Application Of Discrete Element Method Research Articles

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.

A Review of Discrete Element Method Applications in Soil–Plant Interactions: Challenges and Opportunities

The discrete-element method (DEM) has become a pivotal tool for investigating soil–plant interactions in agricultural and environmental engineering. This review examines recent advancements in DEM applications, focusing on both the challenges and opportunities that shape future research in this field. This paper first explores the effectiveness of DEM in simulating soil and plant materials, including seeds, roots, and residues, highlighting its role in understanding interactions that affect agricultural practices. Challenges such as long computation times and the complexity of determining accurate contact parameters are discussed, alongside emerging methods like machine learning that offer potential solutions. Notable advancements include the integration of machine learning algorithms for contact parameter estimation, the use of expanded particle models for dynamic processes, and the development of new techniques for detailed post-processing of DEM simulations. The review also identifies key future research directions, including the incorporation of environmental factors such as air and water, and the exploration of residue management for carbon storage and erosion prevention. By addressing these challenges and seizing these opportunities, future research can enhance the accuracy and applicability of DEM models, advancing our understanding of soil–plant interactions and contributing to more sustainable agricultural and environmental practices.

Application of discrete element method (DEM) in pharmaceutical process of solid traditional Chinese medicine preparations

In recent years, the application of numerical simulation in the research and development(R&D) as well as the pharmaceutical processes of new drugs has expanded considerably. The discrete element method(DEM), an important approach among numerical simulation methods, offers an effective tool for the simulation of discontinuous media. Referring to the research progress of DEM and the formulation of solid traditional Chinese medicine(TCM) preparations in recent years, this paper summarizes and analyzes the application of DEM in the pharmaceutical processes of solid TCM preparations, and discusses the challenges of its application in these processes, in order to provide new methods and ideas for promoting the high-quality production of TCM preparations.

A Multi-Time-Step Parallel Computing Method based on Overlapping Particles for DEM

The application of the discrete element method (DEM) to continuous medium problems is becoming increasingly widespread. In this work, a parallel computing method with multiple time steps based on overlapping particles is proposed. The domain decomposition method (DDM) with overlapping particles method is used to increase the speed up and shorten the computation time and meet the consistency requirements during data transmission. The multi-time-step method (MTSM) is adopted to tackle the matching of asynchronous step boundary. Data are exchanged at the boundaries in each subdomain with a message passing interface (MPI). The computational efficiency of different step ratios in both serial and parallel computing is studied respectively. Numerical examples show that the DEM can effectively handle large structure deformation problems, and provides a shorter calculation time than that of the finite element method (FEM). The DEM with multiple time steps in different subdomains effectively reduces the computation time than that with a single time step in the entire domain. Under fixed step ratio conditions, using parallel computing can save more time than serial computing. This work develops ideas for expanding the application of DEM for large engineering problems.

Analysis of cohesive particles mixing behavior in a twin-paddle blender: DEM and machine learning applications

This research paper presents a comprehensive discrete element method (DEM) examination of the mixing behaviors exhibited by cohesive particles within a twin-paddle blender. A comparative analysis between the simulation and experimental results revealed a relative error of 3.47%, demonstrating a strong agreement between the results from the experimental tests and the DEM simulation. The main focus centers on systematically exploring how operational parameters, such as impeller rotational speed, blender's fill level, and particle mass ratio, influence the process. The investigation also illustrates the significant influence of the mixing time on the mixing quality. To gain a deeper understanding of the DEM simulation findings, an analytical tool called multivariate polynomial regression in machine learning is employed. This method uncovers significant connections between the DEM results and the operational parameters, providing a more comprehensive insight into their interrelationships. The multivariate polynomial regression model exhibited robust predictive performance, with a mean absolute percentage error of less than 3% for both the training and validation sets, indicating a slight deviation from actual values. The model's precision was confirmed by low mean absolute error values of 0.0144 (80% of the dataset in the training set) and 0.0183 (20% of the dataset in the validation set). The study offers valuable insights into granular mixing behaviors, with implications for enhancing the efficiency and predictability of the mixing processes in various industrial applications.

Fast and precise DEM parameter calibration for Cucurbita ficifolia seeds

The lack of discrete element method (DEM) models and calibration parameters for Cucurbita ficifolia seeds, as well as low accuracy and efficiency of common parameters calibration methods, hinder the application of DEM for computer simulation in air-suction directional seeding equipment. In this study, the DEM parameters of the seeds were calibrated. The angle of repose (AOR), intrinsic parameters, and partial contact parameters of the seeds were experimentally measured. The seed 3D models were reconstructed based on the three-view profile information. The parameters and their value ranges were filtered through the Plackett–Burman design and steepest ascent test. The response surface method (RSM) and machine learning were utilised for optimisation inversion of the parameters. The experiments showed that the geometric relative error of the seed model was 0.69–6.54%, which meets the modelling requirements for DEM. The seed–seed static friction coefficient, the seed–seed and the seed–PVC rolling friction coefficient were 0.341, 0.026, and 0.059, respectively, which were obtained by inverting the GA-BP regression model via the Genetic Algorithm. The simulated AOR was 26.64°, with a relative error compared to the actual AOR of 1.64%, which was better than the simulated AOR obtained by RSM optimisation. The greater the smoothing value setting in EDEM software, the less the particle filling, resulting in improved simulation efficiency but reduced model accuracy. The CPU + GPU(CUDA) solver showed high DEM solution efficiency. The results reveal that the method can be used to quickly and accurately construct a 3D model of the seed, and the parameter optimisation accuracy of GA-BP-GA is better than that of RSM.

CFD-DEM Simulation of Fast Fluidization of Fine Particles in a Micro Riser

In recent years, the discrete element method (DEM) has gradually been applied to the traditional fluidization simulation of fine particles in a micro fluidized bed (MFB). The application of DEM in the simulating fast fluidization of fine particles in MFB has not yet received attention. This article presents a drag model that relies on the surrounding environment of particles, namely the particle circumstance-dependent drag model or PCDD model. Fast fluidization in an MFB of fine particles is simulated using DEM based on the PCDD model. Simulations indicate that the local structure in an MFB exhibits particle aggregation, which is a natural property of fast fluidization, forming a structure where a continuous dilute phase and dispersed concentrated phase coexist. There exists a strong effect of solid back-mixing in an MFB, leading to relatively low outlet solid flux. The gas back-mixing effect is, however, not so distinct. The axial porosity shows a monotonically increasing distribution with the bed height but does not strictly follow the single exponential distribution. The solid volume fraction at the bottom of the bed is significantly lower than the correlated value in CFB. The axial heterogeneous distribution of the cross-sectional average porosity in the lower half of the bed is also weakened. The radial porosity shows a higher distribution pattern in the central region and a lower one in the sidewall region.

Modeling Soil–Plant–Machine Dynamics Using Discrete Element Method: A Review

The study of soil–plant–machine interaction (SPMI) examines the system dynamics at the interface of soil, machine, and plant materials, primarily consisting of soil–machine, soil–plant, and plant–machine interactions. A thorough understanding of the mechanisms and behaviors of SPMI systems is of paramount importance to optimal design and operation of high-performance agricultural machinery. The discrete element method (DEM) is a promising numerical method that can simulate dynamic behaviors of particle systems at micro levels of individual particles and at macro levels of bulk material. This paper presents a comprehensive review of the fundamental studies and applications of DEM in SPMI systems, which is of general interest to machinery systems and computational methods communities. Important concepts of DEM including working principles, calibration methods, and implementation are introduced first to help readers gain a basic understanding of the emerging numerical method. The fundamental aspects of DEM modeling including the study of contact model and model parameters are surveyed. An extensive review of the applications of DEM in tillage, seeding, planting, fertilizing, and harvesting operations is presented. Relevant methodologies used and major findings of the literature review are synthesized to serve as references for similar research. The future scope of coupling DEM with other computational methods and virtual rapid prototyping and their applications in agriculture is narrated. Finally, challenges such as computational efficiency and uncertainty in modeling are highlighted. We conclude that DEM is an effective method for simulating soil and plant dynamics in SPMI systems related to the field of agriculture and food production. However, there are still some aspects that need to be examined in the future.

Micro-macroscopic mechanical behavior of frozen sand based on a large-scale direct shear test

The mechanical properties of frozen soil are of utmost importance for infrastructure development in cold regions, and the microscopic characteristics play a vital role in revealing the mechanism of strength and deformation evolution. In this study, the mechanical behaviors of frozen sand at various negative temperatures are investigated by a self-developed temperature-controlled large-scale direct shear apparatus. The shear stress-shear displacement curve, dilation curve and shear strength characteristics at different negative temperatures and normal pressures are analyzed. According to the test results, as the cooling temperature decreases, the cohesion and internal friction angle of frozen sandy sample both increase significantly, and the shear dilatation tends to be larger. The brittle behavior of frozen sand becomes more obvious at lower temperatures. Then, a discrete element method (DEM) model is established to investigate the microscopic mechanical properties of frozen sand in the direct shear test. The particle movement, force chain evolution, and shear surface development during the shearing process are analyzed. The simulations of the DEM model can well explain the microscopic mechanism of the macroscopic strength and deformation law of frozen sandy soil. It is hoped that the research results can provide a reference for the DEM application in frozen soil mechanics.

Review of Discrete Element Method Simulations of Soil Tillage and Furrow Opening

In agricultural machinery design and optimization, the discrete element method (DEM) has played a major role due to its ability to speed up the design and manufacturing process by reducing multiple prototyping, testing, and evaluation under experimental conditions. In the field of soil dynamics, DEM has been mainly applied in the design and optimization of soil-engaging tools, especially tillage tools and furrow openers. This numerical method is able to capture the dynamic and bulk behaviour of soils and soil–tool interactions. This review focused on the various aspects of the application of DEM in the simulation of tillage and furrow opening for tool design optimization. Different contact models, particle sizes and shapes, and calibration techniques for determining input parameters for tillage and furrow opening research have been reviewed. Discrete element method predictions of furrow profiles, disturbed soil surface profiles, soil failure, loosening, disturbance parameters, reaction forces, and the various types of soils modelled with DEM have also been highlighted. This pool of information consolidates existing working approaches used in prior studies and helps to identify knowledge gaps which, if addressed, will advance the current soil dynamics modelling capability.

Evaluation of particle models of corn kernels for discrete element method simulation of shelled corn mass flow

Bulk handling behavior of grains can be studied experimentally, but large-scale investigations of grain flow especially at the commercial scale are expensive, time consuming, and are therefore limited in the treatment factors that can be evaluated in any one study. Recent research has demonstrated the potential of discrete element method (DEM) in simulating grain flow in handling operations. However, application of DEM for simulating grain flow, requires development of appropriate particle models for each grain type. In this study, particle models comprised of one to four overlapping spheres were developed for shelled corn and tested. With these models, measurement of bulk properties, namely bulk density and angle of repose, both involving bulk flow were simulated using EDEM™ software with published data of material and interaction properties of shelled corn as inputs associated with each particle shape. Predicted time for the complete outflow from the bulk density test hopper (hopper emptying time), designated as simulation time in the study, was also recorded as another discriminant for model selection. Variable inputs into the simulation modeling were material properties particle shape, particle size distribution, Poisson's ratio, shear modulus, and density and interaction properties particle coefficients of restitution, static friction, and rolling friction. Computation time is critical in DEM modeling so single sphere particle models were emphasized over multi-sphere particles in the research even though multi-sphere particles represent the corn kernel shape more precisely because their simplicity can provide markedly reduced computation times to complete simulations. Predicted results for hopper emptying time, bulk density, and angle of repose were compared to experimental results or published data to select the most appropriate particle models for simulating bulk behavior of corn kernels in free-flowing grain applications using DEM. From the study, the most appropriate particle model (particle shape/physical properties combination) for corn kernels involved a single-sphere shape particle shape with a particle coefficient of restitution of 0.30, particle coefficients of static friction of 0.30 for corn-corn contact and 0.20 for corn-steel contact, particle coefficient of rolling friction of 0.05, normal particle size distribution with a standard deviation factor of 0.4, and particle shear modulus of 20 MPa.

A Hybrid PBM-DEM Model of High-Pressure Grinding Rolls Applied to Iron Ore Pellet Feed Pressing

Mathematical models of high-pressure grinding rolls (HPGR) have attracted great attention, owing to their role in optimization of operating machines as well as in the design and selection of new ones. Although population balance models (PBM) and the discrete element method (DEM) have been used in this task, both suffer from important limitations. Whereas PBMs have challenges associated to the prediction of operating gap and to the validity of several of its assumptions in different formulations in the literature, application of DEM has its own challenges, in particular when fed with distributions containing large amounts of fines. This work proposes a hybrid approach in which the coupling of DEM to particle replacement models and multibody dynamics is used to predict operating gap, throughput and power, as well as providing information along the rolls length that is used in PBM to predict the product fineness. The hybrid approach is then compared to both DEM and a PBM (Modified Torres and Casali), demonstrating similar results to the later when applied to simulating a pilot-scale machine operating under different conditions, but improved prediction when applied in scale-up to an industrial-scale HPGR.

A BP neural network-based micro particle parameters calibration and an energy criterion for the application of strength reduction method in MatDEM to evaluate 3D slope stability

To enhance the applicability of discrete element method in 3D slope stability analysis, a BP neural network-based micro parameter calibration method and an energy criterion are proposed by taking MatDEM as an example. Firstly, the relationship between the micro particle parameters and the shear strengths of particle aggregate are represented by using the BP neural network. And then the micro particle parameters are obtained for the given shear strengths by using a correction calibration. Next, the energy conversions are investigated for the stable and instable slope models in MatDEM. From a view of practical application, the abrupt in variation tendency and magnitude of the kinetic energy is selected for indicating the emergence of the limit equilibrium state of a slope. Finally, the effectiveness of the proposed improvements is testified by taking Baijiabao landslide as an example. Results verify that the calibration method established in this study is applicable to provide the micro particle parameters when the shear strength is constantly reduced, and the factor of safety determined by the kinetic energy criterion reflects the landslide stability at the global level.

Suggestion of Practical Application of Discrete Element Method for Long-Term Wear of Metallic Materials

This study presents a simulation procedure for the wear of metallic materials exposed to long-term cumulative contact forces and introduces a numerical analysis procedure using the discrete element method (DEM) to predict the wear damage. Since the DEM can calculate the motion and contact load of each particle and the interaction between particles for each dynamic collision of particles, it was possible to analyze the motion of the particles causing metal wear. A method to reflect particle size, material properties, and long-term cumulative friction distance required by the DEM was proposed so that the collision and friction load between particles can be predicted practically. Considering the feature of wear suggested by Archard, it was shown that the wear amount can be predicted efficiently by converting the long-term load into an equivalent material constant. In addition, it was suggested that it is reasonable to determine the size of the particles in consideration of the size of the surface mesh of the metal surface. The accuracy of the analysis results obtained using the procedure proposed in this study was compared with that of the wear test results of metal material specimens presented by former studies. The numerical analysis was also performed in the reference study, but inaccurate results were derived compared to the analysis results. The reason for the inaccuracy of the numerical model performed in the previous study was found to be environmental factors that cannot be considered in a numerical analysis. In this study, it was determined that it was because the behavior of particles and the load transferred to the specimen were not well simulated, which remains a problem for future research. As a result, it was confirmed that it is possible to compute a worn shape similar to the measured shape of experiments. Thereafter, the change in the contact load predicted by simulation is discussed in terms of wear shape and cross-sectional area loss ratio.

Recent progress on the discrete element method simulations for powder transport systems: A review

• DEM and DEM-CFD are widely used to study powder transport in single- and multi-phase systems. • Recent research interests as well as emerging applications of DEM are summarized. • Coarse grained DEM, GPU technique and periodic boundary conditions enable large-scale simulations. Powder transport systems are ubiquitous in various industries, where they can encounter single powder flow, two-phase flow with solids carried by gas or liquid, and gas–solid–liquid three-phase flow. System geometry, operating conditions, and particle properties have significant impacts on the flow behavior, making it difficult to achieve good transportation of granular materials. Compared to experimental trials and theoretical studies, the numerical approach provides unparalleled advantages over the investigation and prediction of detailed flow behavior, of which the discrete element method (DEM) can precisely capture complex particle-scale information and attract a plethora of research interests. This is the first study to review recent progress in the DEM and coupled DEM with computational fluid dynamics for extensive powder transport systems, including single-particle, gas–solid/solid–liquid, and gas–solid–liquid flows. Some important aspects (i.e., powder electrification during pneumatic conveying, pipe bend erosion, non-spherical particle transport) that have not been well summarized previously are given special attention, as is the application in some new-rising fields (ocean mining, hydraulic fracturing, and gas/oil production). Studies involving important large-scale computation methods, such as the coarse grained DEM, graphical processing unit-based technique, and periodic boundary condition, are also introduced to provide insight for industrial application. This review study conducts a comprehensive survey of the DEM studies in powder transport systems.

Calibration of DEM simulations for dynamic particulate systems

Calibration and validation represent crucial but often-overlooked ingredients in the successful application of discrete element method (DEM) simulations. Without rigorous calibration/validation protocols, the results of DEM simulations can be imprecise or even unphysical, yet all too often the methods used by practitioners are at best cursory, and at worst entirely absent. As the particle-handling industries show an increasing interest in DEM, it is vital that this issue be resolved lest a potentially powerful tool be written off by industry as unreliable. In this work, we provide a concise overview of contemporary methods used in the calibration and validation of DEM simulations of powder flows, providing practical insights into their strengths and weaknesses, and ideas for manners in which they may be improved and/or rendered more easily adoptable in the future.

A Review of the Application of Discrete Element Method in Agricultural Engineering: A Case Study of Soybean

The discrete element method has become a common method for analyzing the contact interaction between particulate materials and between particles and mechanical components. It has been widely used in agricultural engineering and other fields. Taking soybean as an example, soybean seed particles always have contact effects between particles and mechanical components in the process of planting, harvesting, threshing, separation, cleaning, and processing. The discrete element method can be used to obtain information on the contact forces between seed particles and mechanical parts, as well as the velocity and displacement of seed particle motion from a microscopic perspective. This paper summarizes the application of the discrete element method in soybean cultivation and production processes in recent years. This will help future researchers to conduct relevant test studies, develop and improve existing research methods. It can also serve as a guide and reference for the production and processing of other granular materials and the optimization of agricultural machinery components.

Challenges and opportunities in modelling wet granulation in pharmaceutical industry – A critical review

Wet granulation is a key step in the pharmaceutical manufacturing of solid-dosage forms. Wet granulation is used in pharmaceutical production to enhance formulation qualities such as flowability, compressibility, etc. Different methods are used for wet granulation, including fluidised bed, twin-screw extrusion, and high-shear method, leading to different mechanisms and granule properties. Modelling wet granulation is of great importance for the pharmaceutical industry, which is aspiring to shift from batch mode to a continuous one. Moreover, model-based understanding is critical for implementing the Quality-by-Design (QbD) approach in the pharmaceutical industry. The current critical review represents an overview of current approaches in modelling wet granulation. Three major models, including the population balance model (PBM), discrete element method (DEM), and data-driven models, are evaluated, and the advantages of each approach are outlined. The PBM is the primary approach for modelling granulation in which granule properties are tracked in the process. On the other hand, DEM provides a better understanding of granulation at the particle level. Recent DEM modelling studies have significantly advanced the knowledge for design and scale-up of DEM models, such as capturing the suitable wetting physics and particle scaling and thus mitigating the traditionally known challenges in the application of DEM for wet granulation processes. However, further research is required to include particle level effects, like breakage, attrition, into the DEM model through either built-in approaches or hybrid approaches. Data-driven models are fast and accurate but do not investigate the mechanisms involved in granulation. Data-driven models based on neural networks have been indicated to be adopted for application in continuous granulation with great accuracy compared to other approaches, which can also be implemented for process control. A great joint prospect for first-principles and data-driven modelling approaches is anticipated, which can play an important role in future process design, optimisation, and control of pharmaceutical wet granulation processes.

A comprehensive review of the application of DEM in the investigation of batch solid mixers

Abstract Powder mixing is a vital operation in a wide range of industries, such as food, pharmaceutical, and cosmetics. Despite the common use of mixing systems in various industries, often due to the complex nature of mixing systems, the effects of operating and design parameters on the mixers’ performance and final blend are not fully known, and therefore optimal parameters are selected through experience or trial and error. Experimental and numerical techniques have been widely used to analyze mixing systems and to gain a detailed understanding of mixing processes. The limitations associated with experimental techniques, however, have made discrete element method (DEM) a valuable complementary tool to obtain comprehensive particle level information about mixing systems. In the present study, the fundamentals of solid-solid mixing, segregation, and characteristics of different types of batch solid mixers are briefly reviewed. Previously published papers related to the application of DEM in studying mixing quality and assessing the influence of operating and design parameters on the mixing performance of various batch mixing systems are summarized in detail. The challenges with regards to the DEM simulation of mixing systems, the available solutions to address those challenges and our recommendations for future simulations of solid mixing are also presented and discussed.

Aspects of size effect on discrete element modeling of concrete

Abstract Present paper focuses on the modeling of size effect on the compressive strength of normal strength concrete with the application of discrete element method, considering specimen of different concrete mixes and shapes. An equation was derived to estimate the parallel bond strength from the compressive strength. The results showed a good agreement with the literature and the derived estimation models showed strong correlation with the measurements. The results indicated that size effect is stronger on concretes with lower strength class and that it is more significant on cube specimens than on cylinders. The relationship of model size and computational time was analyzed and a method to decrease the computational time (iterations) was proposed.