Documents published in Scipedia

• 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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• Finite strain elastoplastic and solid-solid contact problems with the Smoothed Particle Hydrodynamics

  D. Morikawa, M. Asai

  particles2023.

  
  

  Abstract

  The Smoothed Particle Hydrodynamics (SPH) is a numerical scheme in which the domain is discretized into Lagrangian particles in the context of continuum mechanics. It has been widely used for fluid dynamics problems, and recently it has also been applied to solid mechanics with reasonable success. In this work, we present a total Lagrangian SPH (TL-SPH) for the application of solid mechanics and contact problems. Total Langrangian stands for the usage of the reference configuration to calculate the spatial derivatives. As a consequence, all particles maintain a perfect distribution, which, in turns, results in highly accurate calculations. In this way, the method is able to eliminate any problem related to tensile instability, which is one of the main shortcomings of SPH. Here, we introduce a simple, yet robust, way to include finite strain elastoplasticity into the TL-SPH method based on the logarithmic strain. Then, the elastic part can be easily defined with the Hencky elastic model, and the plastic part with any yield criteria such as Drucker-Prager. In addition, we develop a contact algorithm capable of simulating solid-solid contact problems. In this way, the simulation of different objects becomes a mix of continuous and discontinuous problems. Finally, we show the applicability of the method with several simple tests to validate the accuracy of the TL-SPH for simulating the elastoplastic solid material, as well as for contact problems including direct impact and friction effects. REFERENCES [1] Morikawa D.S. Toward robust landslide simulations from initiation to post-failure using the Smoothed Particle Hydrodynamics. Kyushu University PhD thesis (2022).

  Abstract
  The Smoothed Particle Hydrodynamics (SPH) is a numerical scheme in which the domain is discretized into Lagrangian particles in the context of continuum mechanics. It has [...]

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• Discrete element method for combination of continuous and partical problems

  R. Varga

  particles2023.

  
  

  Abstract

  The Discrete Element Method (DEM) is a numerical approach that deals with the motion and interactions of individual elements. This method is mainly used in particle mechanics because it is overshadowed by other techniques such as the finite element method (FEM) when dealing with continuous problems. For this reason, it is not commonly known among structural engineers. However, its use can be found in cases that combine particle and continuum mechanics problems, such as crack propagation in reinforced concrete members. Calculating and optimizing these types of problems using FEM is challenging due to frequent mesh changes or the requirement for difficult-to-detect parameters in standard practice. This post discusses the possibility of using the DEM method with an extension of the beam-bound model (BBM). In this method, a beam element is inserted between the bounded discrete elements to transfer all types of forces and moments. The problem of this method is to define the correct cross-sectional and material characteristics of the individual beam members. In study, we focus on the determination of these parameters and the verification of this method on model examples of reinforced concrete elements such as simply supported and fixed beams.

  Abstract
  The Discrete Element Method (DEM) is a numerical approach that deals with the motion and interactions of individual elements. This method is mainly used in particle mechanics [...]

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• Parametric Physics-Informed Neural Networks for Material Model Calibration from Full-Field Displacement Data

  D. Anton, H. Wessels

  particles2023.

  
  

  Abstract

  The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures and materials age during their service life. This, in turn, leads to a deterioration in both the reliability and quality of the structures. The material parameters indicate possible damage and material degradation, as they directly reflect the resistance of the structure to external impacts. We further developed physics-informed neural networks (PINNs) [1] for the calibration of the linear-elastic material model from full-field displacement data and global force data in a realistic regime [2]. For a realistic data regime, the optimization problem had to be conditioned. The advantage of this method is a straightforward inclusion of observation data. Unlike grid-based methods, such as the least square finite element method approach, no computational grid and no interpolation of the data are required. However, directly solving inverse problems using PINNs is computationally expensive and prone to realistic noise levels in the measurement data. Moreover, the PINN must be trained completely from scratch for each new full-field displacement measurement, even if the geometry and material of the structure remain unchanged. In our ongoing work, we are therefore focusing on learning parameterized solutions of parametric partial differential equations using PINNs, such as [3]. We further investigate the ability of parametric PINNs to act as a surrogate for the identification of material parameters from full-field displacement data. By learning parameterized solutions, the PINN does not need to be completely re-trained for each full-field displacement measurement. The calibration of the material model can thus be drastically accelerated, and information about the material condition can be provided near real-time. Furthermore, we also plan to apply the parametric PINN to more complex material models, such as those for hyper-elastic and elasto-plastic materials.

  Abstract
  The identification of material parameters occurring in material models is essential for structural health monitoring. Due to chemical and physical processes, building structures [...]

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• DEM-BPM modelling of coated particles to investigate their crash absorbing behaviour

  W. Safdar, S. Heinrich, A. Düster

  particles2023.

  
  

  Abstract

  The collision of ships remains a significant cause of accidents, resulting in severe environmental consequences such as oil spillage from oil tankers. Enhancing the crashworthiness of ship structural design is crucial, and one approach being explored is the filling of the double hull structure with granular material [1]. Coating these particles with environmentally friendly materials can optimize their ability to absorb kinetic energy and transfer loads from the outer to the inner hull [2]. However, the mechanical behavior of the coated particles depends on the type of coating material, posing challenges in developing numerical simulation models. To address this, an open-source Discrete Element Method (DEM) code called MUSEN [3] is utilized to numerically model the behavior of coated particles. Furthermore, MUSEN can be extended using the Bonded Particle Method (BPM) to simulate particle breakage through solid bridges. However, the inclusion of coating material in the model increases the number of parameters, as well as computational time and cost. This requires a robust methodology to characterize the mechanical behaviour regardless of the type of coating material with reduced computational cost, keeping in mind the actual application inside the ship double hull. Sensitivity analyses and parametric studies are conducted to understand the effect of input parameters and identify the influential ones. Subsequently, algorithms such as the Particle Swarm Algorithm are employed for parameter optimization. Finally, models of different fidelity are used to compare the results from multi particle compression tests. The findings from these simulations will be presented in this contribution

  Abstract
  The collision of ships remains a significant cause of accidents, resulting in severe environmental consequences such as oil spillage from oil tankers. Enhancing the crashworthiness [...]

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• A Novel Parallelisation Scheme for DEM on Distributed Memory Computers

  E. Staercke, C. Nougiuer-Lehon, F. Frolio

  particles2023.

  
  

  Abstract

  Several strategies have been proposed for high performance computing with DEM. Yet, quite few works have been published on this topic (e.g., [1-3]), compared to the extensive use of DE simulations as a modelling tool in research and industry. This is however not surprising, since granular media at the grain scale appear as highly evolutive and disordered systems, which makes effective parallelisation a challenging task. We present an approach which exploits an inherent limitation of DE simulations, as the opportunity for an efficient and flexible parallel implementation on distributed memory computers. Namely, the “numerical sound speed” set by the representative particle diameter and the time step defines an upper bound for the celerity at which perturbations can propagate across the discrete medium. We show how this numerical artefact (of negligible importance for most applications) can be turned into an original criterion of spatial domain decomposition, which leads to a DEM-suited parallelisation scheme. We present our approach through its actual implementation into the DEM’ocritus code [4,5]. We analyse its performance through benchmarks and parametric analyses of biaxial tests, on assemblies of up to 15 million circular particles.

  Abstract
  Several strategies have been proposed for high performance computing with DEM. Yet, quite few works have been published on this topic (e.g., [1-3]), compared to the extensive [...]

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