Discrete Element Method Parameters 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.

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.

Demonstrating scalability between two blender types using DEM

Powder blending is a critical step in pharmaceutical manufacturing that can impact product quality such as tablet tensile strength. This study utilized the Discrete Element Method (DEM) to investigate blending in a 5-liter mini-batch and a 2-liter Turbula blender. DEM parameters were calibrated using small-scale powder characterization tests, so that the particle behavior in the DEM simulations matches the measured behavior. The research explored the effects of blender designs and process conditions on blending and lubricant dispersion. A predictive model for tablet tensile strength was developed. The model takes the lubricant’s dispersion via the lubrication energy into account. The model is then used to predict the tablet tensile strength depending on the chosen process parameters, blending speed, duration, and fill level. DEM simulations enabled scaling between the two blenders, providing valuable insights for a semi-continuous manufacturing process based on mini-batch blending. The findings contribute to a deeper understanding of blending mechanics, offering potential enhancements in pharmaceutical manufacturing efficiency and product consistency

A new DEM calibration method for wet and stick materials based on the BP neural network

The assessment of flow characteristics and the Discrete Element Method (DEM) calibrations for Wet and Sticky Materials (WSMs) are significantly demanded by the mining and transportation industries. However, the angle of repose (AoR), as a common DEM calibration method, has been facing challenges in adopting for WSMs. Due to the relatively high adhesion and cohesion of WSMs, it is difficult to form repeatable material heaps, resulting in inaccurate measurements of the AoRs. Therefore, this study aims to calibrate DEM parameters for WSMs under various conditions, utilizing a modified AoR test method and the Back Propagation-Neural Network (BP-NN). In the modified AoR test method, WSMs are discharged from a funnel using a screw feeder and fall closely against a vertical baffle to form a heap in a constrained direction. The new method combines the features of the traditional AoR test and the slump plane test to accurately measure the AoRs for different WSMs, including wet white sand, bulk coal, and corn powders. Test results are analysed to show the advantage of the modified AoR test. Furthermore, an innovative DEM calibration method based on the BP-NN is developed. By training the BP-NN with the database established according to the relationship between the measured and simulated AoRs, the DEM calibration parameters can be predicted rapidly for WSMs with different particle sizes and moisture contents. Validation is conducted for the wet white sand, from which the deviations and relative mean deviations between the experiment AoRs (Ae) and the calibration AoRs (Ac) are less than 2°and 4 % respectively, indicating the capability of the developed method for WSMs DEM calibrations.

A flexible and efficient calibration method for discrete element simulations of additive manufacturing in construction

Extrusion-based 3D concrete printing (E3DCP), as one of the most advanced Additive Manufacturing processes, is a hotspot in the construction industry. Using the Discrete Element Method (DEM), the E3DCP can be investigated with reduced prototyping- and trial costs. However, DEM parameter calibration is challenging in the accurate simulation of construction materials because they are hardly typical granular matters. In addition, the introduction of a novel extrusion-based process Near-Nozzle-Mixing calls for a more flexible, efficient, and reliable calibration procedure due to the useable wide variety of material models.In this study, DEM simulation parameters of mortar and its components (e.g. Poraver® and paste) were thoroughly calibrated based on the central composite design method. Notably, the specific shape indices of related substrates were identified in the study to achieve the precise calibration of Poraver® and paste. Moreover, the European standard-based Haegermann flow table test was applied in the calibration by quantifying mortar consistency. This work provides a holistic and efficient calibration approach for three typical and physically different construction materials, which can be universally explored in the DEM simulation of most materials in E3DCP.

DEM parameters of slope cultivated purple soil in Southwest China and interaction mechanism between very narrow tine

The establishment of discrete element method (DEM) model for simulating the behavior of viscous soil can significantly contribute to the development of agricultural machinery, particularly in hilly areas worldwide. There is limited research on the parameter calibration of the DEM for purple soil in the Southwest China and its interaction mechanism between very narrow tine. Piling up test, rotating drum test and design-expert 12.0 software were used to design Plackett–Burman test, steepest climb test and Box–Behnken test successively. Combined with soil intrinsic parameter measurement test, soil simulation parameter calibration and optimization were completed. Based on the optimized DEM simulation parameters, the average relative errors of static repose angle and dynamic repose angle were 1.934 and 2.289%, respectively. The accuracy of parameter calibration results of DEM simulation was proved. Three kinds of very narrow tines were designed and processed. The interaction model and mechanism between soil-touch parts and soil was established. The average relative errors between the DEM simulation results of the forward resistance and disturbance area of the three soil-touch parts and the soil bin test results were 7.22% and 11.24%, respectively, which further proved that the calibration results of DEM parameters of purple soil and the interaction model with the soil-touch parts in this study were reliable. The physical and interaction parameters of slope cultivated purple soil in Southwest China were determined based on static-dynamic repose angle, providing more accurate parameters for study of soil-touch parts.

A systematic evaluation of DEM input parameters measurement and calibration

Rotary drums are widely used in industry, however, determining granular dynamics within them is still a complex task, thus remaining a focal point of research over the years. In this context, experimental techniques require numerical support to obtain data that is not easily acquired experimentally. One well-established numerical technique for studying particulate systems is the Lagrangian approach embed with Discrete Element Method (DEM). Although, DEM has limitations related to the definition of input parameters. It is crucial that DEM input parameters be measured and/or calibrated to reliably represent the granular behavior. These parameters are related to particle properties such as density, Young’s modulus, Poisson’s ratio, among others, and particle interactions, such as coefficients of restitution, static friction, and rolling friction. This study explores the experimental determination of interaction parameters preceding the acquisition of DEM input parameters in a rotary drum. Statistical analysis highlights the importance of each parameter and their combined interactions with the static angle of repose. The granular materials used were: soybeans, glass beads, polyacetal, 6 mm steel, and 4 mm steel. The coefficient of restitution was determined via free-fall methodology using equipment that verifies particle trajectory, static friction coefficient through equilibrium between weight and resistance forces, rolling friction coefficient using a launching device on surfaces of different roughness, and static angle of repose in a rectangular box with sections where particles rested. After removing the partition, angles were measured at both top and bottom sections. Regression techniques using a neural network created in Python were employed for both measurements. Experimental investigations and numerical simulations using DEM indicated that the coefficient of restitution depends on the thickness and type of material of the flat surfaces, showing an asymptotic behavior with increasing surface thickness. The static friction coefficient increased on rougher surfaces and in particles with greater imperfections. Rolling friction coefficient also increased on rough surfaces. Analysis of the angle of repose revealed that granular properties influence the response, such as static and rolling friction coefficients, whereas the coefficient of restitution had no significant influence. Neural network correlation coefficients exceeded 0.95, indicating a strong association between the variables. Our findings revealed that mathematical models can be used in conjunction with static repose angle experiments to calibrate DEM parameters for more robust and complex simulations. This research also quantifies and provides insights into the interactions between DEM parameters in direct measurement experiments, promoting more efficient calibration for future research.

Identification of dominant DEM parameters for multi-component segregation during heap formation, hopper discharge and chute flow

Segregation of granular materials is a critical phenomenon in various industries, such as food processing, pharmaceuticals, and mining. The Discrete Element Method (DEM) is an effective tool for gaining insight into granular segregation by providing particle-level information and the freedom to model mixtures that are often difficult or impossible to achieve through experiments. To ensure realistic material behaviour and correct representation of segregation, it is essential to calibrate the model parameters systematically. However, in the context of multi-component segregation, it is extremely challenging and computationally expensive to consider all parameters in the calibration procedure since interaction parameters between components must also be considered. This work aims to identify the dominant DEM parameters for modelling multi-component segregation during hopper discharge, chute flow and heap formation in a mixture of pellet and sinter. Utilising a representative example of a multi-component mixture with different sizes, densities and shapes used in blast furnaces, the investigation is done for various initial configurations as well as various mass ratios of the mixture. Our findings revealed that, while only particle-geometry interaction parameters dominate the segregation after the hopper and chute flow, particle-particle parameters are also significant for segregation in the heap. We also demonstrated that the downstream segregation is significantly influenced by the segregation upstream. Moreover, we found that the effect of pellet-sinter interactions is negligible. This research provides insights into the dominant DEM parameters, facilitating more efficient and robust calibration of multi-component models in future research endeavours.

On the sensitivity of DEM parameters on pile experiments

AbstractThe discrete element method (DEM) is suitable to investigate problems where large deformations occur especially in granular material. The fitting of reliable DEM parameters is crucial and a challenge which is caused by the high number of DEM parameters and the computational effort. Despite its drawbacks, a trial and error approach is often used for the DEM parameter calibration. The knowledge of the DEM parameter influence on the model response is necessary to improve the calibration and to check whether the experiment is suitable to calibrate specific parameters or not. It is possible to reduce the dimensionality of the optimisation problem by omitting parameters whose influence on the model response is negligibly small. One approach is to perform a global sensitivity analysis based on Sobol’ indices. A frequently used calibration experiment in literature is the pile experiment. The deviation between the experiment and the simulation is evaluated with the angle of repose. In the present paper, an algorithm to determine the angle of repose considering the three-dimensional shape of the heap is discussed. The global sensitivity analysis is performed for two different experimental heap set-ups. To decrease the computational effort of the sensitivity analysis, the model response is approximated with metamodels whose predictability is evaluated using the root mean squared error (RMSE) based on a separate sampling point set.

Calibration of the DEM sliding friction and rolling friction parameters of a cohesionless bulk material

The discrete element method (DEM) has become a valuable tool for understanding the mechanical behaviour of granular assemblies, however, the accuracy of the DEM simulations depends on several interaction parameters such as the sliding friction and rolling friction coefficients. Generally, these parameters are estimated using bulk calibration approach (BCA) where the draw down test has been suggested as an effective way to approach these coefficients. This test provides up to four bulk criteria, the angle of repose, shear angle, mass flow rate and the mass loss which are used to narrow down the possible coefficients. However, there is still more research needed around it to fully understand how this methodology works. An experimental and numerical study was carried out using the draw down test to assess the influence of different mass flow rates and particle shapes on DEM parameters for a cohesionless bulk material. It is concluded that use of multi-sphere particles and three aperture sizes in the draw down test to calibrate the sliding and rolling friction parameters of a cohesionless bulk material can converge to a small feasible region in which a single combination of the friction coefficients can be selected. The calibrated sliding and rolling friction coefficients were validated using multi-sphere particles, where the maximum deviation was 5.9% from the experimental values.

Calibration and validation of DEM parameters of food waste with high total solid content (≥20%) using physical experiments and EDEM simulations

Since discrete element method (DEM) parameters are important for the calculation accuracy of EDEM simulations, the DEM parameters of food waste with high total solid content (≥20%) are calibrated and validated by experiments and simulations in this paper. The physical properties of food waste are obtained by physical experiments, and then the optimal values of DEM model parameters recommended for food waste can be obtained from the GEMM database. Finally, the repose angle and the average torque obtained by EDEM simulations are compared with the experimental results to validate the calibrated DEM parameters of food waste. The average relative error of the repose angle between the experimental values and the simulation values is 3.23%, and the average relative error of the average torque value is 5.29%. That means the DEM parameters of food waste calibrated in this paper are correct and can be applied for simulating the mixing performance of food waste with high total solid content (≥20%).

An impact energy erosion model with an energy allocation rule for the discrete element method

Surface erosion caused by solid particle impact frequently occurs in engineering applications involving particulate flow. With the tremendous progress in computational technology, discrete element method (DEM)-based numerical methods have become a powerful means of studying surface erosion. However, the applicability of applied erosion models remains one of the critical challenges in DEM modelling to obtain satisfactory accuracy over a wide range of materials and impact conditions. In this study, an impact energy erosion model (IEEM) that evaluates erosive wear using accumulated impact energy during the impact process is developed. In IEEM, both cutting and deformation wear are considered. A calibration method of the specific energy that combines DEM simulations and abrasive jet tests is established. To quantitatively predict the erosive wear of the target surface eroded by different particles, an energy allocation rule based on the hardness ratio of the target surface to particles is proposed. Its rationality is thoroughly evaluated before embarking on DEM simulations. Subsequently, the accuracy of DEM simulations incorporated with IEEM and the energy allocation rule is comprehensively verified by comparing them with experimental data on ductile, brittle and semi-brittle materials. The results confirm that DEM simulations can reasonably predict the dependence of erosive wear on impact velocities, impact angles and erodent particle hardness, regardless of the material involved. The calibrated specific energy reflects the properties of the material and further determines the wear mechanism. Finally, the influence of critical DEM parameters on erosion prediction is studied. Generally, DEM incorporated with IEEM and the energy allocation rule provides a quantitative erosion prediction framework applicable to different materials and impact conditions. In the future, this framework has the potential to be applied to the erosion prediction of more systems.

The Impact of Discrete Element Method Parameters on Realistic Representation of Spherical Particles in a Packed Bed

Packed bed reactors play a crucial role in various industrial applications. This paper utilizes the Discrete Element Method (DEM), an efficient numerical technique for simulating the behavior of packed beds of particles as discrete phases. The focus is on generating densely packed particle beds. To ensure the model accuracy, specific DEM parameters were studied, including sub-step and rolling resistance. The analysis of the packed bed model extended to a detailed exploration of void fraction distribution along radial and vertical directions, considering the impact of wall interactions. Three different samples, spanning particle sizes from 0.3 mm to 6 mm, were used. Results indicated that the number of sub-steps significantly influences void fraction precision, a key criterion for comparing simulations with experimental results. Additionally, the study found that both loosely and densely packed beds of particles could be accurately represented by incorporating appropriate values for rolling friction. This value serves as an indicator of both inter-particle friction and friction between particles and the walls. An optimal rolling friction coefficient has been thereby suggested for the precise representation for the densely packed bed of spherical char particles.

Calibration and experimental verification of discrete element parameters for modelling feed pelleting

The discrete element method (DEM) has demonstrated significant advantages in modelling feed-tool interaction. The selection of an appropriate contact model and its parameter calibration is crucial for developing effective DEM simulations. A DEM model of feed powder was developed to forecast powder compression and densification in feed pelleting by using the Edinburgh elasto-plastic adhesion contact model. Firstly, a calibration method of DEM parameters based on the Plackett–Burman (PB) and the central composite (CC) test was proposed to enhance calibration efficiency. Both experimentally and through DEM simulation of the uniaxial confined compression experiments, powder repose angle experiments, and powder static friction angle experiments were used to calibrate the DEM parameters. Extract the DEM parameters that have a significant impact on feed pelleting through PB testing. Then, the effects of these parameters on response were analysed using CC testing, and ascertain their optimal values. Finally, the single-hole open compression experiment and feed pelleting experiment were used to evaluate the accuracy of the parameters. The results show that the average relative error between the predicted ultimate compressive force in the open compression experiment and the measured value is 8.16%, and that of the torque of the pellet mill is 2.10%. Thus, it was shown that the modelling and calibration method can effectively predict the compression and densification of powder in the feed pelleting.

Calibrating polypropylene particle model parameters with upscaling and repose surface method

The discrete element method (DEM) is a computational technique extensively utilized for simulating particles on a large scale, specifically focusing on granular materials. Nonetheless, its implementation requires a substantial amount of computational power and accurate material properties. Consequently, this study delves into an alternative approach referred to as volume-based scaled-up modeling, aiming to simulate polypropylene particles using DEM while mitigating the computational burden and regenerating new material properties. This novel method aims to reduce the CPU time required for the simulation process and represent both the macro mechanical behavior and micro material properties of polypropylene particles. To accomplish this, the dimensions of the polypropylene particles in the DEM simulation were magnified by a factor of two compared to the original size of the prolate spheroid particles. In order to determine the virtual micro material properties of the polypropylene particles, a calibration method incorporating the design of experiments (DOE) and repose surface methodology was employed. The predicted bulk angle of repose (AOR) derived from the upscaled DEM parameters exhibited a remarkably close agreement with the empirical AOR test, demonstrating a small relative error of merely 1.69 %. Moreover, the CPU time required for the upscaled particle model proved to be less than 71 % of that necessary for the actual-scale model of polypropylene particles. These compelling results confirm the effectiveness of enlarging the particle volume used to calibrate micro-material properties in the Discrete Element Method (DEM) through the DOE technique. This approach proves to be a reliable and efficient method

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.

Calibration and verification of DEM parameters for particles in transfer chute from rapid flow to stable accumulation

The discrete element method (DEM) is conducive to optimizing bulk material conveying equipment. Calibration experiments are essential in DEM simulation, requiring particles to accurately replicate the flow behavior from rapid motion (up to a maximum of 6 m/s) to stable accumulation in bulk material handling. In typical calibration experiments, excessively high particle velocities will affect the stability of the characterization data in stable accumulation. The paper presents an innovative calibration approach that enables reliable prediction of both impact forces during rapid flow and bulk density during stable accumulation. A single-factor numerical test determines the sensitivity of simulation parameters to calibration experiments, and comprehensive testing methods ensure that equivalent DEM contact parameters align with experimental particle behavior. Finally, verification experiments indicate that the proposed calibration experiments in this study can successfully replicate the particle flow behavior during bulk material handling.

A modified particle contact model for matching the ratios of uniaxial compressive to tensile strength of brittle rocks

The Hertz-Mindlin with bonding (HMB) contact model in the commercial discrete element method (DEM) software EDEM is widely used to simulate and analyze the mechanical behavior of rocks. However, it cannot match the high ratios of uniaxial compressive strength (UCS) to tensile strength (TS) observed in brittle rocks like basalt. In this study, a modified HMB contact model was developed by adequately considering the contribution of moment to stress and the influence of normal stress on shear strength. At the same time, a DEM parameters inversion method was proposed to achieve rapid and accurate calibration of various microscopic parameters. The results indicate that the moment-contribution factor and maximum tensile strength in the modified HMB contact model are two key parameters affecting the UCS/TS ratio. Specifically, the moment-contribution factor mainly affects UCS, while also jointly influencing TS together with the maximum tensile strength. The modified HMB contact model successfully achieves the high UCS/TS ratio that aligns with the test value and exhibits significant pressure-dependence. This model proves to be appropriate for accurately simulating the mechanical behavior of brittle rocks.

大葱种子物性参数测量及 DEM参数仿真标定

The contact parameters setting determines the accuracy of discrete element method (DEM) simulation analysis, while there is a lack of research on contact parameters of green onion seed. In this paper, the physical parameters of green onion seeds were measured by experiment, and the DEM parameters were calibrated by simulation. Based on EDEM software, the Hertz-Mindlin no-slip model was used to create the particle model of green onion seeds that takes on the shape of peltate with the irregular surface, and the repose angle was measured by the measurement method of lifted cylinder. Plackett-Burman test was designed to screen the significance of parameters, and the optimal range of significance parameters was further determined through the steepest climbing test. Then the regression model of seeds repose angle was obtained by Box-Behnken test and the optimal parameter combination was calculated: the static friction factor between seeds was 0.424, the rolling friction factor was 0.085, the static friction factor between seeds and steel was 0.310. The optimized repose angle in the simulation had an overall relative error of 0.54%, indicating that the contact parameters of the calibrated green onion seed had high accuracy.

A study of particle flow in a ribbon reactor: Effect of ribbon configuration on mixing and heat transfer performance

The mixing and heat transfer of particles are two important operating units in the lithium-ion battery industry due to the cycling performance of the cathode is mainly dependent on the mixing and heat transfer performance of granular matter, such as needle coke and asphalt particles. In this work, simulations of the particle mixing and heat transfer process in a ribbon reactor were conducted based on the three-dimensional discrete element method (DEM). The stacking angle test, rotary drum, and thermal conductivity test were designed to obtain the DEM parameters. The trajectory of tracer particles, total velocity fluctuation, coordinate number, and relative standard deviation (RSD) were used to analyze the mixing and heat transfer process. It was found that particles in the ribbon reactor experience two types of movement. The first one is high-speed tangential movement, and the second one is low-speed recirculation movement in the vertical plane. Particles near the vessel wall and impeller shaft are first heated and a cold core is generated in the initial stage of heat transfer. Results obtained indicate that the mixing performance increases slightly with the increase of ribbon number. The mixing performance increases with the increase of velocity fluctuation and the decrease of coordination number, and the heat transfer can be improved by enhancing the mixing performance of granular matter. Increasing the ribbon width and ribbon pitch can improve the heat transfer performance, while the ribbon pitch has little influence on the mixing performance.