Search Results (835)

In order to tackle the issues of poor mobility and unstable traction of electric vehicles on sandy landscapes, this research develops a high-accuracy numerical model for wheel–sand interaction relying on the Discrete Element Method (DEM). An innovative parameter calibration procedure is proposed herein, which optimizes the sand contact parameters. This reduces the error between the simulated and measured angles of repose to merely 1.2% and substantially improves the model’s reliability. The model was then used to systematically compare the performance of a 205/55 R16 slick tire with a treaded tire on sand. Simulations demonstrate that at a 30% slip ratio, the treaded tire exhibited significantly higher traction and greater sinkage than the slick tire. This indicates that tread patterns enhance traction mechanically by increasing the contact area and promoting shear deformation of the sand. The trends of traction with slip ratio and the corresponding sand flow patterns showed excellent agreement with experimental observations, which validated the simulation approach. This research provides an efficient and accurate tool for evaluating tire-sand interaction, providing critical support for the design and control of electric vehicles on complex terrains. Full article

Overcoming high draft forces has long been a primary challenge in conventional subsoiling. To better utilize this agronomically advantageous technique, it is necessary to substantially reduce the draft. Inspired by the digging behaviors of fossorial animals, a low-draft up-cutting subsoiling method was proposed in this study. Discrete element method (DEM) simulations were employed to study the draft-reducing performance of up-cutting tools compared with regular tools. The results showed that the up-cutting motion reduced the draft by 63.07%, 63.84%, and 58.92%, respectively, at rake angles of 45°, 60°, and 75%, and by 79.73%, 63.84%, and 45.22%, respectively, at advancement velocities of 0.5 m·s⁻¹, 1 m·s⁻¹, and 1.5 m·s⁻¹. An increase in up-cutting velocity reduces the draft. Soil disturbance, particle velocity distribution, and soil deformation and movement patterns change in ways that contribute to this reduction. The draft-reducing performance of a chain subsoiler developed based on the principle of soil-breaking by animal digging was verified using field tests, exhibiting a draft-reduction amplitude approaching or greater than 30%. This study shows the great application potential of the up-cutting method in reducing subsoiling drafts and provides a theoretical basis for future research. Full article

Tool edge preparation is the process aimed at eliminating edge defects and optimizing the micro-geometric parameters of cutting tools. Drag finishing, the primary engineering method, subjects tools to planetary motion (simultaneous revolution and rotation) within abrasive media to remove burrs and micro-chips, thereby improving cutting performance and extending tool life. A discrete element method (DEM) model of drag finishing edge preparation was developed to investigate the effects of processing time, tool rotational speed, and rotation direction on abrasive-mediated tool wear behavior. The model was validated through milling cutter edge preparation experiments. Simulation results show that increasing the processing time causes fluctuating changes in average abrasive velocity and contact forces, while cumulative energy and tool wear increase progressively. Elevating tool rotational speed increases average abrasive velocity, contact forces, cumulative energy, and tool wear. Rotation direction significantly impacts tool wear: after 2 s of clockwise (CW) rotation, wear reached 1.45 × 10⁻⁸ mm; after 1 s of CW followed by 1 s of counterclockwise (CCW) rotation, wear was 1.25 × 10⁻⁸ mm; and after 2 s of CCW rotation, wear decreased to 1.02 × 10⁻⁸ mm. Experiments, designed based on simulation trends, confirm that edge radius increases with time and tool rotational speed. After 30 min of processing at 60, 90, and 120 rpm, average edge radius increased to 22.5 μm, 28 μm, and 30 μm, respectively. CW rotation increased the edge shape factor K, while CCW rotation decreased it. The close agreement between experimental and simulation results confirms the model’s effectiveness in predicting the impact of edge preparation parameters on tool geometry. Rotational speed control optimizes edge preparation efficiency, the predominant tangential cumulative energy reveals abrasive wear as the primary material removal mechanism, and rotation direction modulates the shape factor K, enabling symmetric edge preparation. Full article

Wet agglomeration is essential in heap leaching of minerals, as it improves permeability by forming agglomerates through capillary and viscous forces. The Discrete Element Method (DEM) has been used to model this phenomenon, enabling the detailed tracking of interactions between individual particles. This study employs DEM to analyze the effect of particle-size distribution (PSD) on agglomerate formation inside a rotating agglomeration drum. The DEM model was validated using geometry and parameters reported in the literature, which are based on experimental studies of agglomeration in rotating drums. Both wide and bimodal PSD cases were simulated. The results demonstrate that DEM simulations of drums with exclusively fine particles are prone to producing poorly defined macrostructures. In contrast, the presence of coarse particles promotes the formation of stable agglomerates with fine particles attached to them. Additionally, decreasing the maximum particle size increases the number of agglomerates and improves the homogeneity of the final PSD. These findings improve our understanding of wet agglomeration dynamics and provide practical criteria for optimizing feed design in mineral-processing applications. Full article

Controlled demolition is a critical engineering practice that enables the safe and efficient dismantling of structures while minimizing risks to the surrounding environment. This study presents, for the first time, a detailed, structured framework for understanding the fundamental principles of controlled demolition by outlining key procedures, methodologies, and directions for future research. Through original, carefully designed charts and full-scale numerical simulations, including two 23-story building scenarios with different delay and blasting sequences, this paper provides real-life insights into the effects of floor-to-floor versus axis-by-axis delays on structural collapse behavior, debris spread, and toppling control. Beyond traditional techniques, this study explores how emerging technologies, such as real-time structural monitoring via object tracking, LiDAR scanning, and Unmanned Aerial Vehicle (UAV)-based inspections, can be further advanced through the integration of artificial intelligence (AI). The potential Deep learning (DL) and Machine learning (ML)-based applications of tools like Convolutional Neural Network (CNN)-based digital twins, YOLO object detection, and XGBoost classifiers are highlighted as promising avenues for future research. These technologies could support real-time decision-making, automation, and risk assessment in demolition scenarios. Furthermore, vision-language models such as SAM and Grounding DINO are discussed as enabling technologies for real-time risk assessment, anomaly detection, and adaptive control. By sharing insights from full-scale observations and proposing a forward-looking analytical framework, this work lays a foundation for intelligent and resilient demolition practices. Full article

The discrete element method (DEM) is widely employed in various fields for analyzing rock and soil movement. However, the traditional DEM involves a large number of calculations, which leads to reduced computational efficiency. Deep-learning presents a promising solution to this issue by utilizing neural networks to approximate DEM calculations. Moreover, the consistency between the arrangement of discrete particles and the structure presented in graph neural networks further reinforces the validity of this approach. In this study, we propose a novel model called Graph-DEM based on graph neural networks, which significantly enhances the speed of DEM calculations. Meanwhile, our model demonstrates the capability of adaptive learning across various constitutive relationships. To evaluate the model’s performance, we measure particle-trajectory prediction accuracy on three scenario datasets (dynamic, static, and principle experiments) and on two public datasets. In addition, the computational efficiency of the Graph-DEM model are compared against the traditional DEM. The experimental results demonstrate the superiority of the model in terms of accuracy, universality, and computational efficiency. Full article

The behavior of a cantilever beam equipped with a particle damper, subjected to impact loads at various locations, was investigated using the discrete element method (DEM). The flexible cantilever steel beam and the particle damper attached to the beam’s tip were modeled with bonded particles through DEM. Computational simulations were conducted to explore the influence of different particle damper porosities and positions along the beam’s length. It was observed that reducing the particle damper’s porosity decreases the beam’s displacement. The impact force was significantly influenced by the porosity, where having lower porosities resulted in higher impact forces. In addition, the time intervals between sub-impacts were also affected by the damper’s porosity, showing a reduction as the porosity of the damper decreases. The unique type of particle damper used in this study contained sand grains as fillers and was capable of pressurizing the sand within its housing. This feature was utilized to investigate the effect of different initial pressures on the beam’s response. It was revealed that an increase in initial pressure reduces the beam’s displacement. Based on the results obtained, the optimal location for the particle damper was determined to be at the point where displacement reduction is required. Full article

The presence of discontinuities in fractured reservoirs, their mechanical and physical characteristics, and fluid flow through them are important factors influencing their effective large-scale properties. In this paper, the variation of elastic moduli in a block measuring 100 × 100 × 100 m³ that hosts a discrete fracture network (DFN) is evaluated using the discrete element method (DEM). Fractures are characterised by (1) constant, (2) interlocked, and (3) mismatched stiffness properties. First, three uniaxial verification tests were performed on a block (1 × 1 × 2 m³) containing a circular finite fracture (diameter = 0.5 m) to validate the developed numerical algorithm that implements the three fracture stiffnesses mentioned above. The validated algorithms were generalised to fractures in a DFN embedded in a 100 × 100 × 100 m³ rock block that reproduces in situ conditions at various depths (4.7 km, 2.3 km, and 0.5 km) of the Soultz-sous-Forêts geothermal site. The effective elastic moduli of this large-scale rock mass were then numerically evaluated through a triaxial loading scenario by comparing to the numerically evaluated stress field using the DFN, with the stress field computed using an effective homogeneous elastic block. Based on the results obtained, we evaluate the influence of fracture interaction and stress perturbation around fractures on the effective elastic moduli and subsequently on the large-scale P-wave velocity. The numerical results differ from the elastic moduli of the rock matrix at higher fracture densities, unlike the other methods. Additionally, the effect of nonlinear fracture stiffness is reduced by increasing the depth or stress level in both the numerical and semi-analytical methods. Full article

The mixing process of powder materials determines the final quality of industrial products. This study employs the Discrete Element Method (DEM) to numerically characterize the effects of particle shape and mixer structure on mixing performance. Using the superquadratic equation, nine types of particles with regular shape variations are constructed, and mixing models are further simulated. The feasibility of superquadratic-generated particles is validated through a classic drum calibration experiment. To investigate the intrinsic mechanisms of particle shape effects, the motion and contact behaviors of particles are quantified by the diffusion index, proportion of rotational kinetic energy, interparticle compressive force, and contact number. Meanwhile, to examine geometry effects, three supplementary mixing simulations are conducted by varying the plow angle and deactivating the choppers. The results show that Cubic particles exhibited poor mixing performance, while disk-shaped particles outperformed cylindrical ones; Increasing the plow blade inclination angle enhanced particle convection and diffusion, whereas excessively small angles may fail to achieve homogeneous mixing; The auxiliary shear of chopper blades promoted particle diffusion, effectively overcoming dead zones between plow blade intervals. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

Compared with a traditional distribution transformer with silicon steel sheet as the core material, the no-load loss of an amorphous alloy transformer is greatly reduced due to its core using iron-based amorphous metal material, which has been applied in many countries. However, due to the brittleness of its amorphous strip, an amorphous alloy transformer is prone to debris in the process of production, transportation and work. The charge and migration characteristics of these debris will reduce the insulation strength of the transformer oil and endanger the safe operation of the transformer. In this paper, a charge measurement platform of amorphous alloy debris is set up, and the charging characteristics of amorphous alloy core debris under different flow velocities, particle radius and plate electric field strength are obtained. The results show that with an increase in pipeline flow velocity, the charge-to-mass ratio of the debris increases first and then decreases. With an increase in electric field strength, the charge-to-mass ratio of the debris increases; with an increase in the number of debris, the charge-to-mass ratio of the debris decreases; with an increase in debris size, the charge-to-mass ratio of the debris increases. The debris with different charge-to-mass ratios and types obtained from the above experiments are added to the simulation model of an amorphous alloy transformer. The lattice Boltzmann method (LBM) coupled with the discrete element method (DEM) is used to simulate the migration process of metal particles in an amorphous alloy transformer under the combined action of gravity, buoyancy, electric field force and oil flow resistance under electrothermal excitation boundary. The results show that the trajectory of the debris is related to the initial position, electric field strength and oil flow velocity. The LBM–DEM calculation model and charge measurement platform proposed in this paper can provide a reference for studying the charge mechanism and migration characteristics of amorphous alloy core debris in insulating oil. Full article

Rainfall is a key triggering factor for numerous geotechnical hazards. Hence, it is necessary to investigate the degradation characteristics of rock–soil strength under different water contents. The existing macro–mesoscopic analysis methods for rock–soil strength degradation neglect the intrinsic connection between water content variations caused by external rainfall and mesoscopic mechanical mechanisms. In addition, there is a lack of discrete element method (DEM) mesoscopic parameter calibration methods for rock–soil strength under the influence of external environmental factors. Hence, this study aims to perform a macro–mesoscopic analysis and develop a parameter calibration model for the degradation of rock–soil strength under different water contents. First, the mesoscopic mechanical characteristics under different water contents are investigated by analyzing particle displacement, the bond failure rate, and the anisotropy coefficient. Interrelationships among shear strength, water content, and mesoscopic parameters are qualitatively analyzed, which indicated a macro–mesoscopic synergistic mechanism. A macro–meso-environment data set is constructed. Key mesoscopic parameters are determined using Pearson correlation (Pearson) and mutual information (MI) methods. Then, the mapping relationships are established based on ordinary least squares. The model accuracy is verified by comparing the calibrated simulation results with direct shear test results. The results show that the shear strength increases with vertical pressure under a constant water content. However, as the water content varies, the strength initially increases and then decreases. The average displacement of central particles and bond failure rate both decrease initially and then increase with rising water content, while the anisotropy coefficients show the opposite trend. Normal bond strength, tangential bond strength, and friction coefficient are determined as the key parameters. The goodness-of-fit R² of the parameter calibration model exceeds 0.92. Among 45 validation working conditions, only two are found to have errors of 12.4% and 13.6%, and the remainder have errors below 5%. Full article

To evaluate the feasibility of a virtual overlay tester (OT), a modeling approach was proposed based on the discrete element method (DEM). Simulations were conducted on three types of asphalt mixtures across three different thickness conditions. Through the analysis of the load/displacement curves, crack propagation paths, force chains, and contact force characteristics, it was observed that the peak loads decrease with increasing thicknesses, indicating a notable size effect. The complexity of the crack path was positively correlated with the particle size along the path and the fractal dimension. Coarse aggregates can inhibit crack propagation to some extent. Prior to reaching the peak load, compressive force chains in asphalt concrete-13 (AC13) and large stone porous asphalt mixture-30 (LSPM30) exhibited a symmetrical and divergent distribution along the crack, while tensile force chains formed an arch-like pattern. After the peak load, compressive force chains were symmetrically distributed in an arch shape along the crack. In stone mastic asphalt-13 (SMA13), compressive forces were transmitted along coarse aggregates, forming several continuous vertical paths. The proportion of strong compressive force chains to total compressive force chains across the three gradations ranged from 0.74 to 0.83, while the corresponding proportion for tensile force chains ranged from 0.72 to 0.78. Full article

Soil–rock mixtures pose significant challenges in mountainous regions due to their complex flow behavior and destructive potential during landslides and debris flows. Despite growing interest in using baffle arrays as protective measures, current research has focused on idealized soil or rock materials, leaving a notable gap in understanding their efficacy against heterogeneous soil–rock mixtures under varied slope and baffle configurations. This study employs the Material Point Method to simulate the continuum behavior of the soil matrix, while the Discrete Element Method (DEM) models the discrete dynamics of rock boulders. By incorporating Spheropolygon DEM, the model accurately captures complex soil–rock structure interactions. Parametric simulations are conducted to evaluate the effects of baffle location and slope angle on flow kinematics, impact forces, and energy dissipation. Results show that baffles placed closer to the structure significantly reduce downstream impact forces and kinetic energy by enhancing energy dissipation. Steeper slope angles result in increased impact forces on the structure due to greater conversion of potential energy to kinetic energy. The findings provide quantitative insights into optimizing baffle placement for improving infrastructure resilience against soil–rock mixture flows. Full article

The roller press crushing of ore is a complex process involving the interplay of multiple factors. Roller dimensions, gap settings, and rotational speed all influence this process, which in turn affects the comprehensive crushing performance of the high-pressure grinding rolls (HPGR). Therefore, to simultaneously enhance the HPGR’s size reduction effectiveness (SRE) and throughput while controlling its energy consumption, wear, and edge effect, multi-objective parameter optimization of the HPGR is required. This study utilizes the Discrete Element Method (DEM) to simulate ore comminution within an HPGR. By first dividing the release zone into segments, the particle size distribution of the crushed product at different locations within this zone is investigated. Then, the influence of various factors on the SRE at different locations within HPGR is examined through single-factor experiments. Subsequently, the relative influence of roller diameter, roller width, roller speed, and roll gap on the comprehensive crushing performance of the HPGR is determined through signal-to-noise ratio (SNR) analysis and analysis of variance (ANOVA). Finally, multi-objective parameter optimization of the roller press crushing is conducted based on grey relational analysis (GRA), incorporating the weights assigned to different response target. The results indicate that the proportion of unbroken ore particles is relatively significant, primarily due to the edge effect. Further analysis reveals that along the horizontal diameter of the rollers, regions closer to the roller surface exhibit better SRE. Additionally, roller speed is identified as the most influential factor affecting the uniformity of SRE in the HPGR. The application of GRA to the multi-objective optimization of roller press crushing enables effective balancing of the comprehensive crushing performance in HPGR. Full article