Image-informed numerical modelling of particulate systems with irregular grains

PhD ThesisGranular materials are everywhere around us. Their omnipresence makes our interaction with them on a daily basis a certainty, and yet our understanding of their mechanical behaviour is far from complete. Regarding geotechnical applications, most natural granular materials, such as silts, sands, gravels and ballast, feature irregular particle shapes, a fact that makes their mechanical behaviour all the more complex across scales, from micro to meso and macro. A multitude of experimental and numerical studies have demonstrated the importance of particle morphology in the shear strength of particulate materials, although rarely demonstrating a direct link or mechanisms of causality between them. This is mainly due to the high complexity of the problem but also partially due to the lack of intelligible and accessible tools to quantify the morphology of three-dimensional irregular particles. This thesis aims to contribute to the current state-of-art studying the characterisation of granular materials by providing analytical and numerical tools for shape characterisation. Regarding analytical tools, this thesis attempts a critical review of existing indices to characterise and classify particle form, while introducing a new set of indices. Regarding numerical tools, this thesis provides novel software solutions for automatic particle shape characterisation and for the generation of image-informed numerical models. These open-source tools are meant to shed light on the inherent subjectivity of performing shape characterisation on a practical level. Regarding the generation of numerical models based on imaging data, algorithmic implementations are offered to create simplified polyhedra and multi-sphere particles at user-defined fidelity levels of resolution, the morphology of which can also be characterised and compared to that of the original fidelity level. Combining the produced analytical and numerical tools, this thesis demonstrates a seamless workflow between particle imaging data and numerical modelling, using the discrete element method and non-spherical particles. This workflow is utilised to develop a methodology for the generation of Representative Element Volumes (REVs) of non-spherical particles, which represent the polydispersity of both particle size and shape, aiming to link quantitative morphology characterisation at the particle scale and mechanical characterisation at the level of a representative assembly of particles. The methodology is then applied to systematically generate REVs of railway ballast using image-informed multi-sphere particles of various levels of simulation fidelity, allowing for a parametric study of the effect of several modelling parameters on the shear strength of the material

Structural fluctuations in thin cohesive particle layers in powder-based additive manufacturing

Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion influences the characteristics of the packing structure with a focus on the fluctuation of the local morphology. As cohesion increases, the overall packing density decreases, and the free surface roughness increases, which is calculated from digitized surface height distributions. Local structural fluctuations for both quantities are examined through the local packing anisotropy on the particle scale, obtained from Vorono\"{\i} tessellation. The distributions of these particle-level metrics quantify the increasingly heterogeneous packing structure with clustering and changing surface morphology.Comment: 17 pages, 8 figure

Rigid Clumps in the MercuryDPM Particle Dynamics Code

Discrete particle simulations have become the standard in science and industrial applications exploring the properties of particulate systems. Most of such simulations rely on the concept of interacting spherical particles to describe the properties of particulates, although, the correct representation of the nonspherical particle shape is crucial for a number of applications. In this work we describe the implementation of clumps, i.e. assemblies of rigidly connected spherical particles, which can approximate given nonspherical shapes, within the \textit{MercuryDPM} particle dynamics code. \textit{MercuryDPM} contact detection algorithm is particularly efficient for polydisperse particle systems, which is essential for multilevel clumps approximating complex surfaces. We employ the existing open-source \texttt{CLUMP} library to generate clump particles. We detail the pre-processing tools providing necessary initial data, as well as the necessary adjustments of the algorithms of contact detection, collision/migration and numerical time integration. The capabilities of our implementation are illustrated for a variety of examples

DEM simulation of the powder application in powder bed fusion

The packing behavior of powders is significantly influenced by various types of inter-particle attractive forces, including adhesion and non-bonded van der Waals forces [1, 2, 3, 4, 5, 6]. Alongside particle size and shape distributions, the inter-particle interactions, in particular frictional and adhesive forces, play a crucial role in determining the flow behavior and consequently the packing density of the powder layer. The impact of various types of attractive forces on the packing density of powders with different materials and particle size distributions remains largely unexplored and requires further investigation. Accurately comprehending these effects through experiments while considering specific particle size distributions and material properties poses significant challenges. To address these challenges, we employ Discrete Element Method (DEM) simulations to characterize the packing behavior of fine powders. We can demonstrate quantitative agreement with experimental results by incorporating the appropriate particle size distribution and using an adequate model of attractive particle interactions. Furthermore, our findings indicate that both adhesion, which is modeled using the Johnson-Kendall-Roberts (JKR) model [7], and van der Waals interactions are crucial factors that must be taken into account in DEM simulations.Comment: 22 pages, 14 figure

Investigation of the near-fault directivity pulses' effect on the inelastic behavior of a curved RC bridge

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Δομοστατικός Σχεδιασμός και Ανάλυση των Κατασκευών

Imaging the root–rhizosphere interface using micro computed tomography: quantifying void ratio and root volume ratio profiles

Root growth alters soil fabric and consequently its mechanical and physical properties. Recent studies show that roots induce compaction of soil in their immediate vicinity, a region that is central for plant health. However, high quality quantification of root influence on the soil fabric, able to inform computational models is lacking from the literature. This study quantifies the relationship between soil physical characteristics and root growth, giving special emphasis on how roots in early stage formation influence the physical architecture of the surrounding soil structure. High-resolution X-ray micro-Computed Tomography (µCT) is used to acquire three dimensional images of two homogeneously-packed samples. It is observed that the void ratio profile extending from the soil-root interface into the bulk soil is altered by root growth. The roots considerably modify the immediate soil physical characteristics by creating micro cracks at the soil-root interface and by increasing void ratio. This paper presents the mechanisms that led to the observed structure as well as some of the implications that it has in such a dynamic zone

How Do Roots Interact with Layered Soils?

Vegetation alters soil fabric by providing biological reinforcement and enhancing the overall mechanical behaviour of slopes, thereby controlling shallow mass movement. To predict the behaviour of vegetated slopes, parameters representing the root system structure, such as root distribution, length, orientation and diameter, should be considered in slope stability models. This study quantifies the relationship between soil physical characteristics and root growth, giving special emphasis on (1) how roots influence the physical architecture of the surrounding soil structure and (2) how soil structure influences the root growth. A systematic experimental study is carried out using high-resolution X-ray micro-computed tomography (µCT) to observe the root behaviour in layered soil. In total, 2 samples are scanned over 15 days, enabling the acquisition of 10 sets of images. A machine learning algorithm for image segmentation is trained to act at 3 different training percentages, resulting in the processing of 30 sets of images, with the outcomes prompting a discussion on the size of the training data set. An automated in-house image processing algorithm is employed to quantify the void ratio and root volume ratio. This script enables post processing and image analysis of all 30 cases within few hours. This work investigates the effect of stratigraphy on root growth, along with the effect of image-segmentation parameters on soil constitutive properties

CLUMP: A Code Library to generate Universal Multi-sphere Particles

Particle shape plays a key role in the mechanical and rheological behaviour of particulate and granular materials. The simulation of particulate assemblies typically entails the use of Molecular Dynamics, where spheres are the predominant particle shape, and the Discrete Element Method (DEM). Clumps and clusters of spheres have been used to simulate non-spherical particles, primarily due to the simplicity of contact detection among spheres and their ability to approximate practically any irregular geometry. Various approaches have been proposed in the literature to generate such clumps or clusters, while open-source numerical codes applying these are scanty. The CLUMP code, proposed in this paper, provides a unified framework, where a particle morphology can be approximated using different clump-generation approaches from the literature. This framework allows comparing the representations of the particle generated by the different approaches both quantitatively and qualitatively, providing the user with the tools to decide which approach is more appropriate for their application. Also, one novel generation technique is proposed. Outputs are provided in formats used by some of the most popular DEM codes. Moreover, the resulting clumps can be transformed into surface meshes, allowing for easy characterisation of their morphology. Finally, the effect of clump-generation techniques on the mechanical behaviour of granular assemblies is investigated via triaxial compression tests