarios improves by 17 and 47%, respectively. The insights and methodology presented in this study will facilitate and accelerate the development of accurate SMs for DEM calibration, assisting in developing reliable DEM models in a shorter timeframe.

Discrete Element Modeling of Irregular-Shaped Particles Using an Automatic Generation Algorithm-Based Multi-Sphere Model

Within the Discrete Element Method (DEM) framework, the multi-sphere model capable of creating irregular-shaped particles by several sub-spheres attracts increasing attention. Traditionally, the creation of irregular-shaped particles using the multi-sphere model requires manual determination of the position and diameter of each sub-sphere, which extremely increases the difficulty and workload of DEM modeling of irregular-shaped particles. To tackle this issue, a new automatic generation algorithm that helps to create arbitrary-shaped multi-sphere particles automatically was developed in this study. The quality of created multi-sphere particles can be easily controlled by adjusting the number of sub-spheres and size factor referred to as the size ratio of the smallest sub-sphere to the largest sub-sphere. To verify the effectiveness of the newly developed automatic generation algorithm, two verification tests were conducted in this study: (i) The packing behavior of irregular-shaped particles in a rectangular container and (ii) the mechanical behavior of irregular-shaped particles under the uniaxial compression. The fill heights of the simulations were in line with those of the experiments with an error of less than 8%. The mechanical behavior of irregular-shaped particles under the uniaxial compression can also be well reproduced. Besides, the influence of important parameters on simulation accuracy was also investigated. The results demonstrated that the newly developed automatic generation algorithm can accurately create complex-shaped particles with a small number of sub-spheres. Increasing the number of sub-spheres will improve the simulation accuracy but at the expense of computational efficiency. A larger size factor leads to higher computational efficiency in DEM simulation but is accompanied by a significant loss of accuracy.

DEM analysis of heat generation and transfer during granular flow in a rotating drum

In many granular material handling processes, heat generation, and subsequent temperature rise, due to contacts between particles and surfaces is common. Aiming to explore this phenomenon, friction-induced heat generation and heat transfer models are developed and implemented into a Discrete Element Method (DEM) in this paper. The DEM models are validated qualitatively for the temperature distribution pattern and quantitatively for the evolution of particle temperature rise using data obtained experimentally. Moreover, the temperature distribution of the drum end plates obtained in the simulations shows the same annulus pattern observed in experiments. It is also found that increasing the rotation speed of the drum leads to an increase in the annular heated region of drum and in the net heat that particles obtain. In addition, a higher rotation speed leads to higher absolute fluctuations and lower relative variability of particle temperature.

Role of particle shape in sheared granular media: Roundness and elongation

Particle shape is an intrinsic characteristic of soil particles that significantly influences mechanical responses. In this investigation, a meticulously calibrated and validated two-dimensional discrete element method (DEM) model of a biaxial shearing test was employed to simulate the shearing response of forty distinct particle shapes. The systematic evolution of particle roundness (R) and aspect ratio (AR) was achieved by utilizing idealized polygonal-shaped particles, aiming to comprehend their effects on the macro and micromechanical behaviors of granular materials. The results suggest that a reduction in R limits free rotations and enhances interlocking, thereby promoting relatively stable force transmission between particles and leading to a monotonic increase in shear strength. However, this effect diminishes as particles become more elongated. Conversely, a decrease in AR from 1.0 (increased elongation) constrains particle rotations, increases the coordination number, and enhances fabric anisotropy initially resulting in increased overall shear strength, reaching a maximum before exhibiting a decreasing trend, indicative of non-monotonic variation. For high elongations, notable fabric anisotropy impedes clear force transmission between particles thus facilitating interparticle sliding and overall strength diminishes. The extent to which AR impacts depends on the angularity feature of particles. Finally, a nonlinear equation has been proposed to predict the variation in critical state shear strength of granular samples, based on the R and AR values of the constituent particles.

An optimization DEM modelling method for soil-fibre undrained cyclic loading tests model based on the influence range of fibre

The computational cost of discrete element modelling is high owing to the limitations of particle size and contact in fibre modelling. This paper proposes an optimised discrete element method (DEM) for a hybrid model of soil and fibres based on the fibre influence range. First, a relative velocity state function is established based on the relative motion state between the fibres and soil particles under undrained cyclic loading. Subsequently, the influence range of the fibres is determined using the relative velocity function based on the first few cycles of the undrained cyclic loading numerical tests. Cluster and clump models of the fibre are then generated based on the influence range of the fibre. Finally, a symmetrical shape of the optimised model is developed by extracting the distribution length of the edge curve of the influence range along the vertical direction of the axis. In this study, the proposed optimised DEM was validated through a series of undrained cyclic loading numerical tests on fibre-reinforced soil. The results of the optimised model were highly consistent with those of the traditional model, and the computational time was significantly reduced. The cyclic loading timing for determining the range of influence of the fibre was analysed. The optimised model based on the influence range of the 15th cycles not only restored almost the same results but also saved the calculation cost by nearly eight times. The optimised model established based on the influence range after the 15th cycles had a slight influence on the results. In addition, the applicability of the optimised model is discussed. This paper provides new insights into the establishment of a hybrid model of soil and fibres.

Accelerating Laser Powder Bed Fusion: The Influence of Roller-Spreading Speed on Powder Spreading Performance

The powder spreading process is a fundamental element within the laser powder bed fusion (PBF-LP) framework given its pivotal role in configuring the powder bed. This configuration significantly influences subsequent processing steps and ultimately determines the quality of the final manufactured part. This research paper presents a comprehensive analysis of the impacts of varying spreading speeds, which are enabled by different roller configurations, on powder distribution in PBF-LP. By utilizing extensive Discrete Element Method (DEM) modelling, we systematically examine how spreading speed affects vital parameters within the spreading process, including packing density, mass fraction, and actual layer thickness. Our exploration of various roller configurations has revealed that increasing spreading speed generally decreases packing density and layer thickness for non-rotating, counter-rotating, and forward-rotating rollers with low clockwise rotational speeds (sub-rolling) due to powder dragging. However, a forward-rotating roller with a high clockwise rotational speed (super-rolling) balances momentum transfer, enhancing packing density and layer thickness while increasing surface roughness. This configuration significantly improves the uniformity and density of the powder bed, providing a technique to accelerate the spreading process while maintaining and not reducing packing density. Furthermore, this configuration offers crucial insights into optimizing additive manufacturing processes by considering the complex relationships between spreading speed, roller configuration, and powder spreading quality.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 2. Validation of calibration workflow

This paper is the second in a series of two that describes the application of discrete element method (DEM) and reduced order modeling to predict the effect of disturbances in the concentration of drug substance at the inlet of a continuous powder mixer on the concentration of the drug substance at the outlet of the mixer. In the companion publication, small-scale material characterization tests, a careful DEM parameter calibration and DEM simulations of the manufacturing process were used to develop a reliable RTD models. In the current work, the same calibration workflow was employed to evaluate the predictive ability of the resulting reduced-order model for an extended design space. DEM simulations were extrapolated using a relay race method and the cumulative RTD was accurately parameterized using the n-CSTR model. By performing experiments and simulations, a calibrated DEM model predicted the response of a continuous powder mixer to step changes in the inlet concentration of an API. Thus, carefully calibrated DEM models was used to guide and reduce experimental work and to establish an adequate control strategy. In addition, a further reduction in the computational effort was obtained by using the relay race method to extrapolate results. The predicted RTD curves were then parameterized to develop reduced order models and used to simulate the process in a matter of seconds. Overall, a control strategy evaluation tool based on high-fidelity DEM simulations was developed using material-sparing small-scale characterization tests.

Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression process. Part 1. Calibration workflow

In this work, a high-fidelity digital twin was developed to support the design and testing of control strategies for drug product manufacturing via direct compression. The high-fidelity digital twin platform was based on typical pharmaceutical equipment, materials, and direct compression continuous processes. The paper describes in detail the material characterization, the Discrete Element Method (DEM) model and the DEM model parameter calibration approach and provides a comparison of the system’s response to the experimental results for stepwise changes in the API concentration at the mixer inlet. A calibration method for a cohesive DEM contact model parameter estimation was introduced. To assure a correct prediction for a wide range of processes, the calibration approach contained four characterization experiments using different stress states and different measurement principles, namely the bulk density test, compression with elastic recovery, the shear cell, and the rotating drum. To demonstrate the sensitivity of the DEM contact parameters to the process response, two powder characterization data sets with different powder flowability were applied. The results showed that the calibration method could differentiate between the different material batches of the same blend and that small-scale material characterization tests could be used to predict the residence time distribution in a continuous manufacturing process.

Simulation of the Full‐Process Dynamics of Floating Vehicles Driven by Flash Floods

AbstractFlash flooding has become more prominent under climate change, threatening people's life and property. Post‐event investigations of recent events emphasize the role of floating debris, such as vehicles, in exacerbating damage. Few modeling methods and tools have been developed to simulate the full‐process dynamics of floating debris driven by large‐scale flood waves in real world. In this work, a fully coupled model is developed for simulating the full‐process interactive movements of vehicles driven by flash flood hydrodynamics, from entrainment, transport to deposition. The proposed coupled modeling system consists of a finite volume shock‐capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. The proposed two‐way coupling approach estimates the hydrostatic and hydrodynamic forces acting on solid objects using the water depth and velocity predicted by the hydrodynamic model; the resulting counter forces on the fluid flow are then considered by adding extra source terms in the hydrodynamic model. A multi‐sphere method is further embedded in the DEM model to better represent vehicle shapes. New calculation modules are further implemented to represent the vehicle entrainment, contact and stopping motions. The coupled model is applied to reproduce a flash flood event hit Boscastle in the UK in 2004. Over 100 vehicles were moved and carried downstream by the highly transient flood flow. The model well predicts the hydrodynamics, interactive transport process and the final locations of vehicles. The proposed coupled model provides a new tool for simulating large‐scale flash flooding processes, including debris dynamics.

Coupled Modeling of Computational Fluid Dynamics and Granular Mechanics of Sand Production in Multiple Fluid Flow

Summary Sand production is a significant issue in oil and gas fields with poorly consolidated formations, often involving the multiphase flow of reservoir fluids and solid particles. The multiscale mechanisms of sand production, particularly fluid flow and particle movement, remain poorly understood. This study investigates these mechanisms using a coupled computational fluid dynamics and discrete element method (CFD-DEM) modeling approach. Single and multiple fluid flows of water and heavy oil were simulated with increasing fluid injection velocities, leading to different sand production patterns. The simulation results were compared with experimental results from a large cylindrical specimen of weak artificial sandstone under similar loading conditions. The multiphase conditions created various localized flow and deformation patterns that influenced both fluid and solid production, resulting in shorter transient sand production periods. Microstructures and phenomena such as fingering and water coning were observed, associated with a critical flow rate below which oil displacement was uniform and no water breakthrough occurred. Higher fluid injection velocities and fluid viscosities resulted in greater drag forces, leading to progressive damage zones and explaining the occurrence of single or multiple staged sand production events. The evolution of the microscopic granular structure was visualized under the effect of transient sand production.

Impact of rubber membrane on lunar regolith mechanical properties under low effective confining pressure

The existing lunar exploration activities and associated equipment interactions are limited to the surface environment, where the stress state of the lunar regolith is significantly lower than that in laboratory tests conducted on Earth. To address this, this paper proposes a new framework for discrete modeling of large-scale triaxial tests on lunar regolith under low confining pressure. The framework incorporates particle shapes from the Chang’E-5 mission (CE-5) and flexible boundary conditions. Firstly, the shape characteristics of the lunar regolith particles were adopted in the Discrete Element Method (DEM) model to reproduce the mechanical properties of the lunar regolith as accurately as possible. Then, experiments with varying membrane particle stiffness ratios were conducted to explore the effect of the rubber membrane’s properties on the mechanical characteristics of lunar regolith under low effective confining pressure. Topological Data Analysis (TDA) tools from persistent homology were utilized to quantify the dynamic response of particles during the onset and development of strain localization. The results indicate that under low effective confining pressure, selecting appropriate rubber membrane types is crucial for accurately determining the mechanical properties of lunar regolith.

DEM modeling of granular soils reinforced by disposable face-mask chips

The use of disposable masks, especially in the wake of the COVID-19 pandemic, has led to an increased focus on sustainable disposal and recycling methods. This study investigates the feasibility of integrating shredded mask materials into granular soils to enhance their mechanical properties for engineering applications. After validating the selection of Discrete Element Method (DEM) contact parameters through the conducted physical experiments, a comprehensive series of DEM simulations was performed to explore the effects of various mask contents on the mechanical behavior of sand-mask chip mixtures (e.g., soil’s strength and dilatancy) under different confining pressures. Additionally, the study analyzed the potential of mask chips in improving soil fabric, reducing contact force concentrations under shearing, and contributing to the soil’s stability. The results suggest that waste face masks could serve as a valuable resource for soil stabilization. This not only provides a viable solution for mask waste management but also introduces a novel, eco-friendly material that could improve the engineering properties of granular soils. The findings underscore the importance of understanding the interaction between waste masks and soil and open new pathways for the recycling of non-biodegradable waste in geotechnical applications.

Development of a methodology for prediction and calibration of parameters of DEM simulations based on machine learning

This research work aims to develop a robust methodology for the global estimation of interaction parameters based on machine learning, applicable to the discrete element method (DEM) in the study of granular materials. The specific objectives include establishing a theoretical framework that relates the interaction micro-parameters with the macro-parameters and the physical behaviour of the material; performing DEM simulations for different material parameters; developing a machine learning-based model for global parameter estimation; and consolidating the methodology for obtaining micro-parameters for a given material and behaviour. The methodology will be applied specifically to the case of dry copper ore, evaluating its limitations and the possibility of extension to materials with other characteristics. This approach does not consider direct experimental tests, but focuses on the characterisation of the relationship between the input parameters of the material and its response through simulations, validating the response and sensitivity of the model in its different stages. The methodology is expected to allow the systematic estimation of interaction properties for a DEM model, considering micro-parameter duplicities and their global selection, aspects little addressed in the literature. The final verification includes mechanisms and key questions that facilitate future modifications and improvements, allowing its application to materials of different characteristics beyond the specific case study.

From Darcy to turbulent flow: Investigating flow characteristics and regime transitions in porous media

This research addresses the flow characteristics within a porous medium composed of a monolayer of closely packed spheres, spanning from viscous-dominated to turbulent flow regimes. In the first part of this paper, the turbulent flow characteristics at a 30 MPa pressure drop within the domain are presented. The results are averaged across different cross sections between the inlet and outlet. In the second part of the study, simulations are conducted with pressure drops, ranging from nearly 0 to 100 MPa. The analysis finds distinct flow patterns within the domain and provides estimations for the permeability and the inertial term coefficient. Moreover, the transition from Darcy to non-Darcy and turbulent flow is achieved through the use of different criteria. The specified geometry is suitable for validating and calibrating simplified discrete element method models coupled with computational fluid dynamics. The main goal of this research is to produce a reliable benchmark to figure out the challenge of limited experimental data concerning fluid flow characteristics in densely packed granules specially subjected to high pressure conditions. To do this, representative specimens are designed, accurate simulations are conducted, and precise assessments of the results are carried out.

Study of the Sliding Friction Coefficient of Different-Size Elements in Discrete Element Method Based on an Experimental Method

The materials involved in geotechnical engineering are objects of concern in granular mechanics. In order to study the influence of the sliding friction coefficient corresponding to different-sized elements in the discrete element method (DEM) on the simulation results, we establish a two-dimensional DEM model based on the experimental method to analyze a slope example. The correctness of the DEM model is verified by comparing the sliding surface of a finite element method (FEM) model and the DEM slope model. A sliding friction coefficient algorithm based on the experimental method is embedded into the DEM slope model and compared with the original model. The comparison results show that embedding the DEM model into the sliding friction coefficient algorithm leads to an increase in displacement. The reason for this is that the contact information between elements of different sizes has changed, but the displacement trend is the same. Different sliding friction coefficients should be set based on different-sized elements in the DEM, as they can improve simulation accuracy.

A Simplified Calibration Procedure for DEM Simulations of Granular Material Flow

The discrete element method (DEM) has emerged as an essential computational tool in geotechnical engineering for the simulation of granular materials, offering significant advantages over traditional continuum-based methods such as the finite element method (FEM) and the finite difference method (FDM). The DEM’s ability to model particle-level interactions, including contact forces, rotations, and particle breakage, allows for a more precise understanding of granular media behavior under various loading conditions. However, accurate DEM simulations require meticulous calibration of input parameters, such as particle density, stiffness, and friction, to effectively replicate real-world behavior. This study proposes a simplified calibration procedure, intended to be conducted prior to any granular material flow DEM modeling, based on three fundamental physical tests: bulk density, surface friction, and angle of repose. The ability of these tests, conducted on dry quartz sand, to accurately determine DEM micromechanical parameters, was validated through numerical simulation of cylinder tests with varying height-to-radius ratios. The results demonstrated that this calibration approach effectively reduced computational complexity while maintaining high accuracy, with validation errors of 0% to 12%. This research underscores the efficacy of simplified DEM calibration methods in enhancing the predictive reliability of simulations, particularly for sand modeling in geotechnical applications.

Calibration of Discrete Element Method Parameters for a High-Fidelity Lunar Regolith Simulant Considering the Effects of Realistic Particle Shape

The Discrete Element Method (DEM) is an important tool for investigating the geotechnical properties of lunar regolith. The accuracy of DEM simulations largely depends on precise particle modeling and the appropriate selection of mesoscopic parameters. To enhance the reliability and accuracy of the DEM in lunar regolith studies, this paper utilized the high-fidelity IRSM-1 lunar regolith simulant to construct a DEM model with realistic particle shapes and conducted an angle of repose (AoR) simulation test. The optimal DEM parameters were calibrated using a combination of the Plackett–Burman test, steepest ascent test, and Box–Behnken design. The results indicate that the sliding friction coefficient, rolling friction coefficient, and surface energy significantly influence the simulation AoR. By optimizing against the measured AoR using a second-order regression model, the optimal parameter values were determined to be 0.633, 0.401, and 0.2, respectively. Under these optimal parameters, the error between the simulation and experimental AoR was 2.1%. Finally, the calibrated mesoscopic parameters were validated through a lifting cylinder test, showing an error of 6.3% between the simulation and experimental results. The high similarity in the shape of the AoR further confirms the accuracy and reliability of the parameter calibration method. This study provides a valuable reference for future DEM-based research on the mechanical and engineering properties of lunar regolith.

Process-based reconstruction of digital rock based on discrete element method considering thermal-mechanical coupling effect and actual particle shape

The digital rock is a crucial platform for numerical simulations of pore-scale flow in various fields such as geo-energy, carbon (CO2) sequestration, and hydrogen (H2) storage. Under these conditions, rocks deform, and pore structures change due to temperature and stress effects. However, existing methods for reconstructing digital rocks, such as physical experimental, stochastic simulation, and machine learning, can not consider the influence of high temperature and stress. Therefore, a process-based reconstruction method based on the discrete element method (DEM) considering the thermal-mechanical coupling effect is proposed in this paper. Initially, the computed tomography (CT) images are segmented based on the watershed algorithm, and a contour database is established using the spherical harmonic analysis method. A clump template library is then constructed in PFC3D (Particle Flow Code in 3 Dimensions). Subsequently, the DEM model is established using clumps from the template library according to porosity and particle radius distribution, and the model's accuracy is evaluated based on two-point and linear path correlation functions. Micro-mechanical and thermal parameters between particles are calibrated, and different temperature and stress boundary conditions are applied to obtain digital rocks under varying conditions. Finally, digital rocks' geometric and topological structures under different conditions are analyzed, and permeability and relative permeability are calculated. Using Bentheim sandstone as an example, digital rocks are constructed under various temperature and stress conditions. The research findings indicate that high temperatures and stress result in decreased pore and throat radii, elongated throats, deteriorated connectivity, reduced porosity, and permeability, making the digital rocks more water-wet. This study provides theoretical guidance for the accurate pore-scale flow simulation of geo-energy fluids, CO2, and H2.