Influence of particle shape on the shear behavior of superellipsoids by discrete element method in 3D

This paper aims to study the influence of particle shape on the shear strength of superellipsoidal particles by Discrete Element Method (DEM) simulations of triaxial tests in 3D. A total of forty-nine types of equiaxed superellipsoidal particles from three evolution paths have been created. The definition of effective porosity has been proposed. Our findings show that both the particle sphericity and roundness affect the shear strength of the superellipsoidal particle system. Under the mutual impact of initial porosity and particle shape, the simulation results of shear strength and volumetric strains present a trend of initially decreasing and subsequently increasing. The microstructure evolution of superellipsoidal particles during the shearing process is observed microscopically. The anisotropy of fabric reveals the mechanism of effective porosity and sphericity influencing the macroscopic shear strength at the particle scale.     

Quantifying the effects of elongation and flatness on the shear behavior of realistic 3D rock aggregates based on DEM modeling

It is significant for industrial production and engineering practice to study the macro and micromechanical behaviors of realistic particles in nature. Based on the rock aggregates database obtained by 3D scanning, this study investigated the effect of particle shape on the shear behaviors of particles by discrete element method (DEM) modeling. First, 1200 rock particle models were acquired by white-light scanning, and the elongation index (EI) and flatness index (FI) of the 1200 particles were calculated. After initial dense samples were created for particles with specific EI and FI values by the isotropic compression method, all the samples were sheared in drained triaxial compression tests under a quasi-static condition. Then, the mechanical behaviors of the samples at the peak and critical states were analyzed. Meanwhile, the evolution of internal mechanical behaviors during the shearing of samples with different EI and FI values was evaluated. Finally, through the analysis of the stress-force-fabric relationship, the underlying mechanism explaining why the macroscale mechanical behaviors of samples were dominated by particle shape was revealed from the perspective of fabric anisotropy.     

Numerical simulation of particulate cake formation in cross-flow microfiltration: Effects of attractive forces

We present a two-dimensional simulation model to explore cake formation in cross-flow filtration. The model uses the lattice Boltzmann method (LBM) for fluid computation and the discrete element method (DEM) for particle computation; they were fully coupled with the smoothed profile method. We verified our model by simulating filtration under different transmembrane pressures. We then investigated the effects of attractive forces and particle concentration on the cake formation mechanism. Generally, as the attractive interaction and particle concentration increased, the particles formed a cake layer with a looser body and rough surface, due to the decrease in the mobility of the particles in contact with the cake surface. It is concluded that the effects of particle concentration are affected by the different conditions of attractive interactions between the particles.     

Comprehensive numerical modelling of the hot isostatic pressing of Ti-6Al-4V powder: From filling to consolidation

Hot Isostatic Pressing (HIP) is a manufacturing process for production of near-net-shape components, where models based on Finite Element Method (FEM) are generally used for reducing the expensive experimental trials for canister design. Researches up to date implement in the simulation a uniform powder relative density distribution prior HIPping. However, it has been experimentally observed that the powder distribution is inhomogeneous after filling, leading to a non-uniform tool shrinkage. In this study a comprehensive numerical model for HIPping of Ti-6Al-4V powder is developed to improve model prediction by simulating powder filling and pre-consolidation by means of a two-dimensional Discrete Element Method (DEM). Particles’ dimension has been scaled up in order to reduce the computational cost of the analysis. An analytical model has been developed to calculate the relative density distribution from powder particle distribution provided by DEM, which is then passed in information to a three-dimensional FEM implementing the Abouaf and co-workers model for simulating powder densification during HIPping. Results obtained implementing the initial relative density distribution calculated from DEM are compared with those obtained considering a uniform relative density distribution over the powder domain (classic approach) at the beginning of the analysis. Experimental work has been carried out for validating the DEM (filling) and FEM (HIP) model. Comparison between experimental and numerical results shows the ability of the DEM model to represent the powder flow during filling and pre-consolidation, providing also a reliable values of the relative density distribution. It also highlights that taking into account the non-uniform powder distribution inside the canister prior HIP is vital to improve numerical results and produce near-net-shape components.     

DEM study and machine learning model of particle percolation under vibration

This paper aims to understand model the effect of vibration on particle percolation. The percolation of small particles in a vibrated bed of big particles is studied by DEM. It is found the percolation velocity (V_p) decreases with increasing vibration amplitude (A) and frequency (f) when the size ratio of small to large particles (d/D) is smaller than the spontaneous percolation threshold of 0.154. Vibration can enable percolation when the size ratio is larger than 0.154, while V_p increases with increasing A and f first and then decreases. V_p can be correlated to the vibration velocity amplitude under a given size ratio. Previous radial dispersion model can still be applied while the dispersion coefficient is affected by vibration conditions and size ratio. Furthermore, a machine learning model is trained to predict V_p as a function of A, f and d/D, and is then used to obtain the percolation threshold size ratio as a function of vibration conditions.     

Numerical study of forced axial segregation of binary density granular system in a split rotary drum

Driving and controlling the segregation in a rotary drum is both a theoretical and a practical challenge in powder technology. A novel horizontal split drum design composed of two reverse rotating sub-drums was explored to drive axial segregation of granular matter. DEM (Discrete Element Method) simulations were performed to study the particle dynamics and segregation performance of binary density particles in the split drum. Then, the effects of drum speed, the speed ratio of the two sub-drums, and the split position on the axial segregation were analyzed. It was found that true axial segregation occurred in the split drum and heavier particles tend to accumulate in the region near the split. An increase in drum speed can accelerate the segregation but it has no obvious influence on the final axial distribution of particles. The results obtained indicate when two sub-drums rotate at different speeds, the concentrated region of heavier particles moves towards the low-speed sub-drum. These findings could lead to new designs for a broad range of particle processing industries.     

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.     

DDRM模型在渠江流域的应用研究

为了进一步探究分布式水文模型(DDRM)的适用性,基于渠江流域的数字高程模型(DEM),提取了研究区域的边界、河网水系,计算了研究区域的地形指数;然后采用基于DEM的DDRM模型进行流域降雨-径流过程模拟,并用确定性系数、洪峰相对误差、峰现时间误差等对DDRM模型的模拟精度进行了评价;同时,对比分析了不同DEM栅格分辨率(1 km和2 km)对模型模拟精度的影响。结果表明:基于上述2种分辨率的DDRM模型在渠江流域降雨-径流模拟中均取得了较好的模拟效果,其中基于1 km栅格的DDRM模拟精度略优于2 km栅格。DDRM模型结构简单,参数较少、物理过程明确,而且能够模拟流域土壤含水量和径流量的空间分布,可为缺资料地区水文模拟提供一种新的方法。     

适用于Hadoop云平台的洪水淹没分析并行算法

数字高程模型(Digital Elevation Model, DEM)特殊的数据结构有利于数据在计算机中进行计算,但其数据量大,受计算机性能的限制,无法满足大规模洪水淹没分析的需要。针对单机计算能力有限以及有源淹没分析递归算法占用计算机资源较多的问题,提出了一种利用Hadoop云平台进行洪水淹没分析的并行算法。该算法对研究区域DEM按规则格网的思想进行划分,将大规模的研究区分割成多个规则的小区域。在指定水位下,对每个小区域进行数据压缩,计算每个栅格单元的淹没值,并将同一行中相连的淹没栅格序列记为淹没块,每个淹没块包含该栅格序列所属的区块号、行号、起始列号、终止列号以及淹没标记;当不同行或不同区域中的淹没块相互连通时,需对其淹没标记进行一致性设计。最终,根据淹没点所在淹没块的淹没标记提取出淹没点所在的淹没区,即为实际的淹没范围。实验结果表明,在指定淹没水位下,通过该算法可以快速提取淹没范围以及实现淹没水深的计算。     

基于图遍历的计算DEM数据洪水淹没范围的算法

针对数字高程模型(digital elevation model,DEM)大区域数据量大,而常用的洪水淹没算法(如种子蔓延法)不适用于对DEM的分块计算和分次存储,且用时和耗内存大,设计了一种使用图遍历来有效计算洪水淹没范围的算法,对DEM数据分条带读入计算机内存,然后采用块码压缩方式将潜在淹没区域压缩成块存入磁盘,使用广度优先搜索的图遍历方法读取数据。该算法设计逻辑清晰、实用性强且运算效率高,适用于大范围地形复杂的淹没区域。选取北京市、四川省的DEM数据进行实验,实验结果验证了该算法满足计算快速、占用内存少的要求。