Granular materials demonstrate complexity in many physical attributes with various shapes and sizes, varying from several centimeters down to less than a few microns. Some materials are highly cohesive, while others are free-flowing. Despite such complexity in their physical properties, they are extremely important in industries dealing with bulk materials. Through this research, the factors affecting flowability of particulate solids and their interaction with projectiles were explored. In Part I, a novel set of characterization tools to relate various granular material properties to their flow behavior in confined and unconfined environments was investigated. Through this work, a thorough characterization study to examine the effects of particle size, particle size distribution, and moisture on bulk powder flowability were proposed. Additionally, a mathematical model to predict the flow function coefficient (FFC) was developed, based on the surface mean diameter and moisture level, which can serve as a flowability descriptor. Part II of this research focuses on the impact dynamics of low velocity projectiles on granular media. Interaction of granular media with external foreign bodies occurs in everyday events like a human footprint on the beach. Several studies involving numerical and experimental methods have focused on the study of impact dynamics in both dry and wet granular media. However, most of the studies involving impact dynamics considered spherical projectiles under different conditions, while practical models should involve more complex, realistic shapes. Different impacting geometries with conserved density, volume, and velocity on a granular bed may experience contrasting drag forces upon penetration. This is due to the difference in the surface areas coming into contact with the granular media. In this study, a set of non-spherical geometries comprising cuboids, cylinders, hexagonal prisms and triangular prisms with constant density, volume, and impact velocities, were released onto a loosely packed, non-cohesive, dry granular bed. From these experimental results, a model to determine the penetration depth of projectiles upon impact was developed and how it is influenced by the release height and surface area of the projectiles in contact with the granular media was studied.

The way a granular material is transported and handled plays a huge part in the quality of final product and the overall efficiency of the manufacturing process. Currently, there is a gap in the understanding of the basic relationship between the fundamental variables of granular materials such as moisture content, particle shape and size. This can lead to flowability issues like arching and ratholing, which can lead to unexpected downtimes in the whole manufacturing process and considerable wastage of time, energy, and resources. This study specifically focuses on the development of a model based on the surface mean diameter and the moisture content to predict the flow metric flow function coefficient (FFC) to describe the nature of flow of the material. The investigation involved three parts. The first entailed the characterization of the test materials with respect to their physical properties - density, size, and shape distributions. In the second, flowability tests were conducted with the FT4 Powder Rheometer. Shear cell tests were utilized to calculate each test specimen's flow function parameters. Finally, the physical properties were correlated with the results from the flowability tests to develop a reliable model to predict the nature of flow of the test specimens. The model displayed an average error of -6.5%. Predicted values showed great correlation with values obtained from further shear cell tests on the FT4 Rheometer. Additionally, particle shape factors and other flowability descriptors like Carr Index and Hausner Ratio were also evaluated for the sample materials. All size ranges displayed a decreasing trend in the values of Carr Index, Hausner Ratio, and FFC with increasing moisture percentages except the 5-11 micron glass beads, which showed an increasing trend in FFC. The results from this investigation could be helpful in designing equipment for powder handling and avoiding potential flowability issues.

2D materials with reduced symmetry have gained great interest in the past decade due to the arising quantum properties introduced by the structural asymmetry. A particular example is called 2D Janus materials. Named after Roman god Janus with two faces, Janus materials have different chemical compositions on the two sides of materials, leading to a structure with broken mirror symmetry. Electronegativity difference of the facial elements induces a built-in polarization field pointing out of the plane, which has driven a lot of theory predictions on Rashba splitting, high- temperature ferromagnetism, Skyrmion formation, and so on. Previously reported experimental synthesis of Janus 2D materials relies on high-temperature processing, which limits the crystallinity of as produced 2D layers. In this dissertation, I present a room temperature selective epitaxial atomic re- placement (SEAR) method to convert CVD-grown transition metal dichalcogenides (TMDs) into a Janus structure. Chemically reactive H2 plasma is used to selectively etch off the top layer of chalcogen atoms and the introduction of replacement chalco- gen source in-situ allows for the achievement of Janus structures in one step at room temperature. It is confirmed that the produced Janus monolayers possess high crys- tallinity and good excitonic properties. Moving forward, I show the fabrication of lateral and vertical heterostructures of Janus materials, which are predicted to show exotic properties because of the intrinsic polarization field. To efficiently screen other kinds of interesting Janus structures, a new plasma chamber is designed to allow in-situ optical measurement on the target monolayer during the SEAR process. Successful conversion is seen on mechanically exfoliated MoSe2 and WSe2, and insights into reaction kinetics are gain from Raman spectra evolution. Using the monitoring ability, Janus SNbSe is synthesized for the first time. It’s also demonstrated that the overall crystallinity of as produced Janus monolayer SWSe and SMoSe are correlated with the source of monolayer TMDs. Overall, the synthesis of the Janus monolayers using the described method paves the way to the production of highly crystalline Janus materials, and with the in-situ monitoring ability, a deeper understanding of the mechanism is reached. This will accelerate future exploration of other Janus materials synthesis, and confirmation and discovery of their exciting quantum properties.

Accurate knowledge and understanding of thermal conductivity is very important in awide variety of applications both at microscopic and macroscopic scales. Estimation,however varies widely with respect to scale and application. At a lattice level, calcu-lation of thermal conductivity of any particular alloy require very heavy computationeven for a relatively small number of atoms. This thesis aims to run conventionalmolecular dynamic simulations for a particular supercell and then employ a machinelearning based approach and compare the two in hopes of developing a method togreatly reduce computational costs as well as increase the scale and time frame ofthese systems. Conventional simulations were run using interatomic potentials basedon density function theory-basedab initiocalculations. Then deep learning neuralnetwork based interatomic potentials were used run similar simulations to comparethe two approaches.

Extensive efforts have been devoted to understanding material failure in the last several decades. A suitable numerical method and specific failure criteria are required for failure simulation. The finite element method (FEM) is the most widely used approach for material mechanical modelling. Since FEM is based on partial differential equations, it is hard to solve problems involving spatial discontinuities, such as fracture and material interface. Due to their intrinsic characteristics of integro-differential governing equations, discontinuous approaches are more suitable for problems involving spatial discontinuities, such as lattice spring method, discrete element method, and peridynamics. A recently proposed lattice particle method is shown to have no restriction of Poisson’s ratio, which is very common in discontinuous methods. In this study, the lattice particle method is adopted to study failure problems. In addition of numerical method, failure criterion is essential for failure simulations. In this study, multiaxial fatigue failure is investigated and then applied to the adopted method. Another critical issue of failure simulation is that the simulation process is time-consuming. To reduce computational cost, the lattice particle method can be partly replaced by neural network model.First, the development of a nonlocal maximum distortion energy criterion in the framework of a Lattice Particle Model (LPM) is presented for modeling of elastoplastic materials. The basic idea is to decompose the energy of a discrete material point into dilatational and distortional components, and plastic yielding of bonds associated with this material point is assumed to occur only when the distortional component reaches a critical value. Then, two multiaxial fatigue models are proposed for random loading and biaxial tension-tension loading, respectively. Following this, fatigue cracking in homogeneous and composite materials is studied using the lattice particle method and the proposed multiaxial fatigue model. Bi-phase material fatigue crack simulation is performed. Next, an integration of an efficient deep learning model and the lattice particle method is presented to predict fracture pattern for arbitrary microstructure and loading conditions. With this integration, computational accuracy and efficiency are both considered. Finally, some conclusion and discussion based on this study are drawn.

Two fatigue life prediction methods using the energy-based approach have been proposed. A number of approaches have been developed in the past five decades. This study reviews some common models and discusses the model that is most suitable for each different condition, no matter whether the model is designed to solve uniaxial, multiaxial, or biaxial loading paths in fatigue prediction. In addition, different loading cases such as various loading and constant loading are also discussed. These models are suitable for one or two conditions in fatigue prediction. While most of the existing models can only solve single cases, the proposed new energy-based approach not only can deal with different loading paths but is applicable for various loading cases. The first energy-based model using the linear cumulative rule is developed to calculate random loading cases. The method is developed by combining Miner’s rule and the rainflow-counting algorithm. For the second energy-based method, I propose an alternative method and develop an approach to avert the rainflow-counting algorithm. Specifically, I propose to use an energy-based model by directly using the time integration concept. In this study, first, the equivalent energy concept that can transform three-dimensional loading into an equivalent loading will be discussed. Second, the new damage propagation method modified by fatigue crack growth will be introduced to deal with cycle-based fatigue prediction. Third, the time-based concept will be implemented to determine fatigue damage under every cycle in the random loading case. The formulation will also be explained in detail. Through this new model, the fatigue life can be calculated properly in different loading cases. In addition, the proposed model is verified with experimental datasets from several published studies. The data include both uniaxial and multiaxial loading paths under constant loading and random loading cases. Finally, the discussion and conclusion based on the results, are included. Additional loading cases such as the spectrum including both elastic and plastic regions will be explored in future research.

How to effectively and accurately describe, character and quantify the microstructure of the heterogeneous material and its 4D evolution process with time suffered from external stimuli or provocations is very difficult and challenging, but it’s significant and crucial for its performance prediction, processing, optimization and design. The goal of this research is to overcome these challenges by developing a series of novel hierarchical statistical microstructure descriptors called “n-point polytope functions” which is as known as Pn functions to quantify heterogeneous material’s microstructure and creating Pn functions related quantification methods which are Omega Metric and Differential Omega Metric to analyze its 4D processing.In this dissertation, a series of powerful programming tools are used to demonstrate that Pn functions can be used up to n=8 for chaotically scattered images which can hardly be distinguished by our naked eyes in chapter 3 to find or compare the potential configuration feature of structure such as symmetry or polygon geometry relation between the different targets when target’s multi-modal imaging is provided. These n-point statistic results calculated from Pn functions for features of interest in the microstructure can efficiently decompose the structural hidden features into a set of “polytope basis” to provide a concise, explainable, expressive, universal and efficient quantifying manner. In Chapter 4, the Pn functions can also be incorporated into material reconstruction algorithms readily for fast virtualizing 3D microstructure regeneration and also allowing instant material property prediction via analytical structure-property mappings for material design. In Chapter 5, Omega Metric and Differential Omega Metric are further created and used to provide a time-dependent reduced-dimension metric to analyze the 4D evaluation processing instead of using Pn functions directly because these 2 simplified methods can provide undistorted results to be easily compared. The real case of vapor-deposition alloy films analysis are implemented in this dissertation to demonstrate that One can use these methods to predict or optimize the design for 4D evolution of heterogeneous material. The advantages of the all quantification methods in this dissertation can let us economically and efficiently quantify, design, predict the microstructure and 4D evolution of the heterogeneous material in various fields.

Engineering materials and structures undergo a wide variety of multiaxial fatigue loading conditions during their service life. Some of the most complex multiaxial loading scenarios include proportional/non-proportional loading, mix-mode loading, overload/underload, etc. Such loadings are often experienced in many critical applications including aircraft, rotorcraft, and wind turbines. Any accidental failure of these structures during their service life can lead to catastrophic damage to life, property, and environment. All fatigue failure begins with the nucleation of a small crack, followed by crack growth, and ultimately the occurrence of final failure; however, the mechanisms governing the crack nucleation and the crack propagation behavior depend on the nature of fatigue loading and microstructure of the material. In general, ductile materials witness multiple nucleation sites leading to its failure; however, high strength material fails from the nucleation of a single dominant crack. Crack propagation, on the other hand, is governed by various competing mechanisms, which can act either ahead of the crack tip or in the wake region of the crack. Depending upon the magnitude of load, overload/underload, mode-mixity, and microstructure, dominant governing mechanisms may include: crack tip blunting; crack deflection, branching and secondary cracking; strain hardening; residual compressive stresses; plasticity-induced closure, etc. Therefore, it is essential to investigate the mechanisms governing fatigue failure of structural components under such complex multiaxial loading conditions in order to provide a reliable estimation of useful life. The research presented in this dissertation provides the foundation for a comprehensive understanding of fatigue damage in AA 7075 subjected to a range of loading conditions. A series of fatigue tests were conducted on specially designed specimens under different forms of multiaxial loading, which was followed by fracture-surface analysis in order to identify the governing micromechanisms and correlate them with macroscopic fatigue damage behavior. An empirical model was also developed to predict the crack growth rate trend under mode II overloads in an otherwise constant amplitude biaxial loading. The model parameters were calculated using the shape and the size of the plastic zone ahead of the crack tip, and the degree of material hardening within the overload plastic zone. The data obtained from the model showed a good correlation with the experimental values for crack growth rate in the transient region.

Special thermal interface materials are required for connecting devices that operate at high temperatures up to 300°C. Because devices used in power electronics, such as GaN, SiC, and other wide bandgap semiconductors, can reach very high temperatures (beyond 250°C), a high melting point, and high thermal & electrical conductivity are required for the thermal interface material. Traditional solder materials for packaging cannot be used for these applications as they do not meet these requirements. Sintered nano-silver is a good candidate on account of its high thermal and electrical conductivity and very high melting point. The high temperature operating conditions of these devices lead to very high thermomechanical stresses that can adversely affect performance and also lead to failure. A number of these devices are mission critical and, therefore, there is a need for very high reliability. Thus, computational and nondestructive techniques and design methodology are needed to determine, characterize, and design the packages. Actual thermal cycling tests can be very expensive and time consuming. It is difficult to build test vehicles in the lab that are very close to the production level quality and therefore making comparisons or making predictions becomes a very difficult exercise. Virtual testing using a Finite Element Analysis (FEA) technique can serve as a good alternative. In this project, finite element analysis is carried out to help achieve this objective. A baseline linear FEA is performed to determine the nature and magnitude of stresses and strains that occur during the sintering step. A nonlinear coupled thermal and mechanical analysis is conducted for the sintering step to study the behavior more accurately and in greater detail. Damage and fatigue analysis are carried out for multiple thermal cycling conditions. The results are compared with the actual results from a prior study. A process flow chart outlining the FEA modeling process is developed as a template for the future work. A Coffin-Manson type relationship is developed to help determine the accelerated aging conditions and predict life for different service conditions.

The scope of this project is a combination of material science engineering and mechanical engineering. Overall, the main goal of this project is to develop a lightweight concrete that maintains its original strength profile. Initial research has shown that a plastic-concrete composite could create a more lightweight concrete than that made using the typical gravel aggregate for concrete, while still maintaining the physical strength that concrete is known for. This will be accomplished by varying the amount of plastic in the aggregate. If successful, this project would allow concrete to be used in applications it would typically not be suitable for.<br/>After testing the strength of the concrete specimens with varying fills of plastic aggregate it was determined that the control group experienced an average peak stress of 2089 psi, the 16.67% plastic group experienced an average peak stress of 2649 psi, the 33.3% plastic group experienced an average peak stress of 1852 psi, and the 50% plastic group experienced an average stress of 924.5 psi. The average time to reach the peak stress was found to be 12 minutes and 24 seconds in the control group, 15 minutes and 34 seconds in the 16.7% plastic group, 9 minutes and 45 seconds in the 33.3% plastic group, and 10 minutes and 58 seconds in the 50% plastic group. Taking the average of the normalized weights of the cylindrical samples it was determined that the control group weighed 14.773 oz/in, the 16.7% plastic group weighed 15 oz/in, the 33.3% plastic group weighed 14.573 oz/in, and the 50% plastic group weighed 12.959 oz/in. Based on these results it can be concluded that a small addition of plastic aggregate can be beneficial in creating a lighter, stronger concrete. The results show that a 16.7% fill ratio of plastic to rock aggregate can increase the failure time and the peak strength of a composite concrete. Overall, the experiment was successful in analyzing the effects of recycled plastic aggregate in composite concrete. <br/>Some possible future studies related to this subject material are adding aluminum to the concrete, having better molds, looking for the right consistency in each mixture, mixing for each mold individually, and performing other tests on the samples.