DEM calibration of cohesive material in the ring shear test by applying a genetic algorithm framework

This research demonstrates capturing different stress states and history dependency in a cohesive bulk material by DEM simulations. An automated calibration procedure, based on the Non-dominated Sorting Genetic Algorithm, is applied. It searches for the appropriate simulation parameters of an Elasto-Plastic Adhesive contact model such that its response is best fitted to the shear stress measured in experiments. Using this calibration procedure, the optimal set of DEM input parameters are successfully found to reproduce the measured shear stresses of the cohesive coal sample in two different pre-consolidation levels. The calibrated simulation resembles the stress history dependent values of shear stress, bulk density and wall friction. Through the case study of the ring shear tester, this research demonstrates the robustness and accuracy of the calibration framework using multi-objective optimization on multi-variable calibration problems irrespective of the chosen contact model.     

Replicating cohesive and stress-history-dependent behavior of bulk solids: Feasibility and definiteness in DEM calibration procedure

This paper presents a multi-step DEM calibration procedure for cohesive solid materials, incorporating feasibility in finding a non-empty solution space and definiteness in capturing bulk responses independently of calibration targets. Our procedure follows four steps: (I) feasibility; (II) screening of DEM variables; (III) surrogate modeling-based optimization; and (IV) verification. Both types of input parameter, continuous (e.g. coefficient of static friction) and categorical (e.g. contact module), can be used in our calibration procedure. The cohesive and stress-history-dependent behavior of a moist iron ore sample is replicated using experimental data from four different laboratory tests, such as a ring shear test. This results in a high number of bulk responses (i.e. ≥ 4) as calibration targets in combination with a high number of significant DEM input variables (i.e. > 2) in the calibration procedure. Coefficient of static friction, surface energy, and particle shear modulus are found to be the most significant continuous variables for the simulated processes. The optimal DEM parameter set and its definiteness are verified using 20 different bulk response values. The multi-step optimization framework thus can be used to calibrate material models when both a high number of input variables (i.e. > 2) and a high number of calibration targets (i.e. ≥ 4) are involved.     

Calibration and verification of DEM parameters for dynamic particle flow conditions using a backpropagation neural network

The Discrete Element Method (DEM) requires input parameters to be calibrated and validated in order to accurately model the physical process being simulated. This is typically achieved through experiments that examine the macroscopic behavior of particles, however, it is often difficult to efficiently and accurately obtain a representative parameter set. In this study, a method is presented to identify and select a set of DEM input parameters by applying a backpropagation (BP) neural network to establish the non-linear relationship between dynamic macroscopic particle properties and DEM parameters. Once developed and trained, the BP neural network provides an efficient and accurate method to select the DEM parameter set. The BP neural network can be developed and trained for one or more laboratory calibration experiments, and be applied to a wide range of bulk materials under dynamic flow conditions.     

Probabilistic calibration of discrete element simulations using the sequential quasi-Monte Carlo filter

The calibration of discrete element method (DEM) simulations is typically accomplished in a trial-and-error manner. It generally lacks objectivity and is filled with uncertainties. To deal with these issues, the sequential quasi-Monte Carlo (SQMC) filter is employed as a novel approach to calibrating the DEM models of granular materials. Within the sequential Bayesian framework, the posterior probability density functions (PDFs) of micromechanical parameters, conditioned to the experimentally obtained stress–strain behavior of granular soils, are approximated by independent model trajectories. In this work, two different contact laws are employed in DEM simulations and a granular soil specimen is modeled as polydisperse packing using various numbers of spherical grains. Knowing the evolution of physical states of the material, the proposed probabilistic calibration method can recursively update the posterior PDFs in a five-dimensional parameter space based on the Bayes’ rule. Both the identified parameters and posterior PDFs are analyzed to understand the effect of grain configuration and loading conditions. Numerical predictions using parameter sets with the highest posterior probabilities agree well with the experimental results. The advantage of the SQMC filter lies in the estimation of posterior PDFs, from which the robustness of the selected contact laws, the uncertainties of the micromechanical parameters and their interactions are all analyzed. The micro–macro correlations, which are byproducts of the probabilistic calibration, are extracted to provide insights into the multiscale mechanics of dense granular materials.     

Predicting dust emissions – Experimental study compared to coupled DEM/CFD simulations using a reference test bulk material

The awareness of dust emissions is crucial regarding safe industrial processes, environmental protection and health care. For this purpose, closely linked experimental and numerical investigations are performed. This work presents the results of an experimental study which is used for the calibration of a modelling framework based on the Discrete Element Method (DEM) coupled with Computational Fluid Dynamics (CFD) and applied for the calculation of dust emissions for predictive purposes. The key objective of the approach is to come up with a dust source term which enables to describe and to quantify the release of particle emissions. For the presented experimental study, a wind tunnel and a rotating drum setup, which cover various handling types of bulk materials, are used in order to gain data about parameters having an impact on the dust release. The special feature of the investigations is the use of a reference test bulk material which represents a bulk material in its generally main fractions, the fine and the coarse material, keeping the discrepancy between experiments and simulations low. With the help of the experimental results the calibration of the simulation model was carried out and followed by a comparison.     

Validation of microparameters in discrete element modeling of sea ice failure process

A GPU-based discrete element method (DEM) with bonded particles is investigated to simulate the mechanical properties of sea ice in uniaxial compressive and three-point bending tests. Both the uniaxial compressive strength and flexural strength of sea ice are related to the microparameters in DEM simulation including particle size, sample size, bonding strength, and interparticle friction coefficient. These parameters are analyzed to build the relationship between the material macrostrengths of sea ice and the microparameters of the numerical model in DEM simulations. Based on this relationship, the reasonable microparameters can be calculated by given macrostrengths in the applications of simulating the failure processes of sea ice. In this simulation, both uniaxial compressive strength and flexural strength of ice increase with the increasing ratio of sample size and particle size. The interparticle friction coefficient is directly related to the compressive strength but has little effect on the flexural strength. In addition, numerical simulations are compared with experimental data to show the performance of the proposed model, and a satisfactory agreement is achieved. Therefore, this microparameter validation approach based on macrostrengths can be applied to simulate the complicated failure process of sea ice interacting with offshore platform structures.     

A coupled FEM/DEM approach for hierarchical multiscale modelling of granular media

A hierarchical multiscale framework is proposed to model the mechanical behaviour of granular media. The framework employs a rigorous hierarchical coupling between the FEM and the discrete element method (DEM). To solve a BVP, the FEM is used to discretise the macroscopic geometric domain into an FEM mesh. A DEM assembly with memory of its loading history is embedded at each Gauss integration point of the mesh to serve as the representative volume element (RVE). The DEM assembly receives the global deformation at its Gauss point from the FEM as input boundary conditions and is solved to derive the required constitutive relation at the specific material point to advance the FEM computation. The DEM computation employs simple physically based contact laws in conjunction with Coulomb's friction for interparticle contacts to capture the loading‐history dependence and highly nonlinear dissipative response of a granular material. The hierarchical scheme helps to avoid the phenomenological assumptions on constitutive relation in conventional continuum modelling and retains the computational efficiency of FEM in solving large‐scale BVPs. The hierarchical structure also makes it ideal for distributed parallel computing to fully unleash its predictive power. Importantly, the framework offers rich information on the particle level with direct link to the macroscopic material response, which helps to shed lights on cross‐scale understanding of granular media. The developed framework is first benchmarked by a simulation of single‐element drained test and is then applied to the predictions of strain localisation for sand subject to monotonic biaxial compression, as well as the liquefaction and cyclic mobility of sand in cyclic simple shear tests. It is demonstrated that the proposed method may reproduce interesting experimental observations that are otherwise difficult to be captured by conventional FEM or pure DEM simulations, such as the inception of shear band under smooth symmetric boundary conditions, non‐coaxial granular response, large dilation and rotation at the edges of shear band and critical state reached within the shear band. Copyright © 2014 John Wiley & Sons, Ltd.     

Parameter calibration of corncob based on DEM

Corncob is one of the main components of corn ears. Because the mechanical properties of different parts of corncob are very different, and there is a lack of research on the simulation and calibration of corncob parameters at present, the established corn ear or corncob simulation model has low accuracy and poor reliability. In this study, the simulation tests of corncob calibration parameters are carried out based on DEM. Firstly, a modelling method of corncob is proposed to establish sample models of corncob. Then, the DEM simulation parameters that restitution coefficient, static friction coefficient, and rolling friction coefficient of “particle–particle” and “particle-geometry”, and Poisson’s ratio of particle are determined by the Plackett-Burman test and Box-Behnken test. Next, the simulated bending test of corncob is carried out using the calibrated parameters. Finally, by comparing the physical and simulated bending test results, it shows the anti-destructive forces of corncob are 204.52 N and 197.3 N, respectively, with a relative error of 3.53%. This study verifies the reliability of parameter calibration for the discrete element model of corncob and provides a new method for establishing simulation models of corn ear and other materials.     

Characterisation of binary mixtures of pellets and sinter for DEM simulations

To study the distribution of ferrous burden (which is a mixture of pellets and sinter) in the blast furnace, the burden must be characterised in terms of input parameters which can be used in discrete element method (DEM) simulations. A methodology is presented to determine these parameters which can help represent the ferrous burden mixture. First, angle of repose experiments are performed and determined for pellet, sinter and their mixtures at different proportions. Using this experimental data, the DEM parameters individually for pellets and sinter using previously determined experimental values and DEM calibration approach are chosen and they are represented accurately. From these, the values of DEM parameters for pellet-sinter contact are taken as the average of their individual counterparts. Using all determined parameters for intra-material as well as inter-material particle contacts, simulations of angle of repose for mixtures at varying proportions are done and a good match is found between experimental and simulation values at all proportions. In this way, binary mixtures are characterised while maintaining the constituents as individual species.     

A calibration framework for the microparameters of the DEM model using the improved PSO algorithm

The discrete element method (DEM) is commonly used for simulating the mechanical characteristics of rock materials; however, constructing a DEM model requires the specification of a number of microparameters. In this paper, to obtain the microparameters of the DEM model, the improved particle swarm optimization (PSO) calibration method was presented. Based on numerical simulation examples, the new approach is considered valid for calibrating the microparameters of the DEM model. Moreover, it is concluded that different sets of microparameters can be determined when few macroparameters are used, which indicates that the empirical formula between microparameters and macroparameters is not reliable. From the analysis of the numerical simulation results, it is suggested that more macroparameters should be used to calibrate the microparameters of the DEM model, and the corresponding numerical simulation results could be more reliable; otherwise, the generated numerical model may not accurately simulate the mechanical characteristics of rock materials.     

DEM simulation of laboratory Knelson concentrator to study the effects of feed properties and operating parameters

Understanding of the separation mechanism of Knelson concentrators at particle scale and investigation of the effects of different feed properties and operating parameters helps process engineers to design, control and optimize these particulate solids processing units. This article reports findings of an investigation on separation performance of laboratory Knelson concentrator using an open-source Discrete Element Method (DEM) solver ‘LIGGGHTS’. An available standard 7.5-cm (3″) laboratory Knelson concentrator with five collecting rings was numerically simulated. The effects of feed properties including feed mass, feed type, feed grade, feed particles size, feed particle population and operating parameters including centrifugal force intensity, feeding rate and concentration cycle time on separation performance of laboratory Knelson concentrator were studied. Results of DEM simulations are expressed qualitatively and quantitatively in terms of performance indices, i.e., concentrate grade and total recovery. For validation of simulation predictions, the authors carried out several experimental tests on synthetic samples of pure quartz and magnetite mixtures under the same conditions used in simulation environment. A close agreement between simulation predictions and laboratory measurements for concentrate grade and total recovery was observed which validates DEM simulations.     

Development of a reduced-order model for large-scale Eulerian–Lagrangian simulations

Multiphase flows with solid particles are commonly encountered in various industries. The CFD–DEM method is extensively used to simulate their dynamical behavior. However, the application of the CFD–DEM method to simulate industrial-scale powder processes unavoidably leads to huge computational costs. With the aim of overcoming this issue, we propose a nonintrusive reduced-order model for Eulerian–Lagrangian simulations (ROM-EL) to efficiently reproduce gas–solid flow in fluidized beds. In the model, a Lanczos based proper orthogonal decomposition (LPOD) is newly employed to efficiently generate a set of POD bases. After the numerical snapshots are projected onto the reduced space spanned by the POD bases, a series of multidimensional functions of POD coefficients are constructed using a surrogate interpolation method. To demonstrate the effectiveness of this model, validation studies are performed based on the simulations of a fluidized bed. The macroscopic properties, such as the particle distribution, bed height, pressure drop, and distribution of bubble size, are shown to agree well in the CFD–DEM model and ROM-EL. Further, our proposed ROM-EL reduces the computational cost by several orders of magnitude compared with the CFD–DEM simulation. Accordingly, the ROM-EL could significantly contribute to the progress of modeling and simulation for industrial granular flows.     

DEM modelling for flow of cohesive lignocellulosic biomass powders: Model calibration using bulk tests

Biomass feeding problems greatly hinder the industrialization of entrained-flow gasification systems for production of 2nd generation biofuels. Appropriate DEM modelling could allow engineers to design solutions that overcome these flow problems. This work shows the application of a DEM calibration framework to produce a realistic, calibrated and efficient material model for lignocellulosic biomass. A coarse (500–710?µm) and a fine (200–315?µm) sieving cut of milled poplar were used in this study. The elongated shape and the cohesive behavior were respectively simulated using a coarse-grained multisphere approach and a cohesive SJKR contact model. Measurements of three physical responses (angle-of-repose, bulk density, a retainment ratio) allowed calibration of the sliding (µ_s) and rolling friction (µ_r) coefficients and the cohesion energy density (CED). Using a statistical analysis, the most influential calibration parameters for each bulk response were identified. A Non-Dominated Sorting Genetic Algorithm was used to solve the calibration multi-objective optimization problem. Several sets of optimal solutions reproduced accurately the three physical responses and the experimental shear responses were closely reproduced by simulations for the population of coarse particles. The DEM calibration framework studied here aims to produce material models useful for assessing flow behavior and equipment interaction for biomass particles.     

A more efficient method for calibrating discrete element method parameters for simulations of metallic powder used in additive manufacturing

Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process that uses a high-power laser to selectively melt metal powder that has been spread, layer-by-layer, to create parts with highly complex features. Because of the strong influence that the powder spreading process has on the final part quality, a better understanding of the powder behavior and its interactions with existing powder layers and the solidified surfaces during this process is needed. Discrete element method simulations (DEM) provide a particle-scale approach capable of examining these mechanisms. While proper calibration of these simulations provides confidence in the quantitative results, the usual calibration process (hopper method) is computationally time intensive. Because of the wide variety of powder material used in LPBF, which will affect the powder spreading process, and because of the large numbers of particles present in only a small volume of powder, a more efficient calibration process was necessary. Use of a new cloud method was shown to make calibrations more tractable and reduce simulation times by up to 89% when compared to a typical hopper method. Similar to previous studies shown in the literature, a reduction in the particle material’s Young’s modulus was also used to reduce simulation times by up to 62%. Both sliding and rolling friction were needed for the DEM angles of repose to match the empirical data because of the non-sphericity of some of the powder particles, which has been quantified. The calibrated parameters resulted in determination of a powder density within 10% and an angle of repose within 1% of the targeted experimental values. These parameters will be used in extensive future DEM simulations of the LPBF powder spreading process. While AM processes were the main motivation for this work, the calibration procedures summarized herein can be extended to other gas-atomized powders used in injection molding and other metallurgical fields, as well as many other granular materials.     

基于线性回归的单柱塞泵单向阀参数优化

针对单柱塞泵系统中配流单向阀参数不合理所导致的吸油不充分、系统响应慢等问题,提出了一种基于线性回归的多参数优化方法。首先,通过AMESim软件进行单柱塞泵系统仿真分析,并利用MATLAB拟合工具箱分别探讨了不同单向阀参数（弹簧预紧力、弹簧刚度和阀芯质量）与进油口流量的关系。然后,在利用主成分分析法消除各参数之间相关性的基础上,以进油口流量为因变量,弹簧预紧力、弹簧刚度和阀芯质量为自变量,各参数的取值范围为约束条件,建立了基于线性回归的单向阀参数优化模型,并采用遗传算法进行优化求解。最后,根据优化前后的单向阀参数,对单柱塞泵系统进行仿真分析和实验验证。仿真结果表明,优化后进油口流量提高了21.3%;实验结果表明,优化后进油口的实际流量提高了16.8%。研究表明,所提出的多参数优化方法是一种有效的方法,可为单柱塞泵系统中配流单向阀的参数优化提供参考。     

Two-dimensional discrete element modelling of bender element tests on an idealised granular material

The small-strain (elastic) shear stiffness of soil is an important parameter in geotechnics. It is required as an input parameter to predict deformations and to carry out site response analysis to predict levels of shaking during earthquakes. Bender element testing is often used in experimental soil mechanics to determine the shear (S-) wave velocity in a given soil and hence the shear stiffness. In a bender element test a small perturbation is input at a point source and the propagation of the perturbation through the system is measured at a single measurement point. The mechanics and dynamics of the system response are non-trivial, complicating interpretation of the measured signal. This paper presents the results of a series of discrete element method (DEM) simulations of bender element tests on a simple, idealised granular material. DEM simulations provide the opportunity to study the mechanics of this testing approach in detail. The DEM model is shown to be capable of capturing features of the system response that had previously been identified in continuum-type analyses of the system. The propagation of the wave through the sample can be monitored at the particle-scale in the DEM simulation. In particular, the particle velocity data indicated the migration of a central S-wave accompanied by P-waves moving along the sides of the sample. The elastic stiffness of the system was compared with the stiffness calculated using different approaches to interpreting bender element test data. An approach based upon direct decomposition of the signal using a fast-Fourier transform yielded the most accurate results.     

An iterative method for coupling of deformation and failure mechanisms on different length scales

An iterative method for coupling of numerical simulations on two length scales is presented. The computations on the microscale and on the macroscale are linked via a suitable macroscopic constitutive law. The parameters of this material law depend on the deformation history and are obtained from simulations using microstructurally representative volume elements (RVEs) subjected to strain paths derived from the associated material points in the macroscopic structure. Thus, different constitutive parameter sets are assigned to different regions of the macrostructure. The microscopic and macroscopic simulations are performed iteratively and interact mutually via the strain paths and the constitutive parameters, respectively. As an example, the strip tension test for a porous material is modelled using the finite element (FE) method. The coupling procedure, the material law and its numerical implementation are described. The method is shown to allow for a detailed simulation of the deformation mechanisms both on the micro‐ and the macroscale as well as for an assessment of their interactions while keeping the computational efforts reasonably low. Copyright © 2000 John Wiley & Sons, Ltd.     

基于DEM的回转筛分数值模拟和效果研究

目的 在粮食储藏和加工过程中,平面回转筛是重要的粮食筛分清理设备。研究各项筛分处理参数对回转筛筛分性能的影响,并寻找回转筛的最优筛分参数。方法 基于离散元法（DEM）对回转筛在不同参数条件下的回转筛分过程进行数值模拟,以筛分流量、回转速度、回转半径和筛面倾角为4项变量依次进行单因素分析,得出各参数对筛分性能指标（筛分效率、除杂效率和物料流动速度）的影响规律,再以此选取各因素的较优水平范围进行多因素正交试验,并采用矩阵分析方法对试验结果进行权重排序。结果 得到了综合各项性能评价指标较优的参数组合：筛分流量为3.0 t/h,回转速度为465 r/min,回转半径为24 mm,筛面倾角为9°。结论 研究结果表明,寻求更优的筛分效率必然会降低除杂效率和物料流动速度,即物料在筛面上停留时间越长就越能增大其通过筛孔机会,同时杂质通过筛孔的概率也在增大。     

LS-DYNA MAT54 modeling of the axial crushing of a composite tape sinusoidal specimen

The suitability of a progressive failure material model to simulate the quasi-static crushing of a composite specimen is evaluated. The commercially available material model MAT54 “Enhanced Composite Damage” in LS-DYNA is often utilized to simulate damage progression in dynamic failure simulations because it requires a reduced number of experimental input parameters compared to damage mechanics-based material models. The composite specimen used for the experiments is a semi-circular sinusoid, and is comprised of carbon fiber/epoxy unidirectional prepreg tape. Results show that MAT54 can successfully reproduce experimental results, however the simulation is highly sensitive to changes in model parameters, which are either non-physical (i.e. are purely mathematical expedients), or cannot be measured experimentally. These include element size, contact definition, load–penetration curve, and crush front softening parameter, among others. Therefore, achieving successful simulation results requires extensive calibration of these parameters by trial and error, and a deep understanding of the strengths and challenges of the selected modeling strategy.     

大厚度复合材料曲面典型构件的工艺优化

大厚度复合材料的数值仿真存在缺乏实尺度验证、数值模型待优化等问题。本文针对真空辅助树脂传递模塑成型的大厚度复合材料曲面构件,通过大型风电叶片主梁的工艺仿真与实尺度实验验证,进行了工艺设计与工艺参数模型预测。首先对比研究了不同的工艺仿真方案;然后利用所选优化方案对树脂灌注方案进行工艺设计,并进行了实验验证;最后,提出了不同厚度制件的工艺参数预测模型。结果表明:所选优化方案可同时得到理想的计算效率和流动模拟结果;所设计工艺方案与实验吻合性良好;工艺参数预测模型所得结果与模拟结果基本一致。