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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.

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.

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.

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

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

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>

Unintentional drop of munitions could be encountered during the storage, transportation, and loading processes. In such an impact, malfunctioning of crucial components of munitions is the worst scenario that may be encountered and level of loads should not reach to critical levels. From two possible methods, experimental one is not frequently applied owing to high cost of money and time. On the contrary, particularly in last couple of years, interest is shifted to numerical simulations such as finite element method. In this thesis, foam materials will be investigated as energy absorbers to reduce the effect of loads during the impact. However, modeling the behavior of foam materials by FE codes is a challenging task. In other words, more than a few material parameters which are not commonly specified in literature are sufficient to represent the behavior of foams in an appropriate way. For this reason, material characteristics of the selected two foam materials, expanded polypropylene and v polyethylene, have been obtained in this study. Characterization of EPP and PE is followed by the selection of the appropriate material models in LS-DYNA which is a nonlinear explicit finite element code. Drop tests of munitions on which initially specified foam materials are integrated were done to identify the load levels. Validation of drop tests which are explained in detail in this thesis has been accomplished by LS-DYNA. Final section of the thesis is related to optimization of the foam geometry which will provide reducing load levels to allowable limits. After optimization studies, three alternative geometries which succeed in to reduce loads to allowable load levels were reached. Finally, one of three alternatives is selected considering cost and manufacturing difficulties.