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X-ray tomography is an imaging technique to inspect objects' internal structures with externally measured data by X-ray radiation non-destructively. However, there are concerns about X-ray radiation damage and tomographic acquisition speed in real-life applications. Strategies with insufficient measurements, such as measurements with insufficient dosage (low-dose) and measurements with insufficient projection angles (sparse-view), have been proposed to relieve these problems but are generally compromising imaging quality. Such a dilemma inspires the development of advanced tomographic imaging techniques, in particular, deep learning algorithms to improve reconstruction results with insufficient measurements. The overall aim of this thesis is to design efficient and robust data-driven algorithms with the help of prior knowledge from physics insights and measurement models. We first introduce a hierarchical synthesis CNN (HSCNN), which is a knowledge-incorporated data-driven tomographic reconstruction method for sparse-view and low-dose tomography with a split-and-synthesis approach. This proposed learning-based method informs the forward model biases based on data-driven learning but with reduced training data. The learning scheme is robust against sampling bias and aberrations introduced in the forward modeling. High-fidelity X-ray tomographic imaging reconstruction results are obtained with a very sparse number of projection angles for both numerical simulated and physics experiments. Comparison with both conventional non-learning-based algorithms and advanced learning-based approaches shows improved accuracy and reduced training data size. As a result of the split-and-synthesis strategy, the trained network could be transferable to new cases. We then present a deep learning-based enhancement method, HDrec (hybrid-dose reconstruction algorithm), for low-dose tomography reconstruction via a hybrid-dose acquisition strategy composed of textit{extremely sparse-view normal-dose measurements} and textit{full-view low-dose measurements}. The training is applied for each individual sample without the need of transferring the trained models for other samples. Evaluation of two experimental datasets under different hybrid-dose acquisition conditions shows significantly improved structural details and reduced noise levels compared to results with traditional analytical and regularization-based iterative reconstruction methods from uniform acquisitions under the same amount of total dosage. Our proposed approach is also more efficient in terms of single projection denoising and single image reconstruction. In addition, we provide a strategy to distribute dosage smartly with improved reconstruction quality. When the total dosage is limited, the strategy of combining a very few numbers of normal-dose projections and with not-too-low full-view low-dose measurements greatly outperforms the uniform distribution of the dosage throughout all projections. We finally apply the proposed data-driven X-ray tomographic imaging reconstruction techniques, HSCNN and HDrec, to the dynamic damage/defect characterization applications for the cellular materials and binder jetting additive manufacturing. These proposed algorithms improve data acquisition speeds to record internal dynamic structure changes. A quantitative comprehensive framework is proposed to study the dynamic internal behaviors of cellular structure, which contains four modules: (i) In-situ fast synchrotron X-ray tomography, which enables collection of 3D microstructure in a macroscopic volume; (ii) Automated 3D damage features detection to recognize damage behaviors in different scales; (iii) Quantitative 3D structural analysis of the cellular microstructure, by which key morphological descriptors of the structure are extracted and quantified; (iv) Automated multi-scale damage structure analysis, which provides a quantitative understanding of damage behaviors. In terms of binder jetting materials, we show a pathway toward the efficient acquisition of holistic defect information and robust morphological representation through the integration of (i) fast tomography algorithms, (ii) 3D morphological analysis, and (iii) machine learning-based big data analysis. The applications to two different 4D material characterization demonstrate the advantages of these proposed tomographic imaging techniques and provide quantitative insights into the global evolution of damage/defect beyond qualitative human observation. / Doctor of Philosophy / X-ray tomography is a nondestructive imaging technique to visualize interior structures of non-transparent objects, which has been widely applied to resolve implicit 3D structures, such as human organs and tissues for clinical diagnosis, contents of baggage for security check, internal defect evolution during additive manufacturing, observing fracturing accompanying mechanical tests, and etc. Multiple planar measurements with sufficient X-ray exposure time among different angles are desirable to reconstruct the unique high-quality 3D internal distribution. However, there are practical concerns about X-ray radiation damage to biology samples or long-time acquisition for dynamic experiments in real-life applications. Insufficient measurements by reducing the number of total measurements or the time for each measurement, are proposed to solve this problem but doing so usually leads to the sacrifice of the reconstruction quality. Computational algorithms are developed for tomographic imaging under these insufficient measurement conditions to obtain reconstructions with improved quality. Deep learning has been successfully applied to numerous areas, such as in recognizing speech, translating languages, detecting objects, and etc. It has also been applied to X-ray tomographic imaging to improve the reconstruction results by learning the features through thousands to millions of corrupted and ideal reconstruction pairs. The aim of this thesis to design efficient deep learning-based algorithms with the help of physical and measurement priors to reduce the number of training datasets. We propose two different deep learning-based tomographic imaging techniques to improve reconstruction results with reduced training data under different insufficient measurement conditions. One way is to incorporate prior knowledge of the physics models to reduce the required amount of ground truth data, from thousands to hundreds. The training data requirement is further simplified with another hybrid measurement strategy, which could be implemented on each individual sample with only several high-quality measurements. In the end, we apply these two proposed algorithms to different dynamic damage/defect behavior characterization applications. Our methods achieve improved reconstruction results with greatly enhanced experimental speeds, which become suitable for dynamic 3D recording. Final results demonstrate the advantages of the proposed tomographic imaging techniques and provide quantitative insights into the global dynamic evolution inside the material. This quantitative analysis also provides a much more comprehensive understanding than qualitative human observation.

Portland cement concrete (PCC) structures deteriorate with age and need to be maintained or replaced. Early detection of deterioration in PCC (e.g., alkali-silica reaction, freeze/thaw damage, or chloride presence) can lead to significant reductions in maintenance costs. However, it is often too late to perform low-cost preventative maintenance by the time deterioration becomes evident. Non-destructive evaluation (NDE) methods are potentially among the most useful techniques developed for assessing constructed facilities. They are noninvasive and can be performed rapidly. Portland cement concrete can be nondestructively evaluated by electrically characterizing its complex dielectric constant. The real part of the dielectric constant depicts the velocity of electromagnetic waves in PCC. The imaginary part describes the conductivity of PCC and the attenuation of electromagnetic waves, and hence the losses within the PCC media. Dielectric properties of PCC have been investigated in a laboratory setting using a parallel plate capacitor operating in the frequency range of 0.1MHz to about 40MHz. This capacitor set-up consists of two horizontal-parallel plates with an adjustable separation for insertion of a dielectric specimen (PCC). While useful in research, this approach is not practical for field implementation In this research, a capacitance probe has been developed for field application. The probe consists of two planar conducting plates and is made of flexible materials for placement on exposed surfaces of the specimens to be tested. The calibration method of both capacitive systems has been extensively studied to minimize systematic errors in the measurement process. These two measurement systems will be discussed and compared to one another on the basis of sensitivity and measurement repeatability. / Master of Science

An under-body blast (UBB) refers to the use of a roadside explosive device to target a vehicle and its occupants. During Operation Iraqi Freedom, improvised explosive devices (IEDs) accounted for an estimated 63% of US fatalities. Furthermore, advancements in protective equipment, combat triage, and treatment have caused an increase in IED casualties surviving with debilitating injuries. Military vehicles have been common targets of IED attacks because of the potential to inflict multiple casualties. Anthropomorphic test devices (ATDs) are mechanical human surrogates designed to transfer loads and display kinematics similar to a human subject. ATDs have been used successfully by the automotive industry for decades to quantify human injury during an impact and assess safety measures. Currently the Hybrid III ATD is used in live-fire military vehicle assessments. However, the Hybrid III was designed for frontal impacts and demonstrated poor biofidelity in vertical loading experiments. To assess military vehicle safety and make informed improvements to vehicle design, a novel Anthropomorphic Test Device (ATD) was developed and optimized for vertical loading. ATDs, commonly referred to as crash dummies, are designed to estimate the risk of injuries to a human during an impact. The main objective of this study was to develop and validate a Finite Element (FE) model of the ATD lower limb. / Master of Science / An under-body blast (UBB) refers to the use of a roadside explosive device to target a vehicle and its occupants. During Operation Iraqi Freedom, improvised explosive devices (IEDs) accounted for an estimated 63% of US fatalities. Furthermore, advancements in protective equipment, combat triage, and treatment have caused an increase in IED casualties surviving with debilitating injuries. Military vehicles have been common targets of IED attacks because of the potential to inflict multiple casualties. Anthropomorphic test devices (ATDs) are mechanical human surrogates designed to transfer loads and display kinematics similar to a human subject. ATDs have been used successfully by the automotive industry for decades to quantify human injury during an impact and assess safety measures. Currently the Hybrid III ATD is used in live-fire military vehicle assessments. However, the Hybrid III was designed for frontal impacts and demonstrated poor biofidelity in vertical loading experiments. To assess military vehicle safety and make informed improvements to vehicle design, a novel Anthropomorphic Test Device (ATD) was developed and optimized for vertical loading. ATDs, commonly referred to as crash dummies, are designed to estimate the risk of injuries to a human during an impact. The main objective of this study was to develop and validate a Finite Element (FE) model of the ATD lower limb.

Thermoelectric devices are useful for a variety of applications due to their ability to either convert heat directly into electricity, or to generate a temperature gradient from an electric current. These devices offer several attractive features including compact size, no moving parts, limited maintenance requirements, and high reliability. Thus thermoelectric devices are used for temperature-control, cooling, or power generation in various industrial systems such as automobiles, avionics, refrigerators, chillers, laser diodes, dehumidifiers, and a variety of sensors. In order to improve the efficiency of thermoelectric devices, many endeavors have been made to design and fabricate materials with a higher dimensionless thermoelectric figure of merit (ZT), as well as to optimize the device structure and packaging to manage heat more effectively. When evaluating candidate thermoelectric materials, one must accurately characterize the electrical conductivity, thermal conductivity, and the Seebeck coefficient over the temperature range of potential use. However, despite considerable research on thermoelectric materials for decades, there is still significant scatter and disagreement in the literature regarding accurate characterization of these properties due to inherent difficulties in the measurements such as requirements for precise control of temperature, simultaneous evaluation of voltage and temperature, etc. Thus, a well-designed and well-calibrated thermoelectric measurement system that can meet the requirements needed for multiple kinds of thermoelectric materials is an essential tool for the development of advanced thermoelectric devices. In this dissertation, I discuss the design, fabrication, and validation of a measurement system that can rapidly and accurately evaluate the Seebeck coefficient and electrical resistivity of thermoelectric materials of various shapes and sizes from room temperature up to 600 K. The methodology for the Seebeck coefficient and electrical resistivity measurements is examined along with the optimization and application of both in the measurement system. The calibration process is completed by a standard thermoelectric material and several other materials, which demonstrates the accuracy and reliability of the system. While a great deal of prior research has focused on low temperature thermoelectric materials for cooling, such as Bi2Te3, high temperature thermoelectric materials are receiving increasing attention for power generation. With the addition of commercial systems for the Seebeck coefficient, electrical resistivity, and thermal conductivity measurements to expand the temperature range for evaluation, a wide range of materials can be studied and characterized. Chapter Two of this dissertation describes the physical properties characterization of a variety of thermoelectric materials, including room temperature materials such as Bi0.5Sb1.5Te3, medium temperature level materials such as skutterudites, and materials for high temperature applications such as half-Heusler alloys. In addition, I discuss the characterization of unique oxide thermoelectric materials, which are Al doped ZnO and Ca-Co-O systems for high temperature applications. Chapter Four of this dissertation addresses the use of GaSn alloys as a thermal interface material (TIM), to improve thermal transport between thermoelectric devices and heat sinks for power generation applications at high temperature. I discuss the mechanical and thermal behavior of GaSn as an interface material between electrically insulating AlN and Inconel heat exchangers at temperatures up to 600 °C. Additionally, a theoretical model for the experimental thermal performances of the GaSn interface layer is also examined. / Ph. D. / Thermoelectric materials can directly convert heat into electricity for power generation, or they can be used for cooling or refrigeration applications when supplied with electric power. This dissertation primarily focuses on the evaluation of materials used in thermoelectric generators (TEGs). Specifically, Chapter Two of this work describes the design, development, and validation of a developed measurement system that can evaluate two important properties, the Seebeck coefficient and electrical resistivity, for a variety of thermoelectric materials. Next, Chapter Three discusses the work using other commercial measurement systems to evaluate several types of thermoelectric materials, including Bi2Te3 based materials, skutterudites, half-Heusler alloys, ZnO, and Ca-Co-O for a TEG module. Finally, I discuss the use of GaSn, a liquid metal alloy, as a thermal interface material to improve heat transport between dissimilar materials for TEGs. The GaSn was applied between a thermoelectric device and a heat exchanger for use in energy harvesting devices. The mechanical robustness and thermal reliability were tested, and the GaSn was shown to improve thermal performances both in experiments and through modeling.

This research develops and validates computational tools for the design and analysis of structural components and systems constructed with Ultra-High Performance Concrete (UHPC). The modeling strategy utilizes the Lattice Discrete Particle Model (LDPM) to represent UHPC material and structural member response, and extends a structural-level triaxial continuum constitutive law to account for the addition of discrete fibers. The approach is robust, general, and could be utilized by other researchers to expand the computational capability and simulate the behavior of different composite materials. The work described herein identifies the model material parameters by conducting a complete material characterization for UHPC, with and without fiber reinforcement, describing its behavior in unconfined compression, uniaxial tension, and fracture toughness. It characterizes the effect of fiber orientations, fiber-matrix interaction, and resolves the issue of multi-axial stress states on fiber pullout. The capabilities of the computational models are demonstrated by comparing the material test data that were not used in the parameter identification phase to numerical simulations to validate the models' predictive capabilities. These models offer a mechanics-based shortcut to UHPC analysis that can strategically support ongoing development of material and structural design codes and standards. / Ph. D. / This research develops and validates new computer-based methods to analyze and design civil infrastructure constructed with ultra-high performance concrete (UHPC), achieved when steel fibers are combined with a finely graded cement matrix. With superior performance characteristics in comparison to regular concrete, UHPC is studied herein for its strong potential to advance the durability, efficiency, and resiliency of new and existing infrastructure. The simulation-based methods are extensively verified with novel experiments that evaluate the material limits and failure modes when compressed, bent, or stretched, considering fiber volume and orientation. The computer-based tools can be used to realistically assess the structural performance of innovative UHPC applications in buildings, bridges, and tunnels under natural hazards, leading to surpassed levels of structural efficiency and resiliency across civil infrastructure.

Utilizing carbon fiber reinforced polymers (CFRP) in design offers advantages including as mass reduction, increased stiffness, enhanced corrosion resistance, improved sound damping, and vibration absorption. The notable strength-to-weight ratio of CFRP has driven its adoption over traditional materials like aluminum and steel in various industries such as aerospace, automotive, and sports. The assembly of "Stack-ups," which are layered assemblies of CFRP and metal components, becomes crucial as CFRP increasingly replaces metallic parts in high mechanical loading structural situations. The high thrust force involved in machining fiber reinforced polymers (FRPs) causes a peel-up and push-out effect on the workpiece, leading to delamination of the plies. This study developed an FE tool to simulate the drilling of FRPs effectively, aiming to validate tool design and enhance the cutting process. Modeling the impact of fiber orientation in CFRP material on mechanical behavior is essential for optimizing component design and manufacturing. To reduce the exhaustive experimental work related to CFRP material characterization Abaqus Explicit is used to predict the tensile material response through fracture. FEA analyses included mesh size, mass/time scaling, failure models, and cohesive surfaces. Experimental results with the new fixturing-rig show consistent gauge region failure, regardless of fiber orientation. Puck's model accurately predicts fracture force and displacement for parallel fiber orientation. 45 and 90-degree orientations, maximum strain and LaRCO2 models offer better accuracy. Most apparent, was the criticality of cohesive surfaces to predict the nonlinear loading response observed experimentally. Simulations for various fiber layup orientations indicate similar force-displacement signatures, with a notable reduction in failure force at angles between parallel and 45 degrees. Simulating CFRP mechanical properties under three-point bending to understand cohesive interactions between plies in a laminate was investigated; this capability critical to effectively model the peel-up and push-out problem observed when drilling. A parametric FEA study investigated the affect of mesh size, mass/time scaling, failure models (Hashin, MCT, LaRC02, Maximum Strain, Puck), and cohesive surfaces versus loading response. Experimental results with a larger radius punch show failure on the intended bottom side, facilitating Aramis strain camera recording. Effective mass/time scaling reduces computation time while maintaining accuracy. For perpendicular fiber orientation, all failure models exhibit a similar force-displacement rate. Minimal difference exists among 0-degree models, except for a 4.18% underprediction by LaRC02. At 45 and 90 degrees, Maximum Strain and LaRCO2 models prove more accurate and converge well. The study underscores the need for cohesive surfaces to predict nonlinearity in loading responses for non-parallel bending setups. A 3D drilling model is developed discussing significance of modelling techniques and considerations. The removal of failed elements creates periodic voids between the workpiece and tool, underlining the importance of proper mesh development. Accurate, computationally efficient models with element lengths of 50-75 µm near the expected failure region were emphasized. Using a discrete rigid body yielded a 42.1% reduction in memory requirements and a 2.81x reduction in time step compared to deformable bodies with rigid constraints. Mass scaling led to over tenfold computation time reduction with a mere 5.3% mass change. Increasing viscosity parameters improved the loading response of CFRP laminate during high-speed drilling. Strain rate strengthening, aligned with literature, increased the load profile by 10.9%. Friction in the CFRP drilling model showed less sensitivity than estimated, with a 4.4% standard deviation. The FE model once confidently developed, was compared to experiments. The prediction aligned well with experiments, accurately predicting thrust force differences between CD854 and CD856 drills. The CD856 exhibited reduced inter-ply damage, highlighting the advantage of double-angle drill geometry. The CD854's "spur" cutting edge geometry improved hole quality. The "Stack-up" drilling model effectively predicted thrust force transitions between UD-CFRP and Aluminum layers, confirming the CD854's reduced thrust force when drilling Aluminum, as described by the tool manufacturer Sandvik. / Thesis / Doctor of Philosophy (PhD)

Vascular networks are required to support the formation and function of three-dimensional tissues. Biodegradable scaffolds are being considered in order to promote vascularization where natural regeneration of lost or destroyed vascular networks fails. Particularly; composite materials are expected to fulfill the complex demands of a patient's body to support wound healing. Microbial biopolyesters are being regarded as such second and third generation biomaterials. Methylobacterium extorquens is one of several microorganisms that should be considered for the production of advanced polyhydroxyalkanoates (PHAs). M. extorquens displays a distinct advantage in that it is able to utilize methanol as an inexpensive substrate for growth and biopolyester production. The design of functionalized PHAs, which would be made of both saturated short-chain-length (scl, C [less than or equal to] 5) and unsaturated medium-chain-length (mcl, 6 [less than or equal to] C [less than or equal to] 14) monomeric units, aimed at combining desirable material properties of inert scl/mcl-PHAs with those of functionalized mcl-PHAs. By independently inserting the phaC1 or the phaC2 gene from Pseudomonas fluorescens GK13, recombinant M. extorquens strains were obtained which were capable of producing PHAs containing C-C double bonds. A fermentation process was developed to obtain gram quantities of biopolyesters employing the recombinant M. extorquens ATCC 55366 strain which harbored the phaC2 gene of P. fluorescens GK13, the better one of the two strains at incorporating unsaturated monomeric units. The PHAs produced were found in a blend of scl-PHAs and functionalized scl/mcl-PHAs (4 [less than or equal to] C [less than or equal to] 6), which were the products of the native and of the recombinant PHA synthase, respectively. Thermo-mechanical analysis confirmed that the functionalized scl/mcl-PHAs exhibited the desirable material properties expected. This project contributed to current research on polyhydroxyalkanoates at different levels. The terminal double bonds of the functionalized scl/mcl-PHAs are amenable to chemical modifications and could be transformed into reactive functional groups for covalently linking other biomacromolecules. It is anticipated that these biopolyesters will be utilized as tissue engineering materials in the future, due to their functionality and thermo-mechanical properties.

<p>Curiosity is a powerful tool, and combined with the ability to observe the natural world, grants humankind an unique opportunity, the opportunity to wonder why. Why do things exist?, why do they do the things they do?, why is this even possible?</p> <p>Research in our lab is focused on the basic understanding and potential application of biological materials, in particular, biological adhesives produced by marine organisms such as oysters. Oysters produce a cement-like material that is able to withstand the dynamic conditions found in coastal environments. The focus of this dissertation is to lay the basis of the characterization of new biological materials by observing and analyzing its physical properties, to measure the performance of the material in natural conditions and finally to identify the basic components that give the material the properties that we observe. The end goal of this project is to understand the properties of this material so we are able to develop a synthetic system that is able to imitate, as close as possible, what we find in nature. These results, and more importantly, the new questions that emerge from this research, provide a first look at the adhesive system of oysters leading the way to new discoveries in the future.</p>