Effects of Intrinsic Material Length Scales and Grain Boundaries on Void Density Distributions of Shock-Induced Spall Damage: A 3D Tomography Study

Description
The characterization of spall microstructural damage metallic samples is critical to predicting and modeling modes of failure under blast, ballistic, and other dynamic loads. In this regard, a key step to improve models of dynamic damage is making appropriate connections

The characterization of spall microstructural damage metallic samples is critical to predicting and modeling modes of failure under blast, ballistic, and other dynamic loads. In this regard, a key step to improve models of dynamic damage is making appropriate connections between experimental characterization of actual damage in the form of discrete voids distributed over a given volume of the specimens, and the output of the models, which provide a continuous measure of damage, for example, void fraction as a function of position. Hence, appropriate homogenization schemes to estimate, e.g., continuous void fraction estimations from discrete void distributions, are key to calibration and validation of damage models. This project seeks to analyze 3D tomography data to relate the homogenization parameters for the discrete void distributions, i.e., homogenization volume size and step, as well as representative volume element size, to the local length scales, e.g., grain size as well as void size and spacing. Copper disks 10 mm in diameter and 1 mm thick with polycrystalline structures were subjected to flyer plate impacts resulting in shock stresses ranging from 2 to 5 GPa. The spall damage induced in samples by release waves was characterized using X-ray tomography techniques. The resulting data is thresholded to differentiate voids from the matrix and void fraction is obtained via homogenization using various parameterization schemes to characterize void fraction distributions along the shock and transverse directions. The representative volume element is determined by relating void fraction for varying parameterized window sizes to the void fraction in the overall volume. Results of this study demonstrate that the optimal representative volume element (RVE) to represent void fraction within 10% error of the overall sample void fraction for this Hitachi copper sample is .2304 mm3. The RVE is found to contain approximately 255 grains. Statistical volume elements of 1300 µm3 or smaller are used to quantify void fraction as a function of position and while the results along the shock direction, i.e., the presence of a clear peak at the expected location of the spall plane, are expected, the void fraction along the transverse direction show oscillatory behavior. The power spectra and predominant frequencies of these distributions suggest the periodicity of the oscillations relates to multiples of local material length scales such as grain size. This demonstrates that the grain size in the samples, about 120 µm, is too large compared to the sample size to try to capture spatial variability due to applied loads and the microstructure, since the microstructure itself produces variability on the order of a few grain sizes. These results may play a role for the design of experiments to collect real-world 3D damage data for validating and enhancing the accuracy and definition of simulation models for damage characterization by providing frameworks for microstructural strain variability when modeling spall behavior under dynamic damage.
Date Created
2023-12
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Synthesis and Characterization of Sputter Deposited BN and Cu/BN Multilayer Thin Films

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Description
This thesis presents a study of Boron Nitride (BN) and Copper (Cu)/BN multilayer thin films in terms of synthesis, chemical, structural, morphological, and mechanical properties characterization. In this study, the influence of Ar/N₂ flow rate in synthesizing stoichiometric BN

This thesis presents a study of Boron Nitride (BN) and Copper (Cu)/BN multilayer thin films in terms of synthesis, chemical, structural, morphological, and mechanical properties characterization. In this study, the influence of Ar/N₂ flow rate in synthesizing stoichiometric BN thin films via magnetron sputtering was investigated initially. Post magnetron sputtering, the crystalline nature and B:N stoichiometric ratio of deposited thin films were investigated by X-ray diffraction (XRD) and X-ray Photoelectron Spectroscopy (XPS) respectively. Thicknesses revealed by ellipsometry analysis for nearly stoichiometric B:N thin films and their corresponding deposition times were used for estimating BN interlayer deposition times during the deposition of Cu/BN multilayer thin films. To characterize the microstructure of the synthesized Cu/BN multilayer thin films, XRD and scanning electron microscopy (SEM) have been used. Finally, a comparison of nanoindentation measurements on pure Cu and Cu/BN multilayer thin films having different number of BN interlayers were used for studying the influence of BN interlayers on improving mechanical properties such as hardness and elastic modulus. The results show that the stoichiometry of BN thin films is dependent on the Ar/N₂ flow rate during magnetron sputtering. An optimal Ar/N₂ flow rate of 13:5 during deposition was required to achieve an approximately 1:1 B:N stoichiometry. Grazing incidence and powder XRD analysis on these stoichiometric BN thin films deposited at room temperature did not reveal a phase match when compared to hexagonal boron nitride (h-BN) and cubic boron nitride (c-BN) reference XRD patterns. For a BN thin film deposition time of 5 hours, a thickness of approximately 40 nm was achieved, as revealed by ellipsometry. XRD and microstructure analysis using scanning electron microscopy (SEM) on pure Cu and Cu/BN thin films showed that the Cu grain size in Cu/BN thin films is much finer than pure Cu thin films. Interestingly, nanoindentation measurements on pure Cu and Cu/BN thin films having a similar overall thickness demonstrated that hardness and Young’s modulus of the films were improved significantly when BN interlayers are present.
Date Created
2023
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Improving Quantum Mechanical Calculations Using Graph Neural Networks to Predict Energies from Atomic Structure

Description

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(𝑟), and the mapping from 𝑛(𝑟) to the energy density 𝑒(𝑟) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with 𝑘 = 10 and 𝑘 = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(𝑟) to 𝑒(𝑟) of 8.52 · 10^14 and 3.10 · 10^14 for 𝑘 = 10 and 𝑘 = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.

Date Created
2023-05
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Investigation of the Atomic-Level Response of Aromatic Polymers to High Pressure via In Situ Energy Dispersive X-ray Diffraction Experiments

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Description
Aromatic polymers, with benzene-like rings in their main chains, include materials such as polyurea, an amorphous elastomer capable of dissipating large amounts of energy under dynamic loading, which makes it a promising coating for defensive systems. Although computational research exists

Aromatic polymers, with benzene-like rings in their main chains, include materials such as polyurea, an amorphous elastomer capable of dissipating large amounts of energy under dynamic loading, which makes it a promising coating for defensive systems. Although computational research exists that investigates the atomic-level response of polyurea and other amorphous aromatic polymers to extreme conditions, there is little experimental work to validate these models 1) at the atomic-scale and 2) under high pressures characteristic of extreme dynamic loading. Understanding structure-property relationships at the atomic-level is important for polymers, considering many of them undergo pressure and temperature-induced structural transformations, which must be understood to formulate accurate predictive models. This work aims to gain a deeper understanding of the high-pressure structural response of aromatic polymers at the atomic-level, with emphasis into the mechanisms associated with high-pressure transformations. Hence, atomic-level structural data at high pressures was obtained in situ via multiangle energy dispersive X-ray diffraction (EDXD) experiments at the Advanced Photon Source (APS) for polyurea and another amorphous aromatic polymer, polysulfone, chosen as a reference due to its relatively simple structure. Pressures up to 6 GPa were applied using a Paris Edinburgh (PE) hydraulic press at room temperature. Select polyurea samples were also heated to 277 °C at 6 GPa. The resulting structure factors and pair distribution functions, along with molecular dynamics simulations of polyurea provided by collaborators, suggest that the structures of both polymers are stable up to 6 GPa, aside from reductions in free-volume between polymer backbones. As higher pressures (≲ 32 GPa) were applied using diamond anvils in combination with the PE press, indications of structural transformations were observed in both polymers that appear similar in nature to the sp2-sp3 hybridization in compressed carbon. The transformation occurs gradually up to at least ~ 26 GPa in PSF, while it does not progress past ~ 15 GPa in polyurea. The changes are largely reversible, especially in polysulfone, consistent with pressure-driven, reversible graphite-diamond transformations in the absence of applied temperature. These results constitute some of the first in situ observations of the mechanisms that drive pressure-induced structural transformations in aromatic polymers.
Date Created
2022
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Fundamental Investigations into the Properties and Performance of Advanced Materials

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Description
Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires

Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.
Date Created
2022
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Improving Electric Vehicle and Battery Pack Assembly Through Predictive Modeling of Resistance Spot Welding and Distortion Reduction in Thin-Gauge Metal Sheets

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Description
An approach for modeling resistance spot welding of thin-gauge, dissimilar metal sheets with high electrical conductivity is presented in this work. In this scenario, the electrical and thermal contact resistances play a dominant role in heat generation and temperature evolution

An approach for modeling resistance spot welding of thin-gauge, dissimilar metal sheets with high electrical conductivity is presented in this work. In this scenario, the electrical and thermal contact resistances play a dominant role in heat generation and temperature evolution within the workpieces; these interactions ultimately control the weld geometry. Existing models are limited in modeling these interactions, especially for dissimilar and thin-gauge metal sheets, and at higher temperatures when the multiphysics becomes increasingly interdependent. The approach presented here uses resistivity measurements, combined with thermal modeling and known bulk resistance relationships to infer the relationship between electrical contact resistance and temperature for each of the different material interfaces in the welding process. Corresponding thermal contact resistance models are developed using the Wiedemann-Franz law combined with a scaling factor to account for nonmetallic behavior. Experimental and simulation voltage histories and final weld diameter were used to validate this model for a Cu/Al/Cu and a Cu/Al/Cu/Al/Cu stack-ups. This model was then used to study the effect of Ni-P coating on resistance spot welding of Cu and Al sheets in terms of weld formation, mechanical deformation, and contact resistance. Contact resistance and current density distribution are highly dependent on contact pressure and temperature distribution at the Cu/Al interface in the presence of alumina. The Ni-P coating helps evolve a partially-bonded donut shaped weld into a fully-bonded hourglass-shaped weld by decreasing the dependence of contact resistance and current density distribution on contact pressure and temperature distribution at the Cu/Al interface. This work also provides an approach to minimize distortion due to offset-rolling in thin aluminum sheets by optimizing the stiffening feature geometry. The distortion is minimized using particle swarm optimization. The objective function is a function of distortion and smallest radius of curvature in the geometry. Doubling the minimum allowable radius of curvature nearly doubles the reduction in distortion from the stadium shape for a quarter model. Reduction in distortion in the quarter model extends to the full-scale model with the best design performing 5.3% and 27% better than the corresponding nominal design for a quarter and full-scale model, respectively.
Date Created
2022
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Elucidating the Role of Ultraviolet Weathering and Biofilm Formation on the Adsorption of Micropollutants onto Microplastics

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Description
Plastics, when released into the environment, undergo surface weathering due to mechanical abrasion and ultraviolet (UV) exposure that leads to the formation of microplastics. Weathering also introduces oxygen functional groups on the surface, which will affect surface interactions compared to

Plastics, when released into the environment, undergo surface weathering due to mechanical abrasion and ultraviolet (UV) exposure that leads to the formation of microplastics. Weathering also introduces oxygen functional groups on the surface, which will affect surface interactions compared to pristine plastics. In this study, the adsorption of selected model contaminants of high environmental relevance was evaluated at different level of abiotic and biotic transformation to understand how microplastics aging influences contaminant adsorption on high density polyethylene (HDPE) and polypropylene (PPE). Microplastics were aged through an accelerated weathering process using UV exposure with or without hydrogen peroxide. The effect of UV aging on the microplastics’ morphology and surface chemistry was characterized by Fourier Transform Infrared Spectroscopy, X-Ray Photoelectron Spectroscopy, streaming Zeta potential, Brunauer–Emmett–Teller Krypton adsorption analyses and Computed X-Ray Tomography. Sorption of organic contaminants was found to be higher on aged microplastics compared to pristine ones for all contaminants investigated. This increase in sorption affinity was found to be associated with a change in the surface chemistry and not in an increase in specific surface area after aging. Biological surface weathering (i.e., biofilm formation) was carried out at a lab-scale setting using model biofilm-forming bacteria followed by adsorption affinity measurement of biofilm-laden microplastics with the model organic contaminants. The amount of microbial biomass accumulated on the surface was also evaluated to correlate the changes in sorption affinity with the change in microplastic biofilm formation. The results of this study emphasize the need to understand how contaminant-microplastics interactions will evolve as microplastics are altered by biotic and abiotic factors in the environment.
Date Created
2022
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Discovering Relationship of Atomistic Structure to Generate Stishovite Nucleation Using Convolutional Neural Networks

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Description
This research seeks to answer the question if there is a singular relationship between stishovite nucleation and the atomistic structure of the preshocked amorphous SiO$_2$. To do this a stishovite manufacturing method is developed in which 1,152 samples were produced.

This research seeks to answer the question if there is a singular relationship between stishovite nucleation and the atomistic structure of the preshocked amorphous SiO$_2$. To do this a stishovite manufacturing method is developed in which 1,152 samples were produced. The majority of these samples did crystallize. The method was produced through two rounds of experiments and fine-tuning with the pressure damp, temperature damp, shock pressure using an NPHug fix, and sample origin. A new random atomic insertion method was used to generate a new and different SiO$_2$ amorphous structure not before seen within the research literature. The optimal values for shock were found to be 60~GPa for randomly atom insertion samples and 55~GPa for quartz origin samples. Temperature damp appeared to have a slight effect optimizing at 0.05~ps and the pressure damp had no visible effect, testing was done with temperature damp from 0.05 to 0.5~ps and pressure damp from 0.1 to 10.0~ps. There appeared to be significant randomness in crystallization behavior. The preshocked and postnucleated samples were transformed into Gaussian fields of crystal, mass, and charge. These fields were divided and classified using a cut-off method taking the number of crystals produced in portions of each simulation and classifying each potion as nucleated or non-nucleated. Data in which some nucleation but not a critical amount was present was removed constituting 2.6\% to 20.3\% of data in all tests. A max method was also used which takes only the maximum portions of each simulation to classify as nucleating. There are three other variables tested within this work, a sample size of 18,000 or 72,728~atoms, Gaussian variance of 1 or 4~\AA, and Convolutional neural network (CNN) architecture of a garden verity or all convolution along with the portioning classification method, sample origination, and Gaussian field type. In total 64 tests were performed to try every combination of variable. No significant classifications were made by the CNNs to nucleation or non-nucleation portions. The results clearly confirmed that the data was not abstracting to atomistic structure and was random by all classifications of the CNNs. The all convolution CNN testing did show smoother outcomes in training with less fluctuations. 59\% of all validation accuracy was held at 0.5 for a random state and 84\% was within $\pm0.02$ of 0.5. It is conclusive that prenucleation structure is not the sole predictor of nucleation behavior. It is not conclusive if prenucleation structure is a partial or non-factor within nucleation of stishovite from amorphous SiO$_2$.
Date Created
2021
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Applications of Reduced Order Modeling for Geometrically Nonlinear Structures: Highly Asymmetric Structures and Contact Nonlinearity

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Description
Over the past few decades there has been significant interest in the design and construction of hypersonic vehicles. Such vehicles exhibit strongly coupled aerodynamics, acoustics, heat transfer, and structural deformations, which can take significant computational efforts to simulate using standard

Over the past few decades there has been significant interest in the design and construction of hypersonic vehicles. Such vehicles exhibit strongly coupled aerodynamics, acoustics, heat transfer, and structural deformations, which can take significant computational efforts to simulate using standard finite element and computational fluid dynamics techniques. This situation has lead to development of various reduced order modelling (ROM) methods which reduce the parameter space of these simulations so they can be run more quickly. The planned hypersonic vehicles will be constructed by assembling a series of sub-structures, such as panels and stiffeners, that will be welded together creating built-up structures.In this light, the focus of the present investigation is on the formulation and validation of nonlinear reduced order models (NLROMs) of built-up structures that include nonlinear geometric effects induced by the large loads/large response. Moreover, it is recognized that gaps between sub-structures could result from the these intense loadings can thus the inclusion of the nonlinearity introduced by contact separation will also be addressed. These efforts, application to built-up structures and inclusion of contact nonlinearity, represent novel developments of existing NLROM strategies. A hat stiffened panel is selected as a representative example of built-up structure and a compact NRLOM is successfully constructed for this structure which exhibited a potential internal resonance. For the investigation of contact nonlinearity, two structural models were used: a cantilevered beam which can contact several stops and an overlapping plate model which can exhibit the opening/closing of a gap. Successful NLROMs were constructed for these structures with the basis for the plate model determined as a two-step process, i.e., considering the plate without gap first and then enriching the corresponding basis to account for opening of the gap. Adaptions were then successfully made to a Newton-Raphson solver to properly account for contact and the associated forces in static predictions by NLROMs.
Date Created
2021
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On the Role of the Corner Radius in the Mechanical Behavior of the Hexagonal Honeycomb

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Description
The hexagonal honeycomb is a bio-inspired cellular structure with a high stiffness-to-weight ratio. It has contributed to its use in several engineering applications compared to solid bodies with identical volume and material properties. This characteristic behavior is mainly attributed to

The hexagonal honeycomb is a bio-inspired cellular structure with a high stiffness-to-weight ratio. It has contributed to its use in several engineering applications compared to solid bodies with identical volume and material properties. This characteristic behavior is mainly attributed to the effective nature of stress distribution through the honeycomb beams that manifests as bending, axial, and shear deformation mechanisms. Inspired by the presence of this feature in natural honeycomb, this work focuses on the influence of the corner radius on the mechanical properties of a honeycomb structure subjected to in-plane compression loading. First, the local response at the corner node interface is investigated with the help of finite element simulation of a periodic unit cell within the linear elastic domain and validated against the best available analytical models. Next, a parametric design of experiments (DOE) study with the unit cell is defined with design points of varying circularity and cell length ratios towards identifying the optimal combination of all geometric parameters that maximize stiffness per unit mass while minimizing the stresses induced at the corner nodes. The observed trends are then compared with compression tests of 3D printed Nylon 12 honeycomb specimens of varying corner radii and wall thicknesses. The study concluded that the presence of a corner radius has a mitigating effect on peak stresses but that these effects are dependent on thickness while also increasing specific stiffness in all cases. It also points towards an optimum combination of parameters that achieve both objectives simultaneously while shedding some light on the functional benefit of this radius in wasp and bee nests that employ a hexagonal cell.
Date Created
2021
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