3D Multi-Scale Behavior of Granular Materials using Experimental and Numerical Techniques

Constitutive modeling of granular material behavior has generally been based on global response of laboratory-size specimens or larger models with little understanding of the fundamental mechanics that drive the global response. Many studies have acknowledged the importance of micro-scale and meso-scale mechanics on the constitutive behavior of granular materials. However, much knowledge is still missing to develop and improve robust micromechanical constitutive models. The research in this dissertation contributes to this knowledge gap for many potential applications using novel experimental techniques to investigate the three-dimensional (3D) behavior of granular materials. Critical micromechanics measurements at multiple scales are investigated by combining 3D synchrotron micro-computed tomography (SMT), 3D image analysis, and finite element analysis (FEA). At the single particle level (micro-scale), particle fracture was examined at strain rates of 0.2 mm/min and 2 m/s using quasi-static unconfined compression, unconfined mini-Kolsky bar, and x-ray imaging techniques. Surface reconstructions of particles were generated and exported to Abaqus FEA software, where quasi-static and higher rate loading curves and crack propagation were simulated with good accuracy. Stress concentrations in oddly shaped particles during FEA simulations resulted in more realistic fracture stresses than theoretical models. A nonlinear multivariable statistical model was developed to predict force required to fracture individual particles with known internal structure and loading geometry. At the meso-scale, 3D SMT imaging during in-situ triaxial testing of granular materials were used to identify particle morphology, contacts, kinematics and interparticle behavior. Micro shear bands (MSB) were exposed during pre-peak stress using a new relative particle displacement concept developed in this dissertation. MSB for spherical particles (glass beads) had larger thickness (3d50 to 5d50) than that of angular sands (such as F35 Ottawa sand, MSB thickness of 1d50 to 3d50). Particle morphology also plays a significant role in the onset and growth of shear bands and global fabric evolution of granular materials. More spherical particles typically exhibit more homogeneous internal anisotropy. Fabric of particles within the shear band (at higher densities and confining pressures) exhibits a peak and decrease into steady-state. Also, experimental fabric produces more accurate strength and deformation predictions in constitutive models that incorporate fabric evolution

SMARTI - Sustainable Multi-functional Automated Resilient Transport Infrastructure

The world’s transport network has developed over thousands of years; emerging from the need of allowing more comfortable trips to roman soldiers to the modern smooth roads enabling modern vehicles to travel at high speed and to allow heavy airplanes to take off and land safely. However, in the last two decades the world is changing very fast in terms of population growth, mobility and business trades creating greater traffic volumes and demand for minimal disruption to users, but also challenges, such as climate change and more extreme weather events. At the same time, technology development to allow a more sustainable transport sector continue apace. It is within this environment and in close consultation with key stakeholders, that this consortium developed the vision to achieve the paradigm shift to Sustainable Multifunctional Automated and Resilient Transport Infrastructures. SMARTI ETN is a training-through-research programme that empowered Europe by forming a new generation of multi-disciplinary professionals able to conceive the future of transport infrastructures and this Special Issue is a collection of some of the scientific work carried out within this context. Enjoy the read

Radiomics risk modelling using machine learning algorithms for personalised radiation oncology

One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approach for the correction of intensity non-uniformity in magnetic resonance imaging data was evolved to improve image quality prior to feature computation. Further, different feature selection methods and machine learning algorithms for time-to-event survival data were evaluated to identify suitable algorithms for radiomics risk modelling. An improved model performance could be demonstrated using computed tomography data, which were acquired during the course of treatment. Subsequently tumour sub-volumes were analysed and it was shown that the tumour rim contains the most relevant prognostic information compared to the corresponding core. The incorporation of such spatial diversity information is a promising way to improve the performance of risk models.:1. Introduction 2. Theoretical background 2.1. Basic physical principles of image modalities 2.1.1. Computed tomography 2.1.2. Magnetic resonance imaging 2.2. Basic principles of survival analyses 2.2.1. Semi-parametric survival models 2.2.2. Full-parametric survival models 2.3. Radiomics risk modelling 2.3.1. Feature computation framework 2.3.2. Risk modelling framework 2.4. Performance assessments 2.5. Feature selection methods and machine learning algorithms 2.5.1. Feature selection methods 2.5.2. Machine learning algorithms 3. A physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging 3.1. Intensity non-uniformity correction methods 3.2. Physical correction model 3.2.1. Correction strategy and model definition 3.2.2. Model parameter constraints 3.3. Experiments 3.3.1. Phantom and simulated brain data set 3.3.2. Clinical brain data set 3.3.3. Abdominal data set 3.4. Summary and discussion 4. Comparison of feature selection methods and machine learning algorithms for radiomics time-to-event survival models 4.1. Motivation 4.2. Patient cohort and experimental design 4.2.1. Characteristics of patient cohort 4.2.2. Experimental design 4.3. Results of feature selection methods and machine learning algorithms evaluation 4.4. Summary and discussion 5. Characterisation of tumour phenotype using computed tomography imaging during treatment 5.1. Motivation 5.2. Patient cohort and experimental design 5.2.1. Characteristics of patient cohort 5.2.2. Experimental design 5.3. Results of computed tomography imaging during treatment 5.4. Summary and discussion 6. Tumour phenotype characterisation using tumour sub-volumes 6.1. Motivation 6.2. Patient cohort and experimental design 6.2.1. Characteristics of patient cohorts 6.2.2. Experimental design 6.3. Results of tumour sub-volumes evaluation 6.4. Summary and discussion 7. Summary and further perspectives 8. Zusammenfassun

Experimental Investigation of Clay Aggregate and Granular Biofilm Behavior

Clay minerals are a class of phyllosilicates as the major solid constituents in cohesive fine-grained soils (e.g., clays). Owing to their tiny size (i.e., \u3c 2 μm), high aspect ratio, and active surface properties that inherit from the geological process, clay minerals can extensively interact with other suspended matter (e.g., exoployemers, microorganisms) and dissolved ions via the process of flocculation and aggregation, resulting in the formation of larger, porous cohesive particulate aggregates or flocs. Such a complex mechanism of microscale particle interaction generates significant challenges for understanding the bulk clay behavior as a particulate system. In order to better characterize the flocculation and aggregation of clay minerals under various stimuli and to understand the underlying mechanism of particle interactions, particle/aggregate size kinetics of flocculated suspensions of illite, a representative 2:1 clay mineral abundant in marine soils, are studied with varied ionic strength induced by monovalent salt (NaCl), pH, and hydrodynamic shearing in the first phase of this research. Furthermore, a new statistical data binning method termed “bin size index” (BSI) was employed to determine the probability density function (PDF) distributions of flocculated illite suspensions. The statistical results demonstrate that the size kinetics of flocculated illite suspensions is chiefly controlled by the face-to-face and edge-to-face interparticle interactions under the mutual effects of ionic strength and pH, while the hydrodynamic shearing has minimal effects on the variation of particle size groups. In the second phase of this research, the mechanics of clay aggregates are studied using an innovative measurement technique and analytical approach. Individual clay minerals prepared with different mineralogy and salinities are tested via unconfined compression, which shows that the increasing ionic strength can improve the strength and stiffness of clay aggregates, which are further affected by the mineralogical compositions and dominant microfabric in different water chemistry. In the final phase of this research, a collaborative study with an environmental engineer on an NSF CAREER project was conducted to investigate the mechanical behavior of macroscale, light-induced oxygenic granules (biofilm aggregates) using the same technique and analytics developed for the individual clay aggregates. The findings are expected to provide reference values to subsequent studies and engineering practices associated with the water treatment process

Microstructure Characterization of Continuous-Discontinuous Fibre Reinforced Polymers based on Volumetric Images

Die quantitative Beschreibung der Mikrostruktur von Faserverbundwerkstoffen ist elementar für die Modellierung von thermischen und mechanischen Eigenschaften. Durch die stetige Entwicklung der Computertomographie ist es heute möglich dreidimensionale Bilddaten von Werkstoffen mit einer Auflösung von unter einem Mikrometer zu erzeugen. Moderne Computersysteme bieten ausreichend Rechenleistung um die resultierenden volumetrischen Bilddaten automatisiert auszuwerten und relevante Statistiken zu erzeugen. Die vorliegende Arbeit befasst sich mit der Quantifizierung von mikrostrukturellen Merkmalen von faserverstärkten Polymeren unter Verwendung von computertomographischen Aufnahmen. Diverse Verfahren zur Bestimmung von lokalen Faserorientierungen, -volumengehalt, -krümmungen und -längen wurden implementiert und validiert. Des Weiteren wurden zwei Ansätze zur Berechnung von lokalen Oberflächenkrümmungen zur Porositätsanalyse verglichen. Die Ergebnisse zeigen, dass einige der bereits verfügbaren Orientierungsanalyseverfahren bereits sehr robust sind und auch mit stark verrauschten Aufnahmen mit geringem Kontrast sehr gute Resultate erzielen. Faserlängenverteilungen, die mittels Fasertrackingverfahren aus computertomographischen Aufnahmen extrahiert wurden lieferten nur bis zu einer Probengröße von 5mm verlässliche Faserlängenverteilungen und sind daher nur bedingt für die Anwendung an langfaserverstärkten Polymeren geeignet

Simulation Modeling

The book presents some recent specialized works of a theoretical and practical nature in the field of simulation modeling, which is being addressed to a large number of specialists, mathematicians, doctors, engineers, economists, professors, and students. The book comprises 11 chapters that promote modern mathematical algorithms and simulation modeling techniques, in practical applications, in the following thematic areas: mathematics, biomedicine, systems of systems, materials science and engineering, energy systems, and economics. This project presents scientific papers and applications that emphasize the capabilities of simulation modeling methods, helping readers to understand the phenomena that take place in the real world, the conditions of their development, and their effects, at a high scientific and technical level. The authors have published work examples and case studies that resulted from their researches in the field. The readers get new solutions and answers to questions related to the emerging applications of simulation modeling and their advantages

Role of food material properties on the mechanisms of solid food disintegration during gastric digestion : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Food Technology at Massey University, Palmerston North, New Zealand

The stomach is, after the mouth, the major organ for the breakdown of foods by a complex interaction of biochemical and mechanical mechanisms driven by the diffusion of gastric juice and the peristaltic activity of the stomach. The degree of fragmentation of solid food in the stomach and consequent release of nutrients is largely dependent on the food material properties. Despite extensive research directed at the gastric digestion, the establishment of the proper relationship between the initial material properties of foods and their subsequent breakdown during gastric digestion is still far from being fully understood. To bridge the aforementioned knowledge gap, the aim of this thesis was to characterise the relationship between material properties of solid foods (composition and structure) and their disintegration behaviour in the stomach. Sweet potato (steamed and fried) and egg white gels (pH 5 and pH 9 EWGs) were used as starch and protein based-product models, respectively, to develop experimental models to characterise not only the diffusion of gastric juice into the food matrix, but also the mechanisms underlying the biochemical and mechanical degradation of the food matrix during in vitro gastric digestion. Overall results revealed that the porous network created during frying facilitated a faster gastric acid penetration into the sweet potato food matrix than occurred in the less porous steamed sweet potato. Consequently, the fried sweet potato matrix underwent a faster collapsing and quicker softening time during in vitro gastric digestion than the more compact and denser structure of steamed sweet potato. This led to the faster disintegration and subsequent release of β-carotene in the human gastric simulator from the fried sweet potato matrix. A similar effect was demonstrated with the EWG, where the loose protein network of pH 5 EWG exhibited a significantly higher rate of pepsin diffusion, softening, nutrient release and mechanical breakdown compared to the more tightened gel microstructure found in the pH 9 EWG. In conclusion, gastric disintegration and nutrient release within the solid food structures are mainly controlled by the initial food microstructure and composition. Such knowledge will help to identify key factors for the designing of health-promoting food formulations

Large-Area Multi-Breakdown Characterization of Polymer Films: A New Approach for Establishing Structure–Processing–Breakdown Relationships in Capacitor Dielectrics

The ever-growing need for high-energy density and high operation temperature capacitive energy storage for next-generation applications has necessitated research and development on new dielectric materials for film capacitors. Consequently, various new approaches offering unique ways to tailor dielectric properties of polymers have recently emerged, and new materials such as dielectric polymer nanocomposites (PNC) are envisioned as potential next-generation dielectrics. Establishment of optimized formulation and processing conventions is however necessary in order to achieve improvement in dielectric breakdown properties. Importantly however, such material development puts dielectric breakdown strength assessment of polymer films in a central role in guiding material development process towards highly optimized functional materials. This is not a trivial task though, as the current state-of-the-art breakdown strength measurement techniques rarely provide statistically sufficient amounts of breakdown data from the application point-of-view, thus leading to impaired evaluation of the practical breakdown performance in film capacitors.In this thesis, a new large-area multi-breakdown measurement method enabling detailed dielectric breakdown characterization of polymer films is developed and evaluated. Various aspects encompassing sample film preparation, measurement procedure, breakdown progression, discharge event characterization, breakdown field determination, data validation and statistical analysis are systematically and critically discussed. A data qualification process based on the self-healing discharge energy and breakdown voltage characteristics is developed and shown to be a sensible and convenient way to exclude non-breakdown events from the measurement data prior to Weibull statistical analysis. The measurement method is shown to enable high-statistical-accuracy breakdown characterization of both metallized and non-metallized polymer films of different nature, including laboratory-scale, pilot-scale and commercial-grade capacitor films. Statistical aspects on the area dependence are discussed and the problematic nature of Weibull area-extrapolation of small-area breakdown data to represent larger film areas is exemplified. The fundamental differences between the large-area multi-breakdown and the small-area single-breakdown measurement methods and the statistical aspects thereof are analyzed in more detail by the Monte Carlo simulation method.The large-area multi-breakdown method is utilized to carry out a comprehensive analysis on structure--processing--breakdown relationships in conventional polymer and polymer nanocomposite films. Analysis on the effects of film processing, structure and morphology on the large-area multi-breakdown response of cast- and bi-axially oriented isotactic polypropylene (PP) films emphasizes the determining effect of processing-dependent film morphology in large-area dielectric breakdown response of PP films. Commercial capacitor-grade bi-axially oriented polypropylene (BOPP) films are shown to exhibit differences in breakdown distribution structure and weak point behavior in comparison to the laboratory-scale BOPP films, presumably due to differences in raw material, additives, thermal history and processing. Breakdown characterization of commercial metallized polymer films as a function of inter-layer pressure also emphasizes the importance of careful breakdown characterization under authentic operation stresses in order to ensure proper design and operation in practical applications.BOPP-based polymer nanocomposite (PNC) films are studied with a particular emphasis on the effects of various compositional, structural and film processing factors on the breakdown behavior of laboratory- and pilot-scale melt-compounded BOPP nanocomposite films incorporating silica and/or calcium carbonate nanofillers. The optimum nano-filler content is found to reside at the low fill-fraction range (~1 wt-%), however, the fill-fraction itself is not the only determining factor, as compounds with equal nanoparticle content but with differences in e.g. compounder screw speed are found to exhibit large differences in breakdown response. Indications of possible silica-antioxidant interaction are also reported. Structural imaging of the films shows that nano-structural features cannot solely explain the observed large-area breakdown behavior – this aspect points towards large-area approach being necessary for the imaging techniques as well in order to reliably establish a link between structural properties and engineering breakdown strength. The results point out that up-scaling of PNC production is sensible with conventional melt-blending technology, although further development and optimization of nanocomposite formulations and processing are seen as necessary. Analysis on the ramp-rate-dependence of the breakdown response in dielectric polymer nanocomposite films also provides perspective on the importance of careful breakdown assessment when dielectric films of more complex internal structure are studied

The Development of Regional Forest Inventories Through Novel Means

For two decades Light Detection and Ranging (LiDAR) data has been used to develop spatially-explicit forest inventories. Data derived from LiDAR depict three-dimensional forest canopy structure and are useful for predicting forest attributes such as biomass, stem density, and species. Such enhanced forest inventories (EFIs) are useful for carbon accounting, forest management, and wildlife habitat characterization by allowing practitioners to target specific areas without extensive field work. Here in New England, LiDAR data covers nearly the entire geographical extent of the region. However, until now the region’s forest attributes have not been mapped. Developing regional inventories has traditionally been problematic because most regions – including New England – are comprised of a patchwork of datasets acquired with various specifications. These variations in specifications prohibit developing a single set of predictive models for a region. The purpose of this work is to develop a new set of modeling techniques, allowing for EFIs consisting of disparate LiDAR datasets. The work presented in the first chapter improves upon existing LiDAR modeling techniques by developing a new set of metrics for quantifying LiDAR based on ecological ii principles. These fall into five categories: canopy height, canopy complexity, individual tree attributes, crowding, and abiotic. These metrics were compared to those traditionally used, and results indicated that they are a more effective means of modeling forest attributes across multiple LiDAR datasets. In the following chapters, artificial intelligence (AI) algorithms were developed to interpret LiDAR data and make forest predictions. After settling on the optimal algorithm, we incorporated satellite spectral, disturbance, and climate data. Our results indicated that this approach dramatically outperformed the traditional modeling techniques. We then applied the AI model to the region’s LiDAR, developing 10 m resolution wall-to-wall forest inventory maps of fourteen forest attributes. We assessed error using U.S. federal inventory data, and determined that our EFIs did not differ significantly in 33, 25, and 30/38 counties when predicting biomass, percent conifer, and stem density. We were ultimately able to develop the region’s most complete and detailed forest inventories. This will allow practitioners to assess forest characteristics without the cost and effort associated with extensive field-inventories