Realistic Particle Shapes Research Articles

Investigating the Combined Effects of Inherent and Stress-Induced Anisotropy on the Mechanical Behavior of Granular Materials Using Three-Dimensional Discrete Element Method

The three-dimensional discrete element method (DEM) was employed to investigate the combined effects of inherent and stress-induced anisotropy of granular materials. The particles were modeled following real particle shapes. Both isotropic and inherently anisotropic specimens were prepared, and then true triaxial numerical tests were conducted using different intermediate principle stress ratios (b). The results indicate that the oriented particles in the anisotropic specimens form strong contacts in their long axis direction in the early stages of shearing, which restrains the contraction of the specimens. As the strain increases, the oriented particles start to rotate and slide, which results in shorter contraction stages and fewer number of interparticle contacts with peak values compared to the isotropic specimens. In addition, the increase inbvalues aggravates the rotating and sliding of particles in the inherently anisotropic specimens and restrains the contraction of the granular and the increase of contact forces. As a result, the inherent anisotropy reduces the effects of stress-induced anisotropy on the mechanical behavior of granular materials.

Evaluation of particle shape on direct shear mechanical behavior of ballast assembly using discrete element method (DEM)

Abstract Ballast layer is a part of railway foundation that its behavior is affected by the properties of constituent particles. The shape of the particles, which affects ballast behavior, is an important characteristic that is represented by angularity and sphericity indexes. In this study, the macro- and micro-mechanical shear behaviors of railway ballast are investigated by using Discrete Element Method (DEM). For this purpose, a three-dimensional (3D) program based on DEM has been modified and verified with the experimental results to simulate different real particle shapes for the direct shear test. Eight assemblies with different particle shape indexes were prepared. The results showed that the shear strength of ballast layer increased significantly with an increase in the angularity index; however, the strength initially increased with increasing sphericity and then dropped in as the sphericity decreased in the assembly of particles. Also, micro-mechanical responses showed that particle shape affected the formation of shear band.

Porosity Prediction of Granular Materials through Discrete Element Method and Back Propagation Neural Network Algorithm

Granular materials are used directly or as the primary ingredients of the mixtures in industrial manufacturing, agricultural production and civil engineering. It has been a challenging task to compute the porosity of a granular material which contains a wide range of particle sizes or shapes. Against this background, this paper presents a newly developed method for the porosity prediction of granular materials through Discrete Element Modeling (DEM) and the Back Propagation Neural Network algorithm (BPNN). In DEM, ball elements were used to simulate particles in granular materials. According to the Chinese specifications, a total of 400 specimens in different gradations were built and compacted under the static pressure of 600 kPa. The porosity values of those specimens were recorded and applied to train the BPNN model. The primary parameters of the BPNN model were recommended for predicting the porosity of a granular material. Verification was performed by a self-designed experimental test and it was found that the prediction accuracy could reach 98%. Meanwhile, considering the influence of particle shape, a shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape.

Modelling for the Prediction of Melting Temperature for Metallic Nanoparticles

At nano level, materials show very interesting physical properties with the variation of shape and size. The prediction of this behaviour has been a burning issue in the recent years in the scientific community as well. Even the physical properties of these materials are poorly investigated experimentally. To explain the sharp change in the properties of metals, as reported by some investigators, at their nanolevel, different models have been proposed. It is observed that in their theoretical prediction, they have not considered the exact arrangement of atoms in the lattice. In our attempt to understand the behaviour of the nanomaterials, we have studied the melting temperature of some nanosolids having face centered cubic lattice such as Aluminium (Al), Copper (Cu), Paladium (Pd), Platinum (Pt) and Gold (Au), considering different shapes with their sizes ranging from 30 nm to more smaller dimensions. For modelling analysis, we have considered the very basic and fundamental relation of cohesive energy with melting temperature along with modification with two realistic physical quantities-packing fraction and particle shape factor simultaneously to account the arrangements of atoms within the nanoparticle and on the surface as well. Our study shows that there is a very marked change in the melting temperature of the metallic nanosolids below 20 nm. Although in the earlier reported works, it has been claimed that this variation occurs at somewhat higher values. In this variation, the tetrahedral structure exhibits maximum variation of melting temperature while spherical one corresponds to the minimum change. In case of gold, our simulated data has been compared with available experimental values which is found in good agreement with it. This agreement between experimental and computed data validates our proposed model for the prediction of melting temperature of nanoparticles at varying dimensions viz, shape and size. Thus our proposed modification in the existing model is more appropriate in the prediction of melting point of nanoparticles with its varying shape and size.

Probabilistic characterization and simulation of realistic particle shape based on sphere harmonic representation and Nataf transformation

Significant commitment of cost, man power, and time are often required for characterizing particle morphology of granular materials based on X-ray micro-computed tomography (μCT) scanning and image processing. As a result, only a small portion of particles can be scanned, leading to uncertainty in characterizing realistic particle shape. This paper develops a probabilistic method to characterize and simulate sand particle shape based on Sphere Harmonic (SH) analysis, where Nataf transformation is used to construct the joint probability distribution of SH shape descriptors and to generate their random samples for reconstructing virtual particles. The proposed approach was applied to characterizing and simulating particle shapes of Leighton Buzzard sand (LBS), and these simulated particles were used as clump templates for discrete element method to account for effect of realistic particle shape. It was shown that the proposed approach properly characterizes and simulates the particle shape of LBS in a probabilistic manner.

Experimental and numerical investigation of Weibullian behavior of grain crushing strength

The Weibullian behavior of single grain crushing strength was investigated experimentally and numerically with the aim of enhancing the understanding of rock grain breakage. The morphologies of pebble grains were obtained using white light 3D laser scanning and image processing. A grain shape library was constructed for grain shape analysis with different shape descriptors. The use of the shape library and grain stability analysis is discussed for a suggested procedure to rotate a grain to its most stable configuration. Single grain crushing tests were performed for 30 pebbles to obtain force-displacement curves and fracture patterns. Each grain was compressed diametrically between flat platens. As expected, the values of the stress at bulk fracture follow a Weibull distribution. A procedure for generating crushable agglomerates with realistic particle shapes was demonstrated, which was accomplished in the discrete element modeling (DEM) of the single grain crushing test. The work presented here is novel in that both the heterogeneous micro-structures and randomly distributed flaws are considered. The DEM results demonstrate that the proposed modeling approach and calibrated parameters are reliable and can reflect the crushing behavior of rock pebbles. Finally, three parametric studies were presented evaluating the effects of micro-crack density, micro-crack disorder, and grain morphology on the Weibullian behavior of the crushing strength, none of which has previously been thoroughly considered. These three studies provide a deeper insight into the origin of the Weibullian behavior of single grain crushing strength.

Microstructural design for advanced light metals

Abstract

Importance of load frequency in applying cyclic loads to investigate ballast deformation under high-speed train loads

Railway ballast is now subjected to dynamic train loads with higher frequency components due to the continue increase of the train speed. However, the effect of load frequency on ballast permanent deformation was ignored in most laboratory tests. This study aims to clarify the importance of load frequency in applying cyclic loads to investigate the ballast permanent deformation under high-speed rail loading conditions. A three-dimensional discrete element model was established using clumps with realistic ballast particle shapes and validated with a laboratory test. Numerical simulations were then conducted with the validated DEM model to study the ballast deformation behavior. The simulated ballast deformation under high-speed train loads and that under cyclic loads with different load frequencies were compared to demonstrate the quantitative discrepancy between them. The results from this study confirm that under high-speed rail loading scenarios, the load frequency has significant influence on ballast permanent deformation and there is significant importance to take the effect of the load frequency into account when investigating the permanent deformation of high-speed railway ballast by applying cyclic loads. It is recommended to apply the cyclic loading with the same amplitude to the high-speed train load and with the frequency equivalent to the passing frequency of the train’s wheelbase to the ballast in laboratory tests so as to obtain quantitative ballast deformation results by using one load cycle to simulate two axle loads of a train.

Numerical simulation of dynamic repetitive load test of unbound aggregate using precision unbound material analyzer

This study developed discrete element models to simulate the dynamic repetitive load test using precision unbound material analyzer (PUMA). The simulated aggregate was created with the clump method simulating real particle shape. The parameters of the real particle shape were determined by the smoothness degree of particle shape and the required time-steps, which balanced the accuracy of particle shape and computational efficiency. The rubber ball layer (RBL) was adopted as the boundary conditions of repeated load test according to the features of the PUMA apparatus. Through the comparisons of results between numerical simulation and laboratory tests, the thickness of RBL was found close to the median size of particles resulting in the better simulation. Moreover, the numerical simulation results were verified with the conducted laboratory tests. The predicted permanent deformation was consistent with the laboratory measurement, which showed that discrete element based simulation can obtain the reliable results for the dynamic repetitive load test. The predicted permanent deformation using the real particle model was found much closer to the experimental results, which was greater than the one obtained using the ball model. The analytical results of volumetric-axial strains, directions of displacements, and contact forces indicated that the real particle method can better represent the migration movements of aggregates in the laboratory conditions. The simulation results indicate the importance of considering real aggregate shape in discrete element modelling of unbound material behaviour.

Reconstructing granular particles from X-ray computed tomography using the TWS machine learning tool and the level set method

X-ray computed tomography (CT) has emerged as the most prevalent technique to obtain three-dimensional morphological information of granular geomaterials. A key challenge in using the X-ray CT technique is to faithfully reconstruct particle morphology based on the discretized pixel information of CT images. In this work, a novel framework based on the machine learning technique and the level set method is proposed to segment CT images and reconstruct particles of granular geomaterials. Within this framework, a feature-based machine learning technique termed Trainable Weka Segmentation is utilized for CT image segmentation, i.e., to classify material phases and to segregate particles in contact. This is a fundamentally different approach in that it predicts segmentation results based on a trained classifier model that implicitly includes image features and regression functions. Subsequently, an edge-based level set method is applied to approach an accurate characterization of the particle shape. The proposed framework is applied to reconstruct three-dimensional realistic particle shapes of the Mojave Mars Simulant. Quantitative accuracy analysis shows that the proposed framework exhibits superior performance over the conventional watershed-based method in terms of both the pixel-based classification accuracy and the particle-based segmentation accuracy. Using the reconstructed realistic particles, the particle-size distribution is obtained and validated against experiment sieve analysis. Quantitative morphology analysis is also performed, showing promising potentials of the proposed framework in characterizing granular geomaterials.

A rheometry based calibration of a first-order DEM model to generate virtual avatars of metal Additive Manufacturing (AM) powders

The technology of metal powder-bed Additive Manufacturing (AM) has been evolving rapidly over the last few years. Creating parts through additive manufacturing within acceptable tolerances for porosity, strength and roughness is contingent upon many process variables. One important factor is the ability to spread uniform layers of loose powder for a given thickness over a given coverage area. This is related to the angle of repose of the powder and its rheological behavior i.e., the flow or shear motion of the powder. As in the case of spreading which is essentially a shear flow under a load, in this work, a benchtop powder rheometer with the capabilities to study bulk flow performance of AM powders is utilized to characterize a metal AM powder. When used in tandem with powder dynamics modeling, the rheometer can provide the powder rheological parameters to quantify ‘spreadability’ i.e., the ease with which a powder will spread under a specified set of conditions. Powder dynamics modeling using the discrete element method (DEM) can simulate powder spreading and capture powder layer quality descriptions such as segregation, porosity and surface roughness but needs to account for true particle shapes and sizes. The requirement to simulate realistic particle shapes and sizes results in computationally expensive DEM simulations with millions of particles. This study is therefore directed towards modeling bulk powder performance and determining the minimum volume of powder to be simulated to understand its rheological properties. This work aims to calibrate a virtual media with monodispersed spherical particles against angle of repose and rheological properties of a real media made up of almost spherical particles and comparable sizes. This calibrated media, which is the “virtual avatar” of the real powder, is also tested for rheological properties at varying load conditions and can further be made available to study the ‘spreadability’ of AM powders. The model compares well with published results for a benchmark granular media (2 mm glass beads) and has moderate accuracy for in-house experiments on a well-known AM powder (100 − 250μm Ti-6Al-4V). CPU-based serial computing is used for modeling around 8000 2 mm glass beads and subsequently, significantly faster GPU-based parallel computing is used to model 1.3 million 250μm Ti-6Al-4V powder.

DEM-aided direct shear testing of granular sands incorporating realistic particle shape

Discrete element method (DEM) of granular sands incorporating the effect of the realistic particle shape has been an important issue for many years. In this context, this study proposed a novel framework for the generation of realistic-shaped particles of natural sands in 3D DEM simulations. The generation framework mainly included micro-CT (μCT) scanning of sand particles, image processing of μCT images, spherical harmonic reconstruction of the particle surface, and clump generation by the overlapping multisphere clump method (OMCM) in DEM simulations. To validate the accuracy of OMCM, the volume and inertia moment of the clump were carefully investigated, and a set of optimized generation parameters was then determined to ensure the accuracy of the clump and the limit number of the filling spheres. Based on the generation framework, a clump sample with realistic particle shapes and a corresponding sphere sample were generated to conduct a series of direct shear testing. The simulation results demonstrated that the realistic particle shape highly increases the particle interlocking rather than the anisotropic intensity of strong contact force chains, and in turn enhances the shear resistance and the shear-induced dilation of the sands. It was also found that the inter-particle contacts of the clump sample have higher friction mobilization than that of the sphere sample, which identified the micromechanism of the shape effect on the particle interlocking.

Algorithm for Virtual Aggregates’ Reconstitution Based on Image Processing and Discrete-Element Modeling

Based on the Aggregate Imaging Measurement System (AIMS) and the Particle Flow Code in Two Dimensions (PFC2D), an algorithm for modeling two-dimensional virtual aggregates was proposed in this study. To develop the virtual particles precisely, the realistic shapes of the aggregates were captured by the AIMS firstly. The shape images were then processed, and the morphological characteristics of aggregates were quantified by the angularity index. By dividing the particle irregular shape into many triangle areas and adjusting the positions of the generated balls via coordinate systems’ conversion within PFC2D, the virtual particles could be reconstructed accurately. By calculating the mapping area, the gradations in two-dimensions could be determined. Controlled by two variables (μ_1 and μ_2), which were drawn from the uniform distribution (0, 1), the virtual particles forming the specimens could be developed with random sizes and angular shapes. In the end, the rebuilt model of the SMA-13 aggregate skeleton was verified by the virtual penetration tests. The results indicated that the proposed algorithm can not only model the realistic particle shape and gradations precisely, but also predict its mechanical behavior well.

Optimisation of blade type spreaders for powder bed preparation in Additive Manufacturing using DEM simulations

Powders used in the Particle Bed Fusion process are spread onto compact layers and are then fused to generate a layer of the final part. This process is repeated layer-upon-layer to form the final product. It has recently been demonstrated (Haeri et al., 2017) that spreading the particles with a counter-rotating roller produces a bed with a higher quality (i.e. a lower void fraction) compared to a blade type spreader. This is related to the geometry of the two spreaders which directly changes the bed-spreader contact dynamic and consequently affects the bed's quality. Based on this rationale, here, it is postulated that changing the blade profile at the blade bed contact region can significantly enhance the bed's quality and improve the effectiveness of a blade as a spreading device. A set of Discrete Element Method (DEM) simulations is performed at device-scale to optimise the geometry of blade spreaders to yield the lowest void fraction using simple rod-shaped grains to control the computational costs. The blade profile is parametrised using a super-ellipse with three geometrical parameters. Firstly, it is demonstrated that geometric optimisation of a blade profile is an effective alternative to using more complex spreading devices. Secondly, for the proposed parametrisation, the optimum values are found using computer simulations and it is shown that bed volume fractions close to critical values are achievable. Finally, a new technique for multi-sphere approximation (MSA) is developed and applied to 3D models of real powder grains to generate realistic particle shapes for the DEM simulations. Then using these grains it is shown that the proposed optimum blade profile is capable of producing a bed with qualities comparable (and even better) to a roller at the actual operating conditions and with realistic grain characteristics.

Discrete element modeling of crushable sands considering realistic particle shape effect

This study proposed a novel approach for generating crushable agglomerates with realistic particle shapes in discrete element modeling (DEM). The morphologies of sand particles were obtained by X-ray micro-computed tomography scanning and image processing. Based on the particle surface reconstructed by spherical harmonic analysis, the crushable agglomerates with realistic particle shapes can be generated in DEM simulations. The results of single particle crushing tests showed that particle shapes significantly influence the fracture patterns and crushing strengths of sand particles. Furthermore, two one-dimensional compression tests were conducted to investigate the particle shape effect on micro- and macro-mechanical behaviors of crushable sands.

Discrete element modeling of realistic particle shapes in stone-based mixtures through MATLAB-based imaging process

The objective of this paper is to present a newly-developed approach for discrete element simulation of realistic particle shapes in stone-based mixtures through a MATLAB-based imaging process and PFC5.0 Suite-based FISH codes to achieve both accuracy and efficiency. This newly-developed approach consists of three major steps, namely a MATLAB program for processing X-ray CT images of aggregates, a FISH code for generating discrete element models of individual aggregate particles, and a FISH code for creating the virtual sample of a stone-based mixture. In order to validate the newly-developed approach, parametric analysis was performed and the computational efficiencies of the ball-based model and the clump-based model were compared. It was found that both the MATLAB-based imaging processing method and the FISH codes of PFC5.0 were validated with high accuracy and efficiency.

Visual Hull Method for Realistic 3D Particle Shape Reconstruction Based on High-Resolution Photographs of Snowflakes in Free Fall from Multiple Views

AbstractA visual hull method for reconstruction of realistic 3D shapes of snowflakes and other hydrometeors based on high-resolution photographs of particles in free fall from multiple views captured by a multiangle snowflake camera (MASC), or another similar instrument, is proposed and presented. The visual hull of an object is the maximal domain that gives the same silhouettes as the object from a certain set of viewpoints. From the measured fall speed and the particle shape reconstruction, the particle density and dielectric constant are estimated. This is the first time 3D shape reconstructions based on multiple high-resolution photographs of real (measured) snowflakes are performed. The results are clearly much better than any similar data in the literature. They demonstrate—in experiments involved in real snowstorm observations and those with simulated and fake 3D-printed snowflakes—sufficient silhouette information from the five cameras of the expanded MASC system and excellent performance of the implemented mechanical calibration and software self-calibration of the system. In addition to enabling realistic “particle by particle” computations of polarimetric radar measurables for winter precipitation, the visual hull 3D shape reconstructions of hydrometeors can be used for microphysical characteristics analyses, hydrometeor classification, and improvement of radar-based estimations of liquid-equivalent snow rates.

Pore-scale modeling of water-phase fragmentation in simulated soils with realistic pore geometry

Bacterial diversity in soil is extremely high, and evidence suggests that spatial isolation created by fragmented aquatic microhabitats in unsaturated soil plays a large part in creating this diversity. Soil texture determines the extent and connectivity of hydrated microbial microhabitats through its influence on the pore size distribution. In this study we simulate pore-scale water retention processes in soils of varying textures. We develop a novel methodology for generating realistic soil particle shapes, conforming to experimentally-determined sphericity and roundness values for sand and silt particles. We then employ the lattice Boltzmann method, using a 3-D single-component multiphase model to simulate equilibrium water configurations in a series of simulated soils, and quantify the fragmentation of the water phase by identifying isolated water pockets that could serve as bacterial microhabitats. Pocket counts at a range of liquid saturations were dominated by filling of small pores created by domains of high silt content, which was a realistic depiction of the fragmentation behavior for the finest-textured soil. Fragmentation measures in the coarser soils showed sensitivity to a thresholding parameter controlling thickness of liquid films on rough surfaces of soil grains, indicating the importance of such films in controlling fragmentation in coarse soils. This work highlights the differential mechanisms controlling hydraulic fragmentation in soils of different textures: filling of pores due to capillary forces in finer soils, and disconnected water films on grain surfaces in coarser soils. Follow-on work will include grid refinement to better resolve the spatial distribution of surface films in coarse soils.

Particle-based simulations of powder coating in additive manufacturing suggest increase in powder bed roughness with coating speed

We have developed the first particle-based numerical tool to simulate the coating of powder particles in additive manufacturing devices. Our Discrete Element Method considers realistic particle shapes and incorporates attractive interaction (van-der-Waals) forces between the particles. From simulations of powder coating using a roller as coating device, we find that the surface roughness of the powder bed scales with the square of coating speed. Moreover, we find that using fine, highly polydisperse powders may lead to larger powder bed roughness, compared to process simulations using coarser powders, due to the formation of agglomerates resulting from cohesive forces.

Comparison of multi-sphere and superquadric particle representation for modelling shearing and flow characteristics of granular assemblies

In the current study, complex-shaped particles are simulated with the Discrete Element Method (DEM) using two different approaches, namely Multi-spheres (MS) and Superquadrics (SQ). Both methods have been used by researchers to represent the shape of real particles. However, despite the growing popularity of utilizing MS and SQ particles in DEM simulations, few insights have been given on the comparison of the macro scale characteristics arising from the two methods. In this respect, initially the characteristics of the two shape representation methods are evaluated in a direct shear test simulation. The results suggest that controlling the sharpness of the edges for SQ particles can lead to a good agreement with the results of MS particles. This way, a set of SQ and MS particles, which are numerically calibrated in the shear tester, are obtained. Furthermore, the macro-scale responses of the numerically calibrated particles are assessed during a slow shearing scenario, which is achieved through simulating quasi-static flow of the particles from a flat-bottom silo. The results for mass discharge, flow profile and wall pressure show a good quantitative agreement. These findings suggest that the numerically calibrated MS and SQ particles in the shear tester can provide similar bulk-scale flow properties. Moreover, the results highlight that surface bumpiness for MS particles and corner sharpness for SQ particles change the characteristics of particles and play a significant role in the shear strength of the material composed of these particles.