Documents published in Scipedia

• A Discrete Element Analysis of the Mechanical Behaviour of an Electrode Active Layer

  A. Lundkvist, P. Larsson, E. Olsson

  particles2023.

  
  

  Abstract

  One of the main challenges in the development of lithium-ion batteries is mitigating the decrease in charge capacity over time. The loss of charge capacity in lithium-ion batteries stem from different phenomena, one of which is mechanical degradation. This study uses the discrete element method (DEM) to investigate the mechanical properties of a positive electrode layer. The goal is to link the local mechanical behaviour, on the particle scale, to the global behaviour of the electrode layer. Understanding the coupling between length scales is crucial for understanding and reducing the mechanical degradation and as the active particles, and the binder connecting it, form a granular structure in the electrode layer, DEM is a well-suited method to apply. The DEM model developed considered both interaction between active particles, as well as interaction between particles separated by binder. This study targeted to replicate the in-plane unloading stiffness of the electrode layer, which had been measured experimentally by Gupta et al. [1] through a U-shape bending test. The experiments measured the stiffness both in compression and in tension at various strain levels and load rates. The developed model was able to capture the constant stiffness in tension at different strain levels and the stiffness increase at higher compression levels markedly. The viscoelastic behaviour of the layer, with an increased stiffness at increased load rates, could be captured quantitatively by increasing the binder stiffness. This work lays an excellent foundation for further investigations of the mechanical properties of the active layer and its mechanical degradation mechanisms, such as viscoelasticity in the binder and swelling and fracture of the active particles.

  Abstract
  One of the main challenges in the development of lithium-ion batteries is mitigating the decrease in charge capacity over time. The loss of charge capacity in lithium-ion [...]

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• Investigation of the draw down test method for the calibration of the DEM sliding friction and rolling friction parameters of a cohesionless bulk material

  J. Marín, T. Comlekci, D. MacKenzie, Y. Gorash

  particles2023.

  
  

  Abstract

  Discrete Element Method (DEM) is a numerical method that evaluates the interaction between particles and particle-boundaries [1]. The accuracy of the DEM simulations depends on several interaction parameters such as sliding friction and rolling friction coefficients. Most recent studies have shown that a draw down test can provide four criteria: angle of repose, shear angle, mass in the upper/lower box after test is done and mass flow rate that were used to calibrate bulk material sliding friction and rolling friction coefficients [2-3]. In general, these studies have used a fix aperture size of the upper box leading to a specific behaviour flow regime. In this study, three different flow regimes have been studied to evaluate if one set of unique parameters can reproduce the same response for these scenarios. A draw down test was carried out using gravel with particle size between 8-12.5 mm. The aperture size used were 50, 100 and 150 mm. During the test, the transient load was measured using a load cell in the upper box and pictures were taken after the test was done. DEM simulations were performed using the EDEM software where a single spherical and multi-sphere particle shape were used to obtain draw down calibration parameters by varying sliding friction and rolling friction parameters between 0.1 to 0.8 with an step of 0.1. When overlapping the four bulk criteria from 100 and 150 mm in aperture size using single sphere, it was possible to obtain a unique set of parameters. However, for 50 mm there was no overlapping which may indicate that using single sphere might not be accurate for all flow regimes. When using multi-sphere particle, the overlap led to a wide range of sliding friction coefficient for all cases, and although when overlapping the three scenarios there was no feasible region, it shows a tendency where a unique set of parameters might provide an approximate solution for all cases.

  Abstract
  Discrete Element Method (DEM) is a numerical method that evaluates the interaction between particles and particle-boundaries [1]. The accuracy of the DEM simulations depends [...]

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• Particle-Level Experiments of Non-Dilative Interfaces: Accurate Input Parameters for Numerical Simulations

  L. Kandpal, P. Vangla

  particles2023.

  
  

  Abstract

  The development of more accurate force prediction models developed through particle-level experiments is required to accurately model non-dilative interfaces from micro to macro. Further, selecting reliable input parameters for DEM remains a challenge. Thus, micromechanical experimental studies are of fundamental importance that can provide insights into microscale aspects for in-depth knowledge of non-dilative interfaces. This study presents custom-designed, reliable, and sensitive equipment that facilitates shear tests for non-dilative interfaces under different configurations that simulate suitable conditions for geotechnical applications. This research offers a logical rationale for the non-dilative interface system's observed shear behavior.

  Abstract
  The development of more accurate force prediction models developed through particle-level experiments is required to accurately model non-dilative interfaces from micro to [...]

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• Development and Validation of a High Order Finite Element CFD-DEM Solver

  T. Geitani, S. Golshan, B. Blais

  particles2023.

  
  

  Abstract

  Currently existing computational fluid dynamics-discrete element method (CFD-DEM) solvers suffer from computationally expensive coupling between the CFD and DEM as it requires calculating at each fluid time-step the void fraction and the solid-fluid forces such as drag, lift, buoyancy, and undisturbed flow forces. We develop a unified finite element CFD-DEM solver which integrates the CFD and DEM solvers into a single software resulting in faster and cheaper coupling. Our fluid formulation is stabilized using tailored techniques to prevent oscillations in regions of sharp gradients, to enhance the robustness of the formulation and local mass conservation, and to relax the Ladyzhenskaya-Babuska-Brezzi inf-sup condition. The developed solver supports high order finite elements resulting in better accuracy with larger cell sizes. Moreover, our solver supports dynamic load balance parallelization for both the particles and the fluid. This evens the distribution of workloads among processors, resulting in better efficiency and resource exploitation. Additionally, we develop a new spatially and temporally continuous analytical void fraction scheme called Quadrature-Centered Method (QCM). This scheme results in less computational time, better accuracy and convergence, and enhanced mass conservation. It also enables the use of very small CFD time-steps thus achieving better temporal accuracy and the use of mesh sizes smaller than those commonly used in CFD-DEM (< 3 times the particle diameter.) We validate our solver through several cases among which we will discuss a spouted bed test case where particle velocities matched those of the experiments, a particle sedimentation test case where we study the effect of the void fraction scheme choice on the particle’s terminal velocity, and a particle Rayleigh-Taylor Instability where the particles constituted the heavy phase and where we study the evolution of the mixing layer with time.

  Abstract
  Currently existing computational fluid dynamics-discrete element method (CFD-DEM) solvers suffer from computationally expensive coupling between the CFD and DEM as it requires [...]

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• Assessing the Impact of Relative Motion on Hydrodynamic Force Interactions between Particles Pair

  L. Barbeau, B. Blais, C. Béguin, S. Éthienne

  particles2023.

  
  

  Abstract

  The hydrodynamic interactions between particles have significant effects in many engineering fields, such as fluidized beds and slurry sedimentation. This is because they can impact the macroscopic process parameters of these systems (e.g., particles' cluster sedimentation speed) [1-2]. To better understand the hydrodynamic interaction between particles and its effects on macroscopic process parameters, it is necessary to understand the hydrodynamic force interactions between individual particles as a function of their relative position and velocity. The first step in this direction is the interaction between a pair of particles, which remains an active area of research. It is established that drag and lift forces applied on a particle change in function of its relative position and velocity to another particle. However, the impact of these changes on their dynamics is limited. We first study the effects of the relative position between the particles and the Reynolds number on the drag and lift forces applied to them to constitute a pairwise fluid force model. This force model is the basis for a new reduced-order particle dynamics model that also includes lubrication, Basset, and added mass forces. We compare the results obtained for a series of sedimentation cases with the reduced-order model with those obtained from a resolved computational fluid dynamic solver coupled with a discrete element method (CFD-DEM) [3]. Comparing the fluid forces obtained between these to the model enables us to assess the impact of the particles' relative motion on the hydrodynamic forces applied to them (e.g., drag, lift, and forces). These sedimentation cases also allow us to evaluate the effects of the density ratio between the particle and the fluid on the virtual mass force and its impact on the dynamic of the particles. This study is a stepping stone toward a complete model for hydrodynamic forces in particle clusters.

  Abstract
  The hydrodynamic interactions between particles have significant effects in many engineering fields, such as fluidized beds and slurry sedimentation. This is because they [...]

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• DEM and CFD-DEM: From established technology to new frontiers

  C. Kloss, A. Mayrhofer, M. Niemann, A. Aigner, P. Seil, M. Kwakkel, C. Govina

  particles2023.

  
  

  Abstract

  Discrete Element Method (DEM) and DEM coupled to Computational Fluid Dynamics (CFD-DEM) are established techniques for optimization and design of particle processes. Its applicability to a wide range of processes has been proven for many different industrial and environmental applications. The extension to new fields and processes has been made possible by continuous improvements of: (i) models, (ii) numerical methods and (iii) computational performance. DEM and CFD-DEM have evolved from pure “particle modelling tools” to numerical tools to model “particulate flow””. Combining the Lagrangian nature of discretization with complex interaction models, the behaviour of viscous pastes, compressible powders, melting polymers just to name a few, has become feasible. Additionally much attention has been given to improvement of numerical aspects, which led to improved stability and therefore applicability of the models. Last but not least, the computational efficiency and possibility to make use of available computational resources has boosted the technology to new levels. The authors give their perspective on some corner-stones and highlights in modelling and development that were made in the past few years, which lead to this break-through and give some concrete examples of current state of the art modelling capability. Based on this solid foundation that has been build, new goals are within reach and the authors will give some insight on future opportunities for this modelling technology.

  Abstract
  Discrete Element Method (DEM) and DEM coupled to Computational Fluid Dynamics (CFD-DEM) are established techniques for optimization and design of particle processes. Its applicability [...]

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• Learning Physics-consistent Particle Interactions

  Z. Han, O. Fink, D. Kammer

  particles2023.

  
  

  Abstract

  Interacting particle systems are ubiquitous in nature and engineering. Access to the governing particle interaction law is of fundamental importance for a complete understanding of such systems. However, it is particularly challenging to extract this information from experimental observations due to the intricate configuration complexities involved. Machine learning methods have the potential to learn the behavior of interacting particle systems by combining experiments with data analysis methods. However, most existing algorithms focus on learning the kinetics at the particle level and do not learn the pairwise interactions specifically. Moreover, in reality, interacting particle systems are often heterogeneous, where multiple interaction types coexist simultaneously and relational inference is required. An approach that can simultaneously reveal the hidden pairwise interaction types and infer the unknown heterogeneous governing interactions constitutes a necessary advancement for our understanding of particle systems. However, this task is considerably more challenging than its homogeneous counterpart. Here, we propose the physics-induced graph network for particle interaction (PIG'N'PI) allowing to precisely infer the pairwise interactions that are consistent with underlying physical laws by only being trained to predict the particle acceleration for homogeneous systems. We further propose a novel method for relational inference which combines probabilistic inference and PIG'N'PI to learn different kinds of interactions for heterogeneous systems. We test the proposed methodologies on multiple benchmark datasets and demonstrate that the learnt interactions are consistent with the underlying physics and the proposed relational inference method achieves superior performance in correctly inferring interaction types. In addition, the proposed model is data-efficient and generalizable to large systems when trained on small systems, contrary to previously proposed solutions. The developed methodology constitutes a key element for the discovery of the fundamental laws that determine macroscopic mechanical properties of particle systems.

  Abstract
  Interacting particle systems are ubiquitous in nature and engineering. Access to the governing particle interaction law is of fundamental importance for a complete understanding [...]

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• DEM Study Investigating the Effect of Particle Shape on Compaction of Realistic Non-Spherical Particles Using Convolutional Neural Network

  K. Giannis

  particles2023.

  
  

  Abstract

  This study focused on investigating the deformation behavior of non-spherical particles under high-load compaction, utilizing the multi-contact discrete element method (MC-DEM). To account for the non-spherical shape of the particles, two methods were employed: the bonded multi-sphere method (BMS) and the conventional multi-sphere (CMS). The BMS approach yielded accurate results in predicting the compression behavior of a single rubber sphere, while the CMS method failed to replicate the same behavior. Building on these findings, the BMS method was utilized to study the uniaxial compaction of Avicel® PH 200, a popular choice of excipient due to its ability to enhance the stability, flowability, and compressibility of tablet formulations. The results obtained from this study showed very good agreement with experimental data. To generate realistic 3D models of particles, a novel approach was introduced, which combines 2D projections and deep learning algorithms utilizing a 3D convolutional neural network (3D-CNN) methodology. Surrogate model was used to overcome the computational cost of DEM simulations. The findings of this study offer a valuable tool for researchers and engineers to efficiently and accurately generate 3D models of particles, leading to new insights and innovations in a range of applications such as rock and mineral analysis, battery materials, pharmaceuticals, and space exploration.

  Abstract
  This study focused on investigating the deformation behavior of non-spherical particles under high-load compaction, utilizing the multi-contact discrete element method (MC-DEM). [...]

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• Particle-based Modeling of Metal Powder Additive Manufacturing

  M. Liu

  particles2023.

  
  

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• Adaptive Data-Driven Modeling of Complex Systems

  J. Andrade

  particles2023.

  
  

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