Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item A CAUSAL INFORMATION FUSION MODEL FOR ASSESSING PIPELINE INTEGRITY IN THE PRESENCE OF GROUND MOVEMENT(2024) Schell, Colin Andrew; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Pipelines are the primary transportation method for natural gas and oil in the United States making them critical infrastructure to maintain. However, ground movement hazards, such as landslides and ground subsidence, can deform pipelines and potentially lead to the release of hazardous materials. According to the Pipeline and Hazardous Materials Safety Administration (PHMSA), from 2004 to 2023, ground movement related pipeline failures resulted in $413M USD in damages. The dynamic nature of ground movement makes it necessary to collect pipeline and ground monitoring data and to actively model and predict pipeline integrity. Conventional stress-based methods struggle to predict pipeline failure in the presence of large longitudinal strains that result from ground movement. This has prompted many industry analysts to use strain-based design and assessment (SBDA) methods to manage pipeline integrity in the presence of ground movement. However, due to the complexity of ground movement hazards and their variable effects on pipeline deformation, current strain-based pipeline integrity models are only applicable in specific ground movement scenarios and cannot synthesize complementary data sources. This makes it costly and time-consuming for pipeline companies to protect their pipeline network from ground movement hazards. To close these gaps, this research made significant steps towards the development of a causal information fusion model for assessing pipeline integrity in a variety of ground movement scenarios that result in permanent ground deformation. We developed a causal framework that categorizes and describes how different risk-influencing factors (RIFs) affect pipeline reliability using academic literature, joint industry projects, PHMSA projects, pipeline data, and input from engineering experts. This framework was the foundation of the information fusion model which leverages SBDA methods, Bayesian network (BN) models, pipeline monitoring data, and ground monitoring data to calculate the probability of failure and the additional longitudinal strain needed to fail the pipeline. The information fusion model was then applied to several case studies with different contexts and data to compare model-based recommendations to the actions taken by decision makers. In these case studies, the proposed model leveraged the full extent of data available at each site and produced similar conclusions to those made by decision makers. These results demonstrate that the model could be used in a variety of ground movement scenarios that result in permanent ground deformation and exemplified the comprehensive insights that come from using an information fusion approach for assessing pipeline integrity. The proposed model lays the foundation for the development of advanced decision making tools that can enable operators to identify at-risk pipeline segments that require site specific integrity assessments and efficiently manage the reliability of their pipelines in the presence of ground movement.Item LENGTH-SCALE DEPENDENCE OF VISCOPLASTIC PROPERTIES OF SILVER SINTER REVEALED BY INDENTATION TESTING AND MODELING(2024) Leslie, David; Dasgupta, Abhijit; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This doctoral dissertation research focuses on using a combination of indentation testing and modeling to characterize the creep behavior of heterogeneous silver sinter at different temperatures, using multiple indenter sizes to interrogate length-scale effects. The measured steady-state creep deformation is characterized with three different modeling approaches, that rely on: (i) conventional deviatoric creep potential; (ii) pressure-sensitive Drucker-Prager creep potential; and (iii) length-scale dependent deviatoric creep potential. The creep flow rule for all three cases is Norton’s power-law creep. The materials in this study are from a family of sintered silver materials used for interconnects and die-attach in high-temperature electronics and for conductor traces in printed electronics. The dissertation focuses on identifying and quantifying the length-scale dependence presented by sintered materials due to their non-homogeneous morphology. Testing consists of constant-force indentations using spherical indenters of two different radii at three different temperatures: 25°C, 75°C, and 125°C. The indentation results were first analyzed using two different post-processing methods: an empirical approach with closed-form models (CFM) and a computational FEA approach based on classical continuum mechanics. Differences found between the CFM and numerical (FEA) analyses, while significant at room temperature, reduce with temperature. Both models reveal that indenters of different radii cause significantly different viscoplastic behavior. This dependence on tip radius increases with temperature The research was extended to examine two second-order influences of the metallic agglomerated phase and the discontinuous compliant phase of the microstructure of sintered silver on its viscoplastic behavior: (i) dependence on hydrostatic stress; (ii) dependence on microstructural length-scale. The aim of incorporating the pressure-sensitive modeling was to investigate what effect the intrinsic compressive hydrostatic stress in indentation tests might have on the measured viscoplastic properties. Results from using the Drucker-Prager creep model further confirmed the increasing dependence on length-scale with temperature. The length-scale dependence seen in all the above results is investigated and quantified with the help of a simplified strain-gradient viscoplastic model. This modeling approach is motivated by the conventional mechanism-based strain gradient (CMSG) model that is widely used in plasticity theory to quantify length-scale effects. The characteristic length-scale metric in this problem is presented by the agglomerate size distribution in the sintered material and is quantified in this study with ‘watershed analysis’ of cross-sectional features observed via electron microscopy. This discrete length scale is believed to cause the variations in the observed creep response when queried with indenters of different radii, because of the different strain gradients produced by the two different indenters. The length-scale dependence is incorporated in a strain-gradient viscoplastic constitutive model suitable for finite element modeling of deformation fields containing strong strain-gradients (e.g. in the die attach layer in microelectronics chip assembly). Finally, a procedure is proposed, to incorporate the scale-dependence in the empirical closed-form approach, currently available in the literature, for extracting viscoplastic properties from indentation tests. This approach provides corrected model constants for the strain-gradient viscoplastic model, using simple closed-form equations instead of expensive finite element modeling.Item Phonon Transport and Nonequilibrium Kinetics with Stimulation Modeling in Molecular Crystals(2024) Liu, Zhiyu; Chung, Peter W.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)An important family of materials known as molecular crystals has been used extensively in fields such as organic semiconductors, energy, optoelectronics, and batteries. Due to their periodic crystal structure, phonons are the predominant heat and energy carriers. Phonons and their transport behaviors are crucial to the performance of semiconductors, the figure of merit of thermoelectrics, shock-induced properties of molecular crystals, and light-matter interactions of materials. Recent decades have seen significant advancements in the understanding of the phonon transport behaviors in inorganic crystals. However, a comprehensive understanding of phonon properties in molecular crystals is still lacking. While various theoretical models and computational simulations have been developed to study vibrational energy transfer in molecular crystals and to correlate vibrational structure with the stability of materials, these approaches often suffer from limitations. Many of these studies either neglect anharmonic scattering entirely or rely on simplified representations of phonon scattering. In this dissertation, we focus on investigating the phonon transport and nonequilibrium kinetics in molecular crystals. In the first work, we study the harmonic phonon properties of cellulose Iβ using tapered reactive force fields (ReaxFF). While geometry optimization with the original ReaxFF potential often results in structures with negative eigenvalues, indicating structural instability, the modified potential with a tapering function yields structures with no associated negative eigenvalues. Three ReaxFF parameterizations are evaluated by comparing lattice properties, elastic constants, phonon dispersion, temperature-dependent entropy, and heat capacity with experimental results from the literature. In the second study, we study the phonon transport behavior of Si, Cs2PbI2Cl2, cellulose Iβ, and α-RDX by calculating the thermal conductivity using different thermal transport models including the Phonon gas model, Cahill-Watson-Pohl, and the Allen-Feldman model and the Wigner formulation. By comparing the calculated thermal conductivity with experimental values, we highlight the significant contributions of wave-like heat carriers in cellulose Iβ and α-RDX. We show how different phonon properties influence particle-like and wave-like behavior in various materials and reveal unusual mechanisms present in molecular crystals. Lastly, we investigate nonequilibrium phonon kinetics resulting from direct vibrational excitations by employing the phonon Boltzmann transport equations. The results of our mid-IR pump-probe spectroscopy simulations align closely with experimental data from the literature. Additionally, by exciting different phonon modes at varying frequencies, we uncover distinct stages and pathways of vibrational energy transfer. To gain insights into the decomposition mechanism of RDX under excitation, we further calculate the bond activities of the N-N and N-O bonds, identifying possible stimuli that could trigger bond cleavage.Item Analyzing Inverse Design Problems from a Topological Perspective(2024) Chen, Qiuyi; Fuge, Mark; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Inverse design (ID) problems are inverse problems that aim to rapidly retrieve the subset of valid designs having the desired performances and properties under the given conditions. In practice, this can be solved by training generative models to approximate and sample the posterior distributions of designs. However, little has been done to understand their mechanisms and limitations from a theoretical perspective. This dissertation leverages theoretical tools from general and differential topology to answer these three questions of inverse design: what does a set of valid designs look like? How helpful are the data-driven generative models for retrieving the desired designs from this set? What topological properties affect the subset of desired designs? The dissertation proceeds by dismantling inverse (design) problems into two major subjects: that is, the representing and probing of a given set of valid designs (or data), and the retrieval of the desired designs (or data) from this given set. It draws inspiration from topology and geometry to investigate them and makes the main contributions below: 1. Chapter 3 details a novel representation learning method called Least Volume, which has properties similar to nonlinear PCA for representing datasets. It can minimize the representation's dimension automatically and, as shown in Chapter 4, conducts contrastive learning when applied to labeled datasets. 2. Two conditional generative models are developed to generate performant 2-D airfoils and 3-D heat sinks in Chapter 5 and 6 respectively. They can produce realistic designs to warm-start further optimization, with the relevant chapters detailing their acceleration effects. 3. Lastly, Chapter 7 describes how to use Least volume to solve high-dimensional inverse problems efficiently. Specifically, using examples from physic system identification, the chapter uncovers the correlation between the inverse problem's uncertainty and its intrinsic dimensions.Item SOCIOTECHNICAL NETWORK MODELING TO QUANTITATIVELY ANALYZE THE IMPACT OF BLOCKCHAIN ON THE RISK OF PROCURING COUNTERFEIT ELECTRONICS(2024) Akhavantaheri, Hirbod; Sandborn, Peter PS; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation develops sociotechnical agent-based network modeling to quantitatively analyze the impact of blockchain and other related policies on the supply-chain risk associated with the procurement of counterfeit electronics in critical systems. Safety-critical, mission-critical, and infrastructure-critical systems (e.g., aerospace, transportation, defense, and power generation) are forced to source parts from a supply chain that they do not control over exceptionally long periods of time. Critical systems are exposed to the dual risks of the impacts of system failure and the exposure to the vagaries of the marketplace over decades. Therefore, critical-systems operators, manufacturers, and sustainers, must implement policies and technologies to reduce the risk of obtaining counterfeit parts. Several policies, ranging from debarment and claw back to “hop counting,” have been considered and used to mitigate such risks. One technology that critical-system operators, manufacturers, and sustainers could adopt is distributed digital ledger (i.e., blockchain) for the supply chain. This dissertation does not focus on how such a blockchain could be implemented but rather on how (and if) blockchain for the supply chain can provide value for verifying the authenticity of parts when prolonged periods of time (decades) elapse between part manufacturing and part sourcing. Additionally, during a part’s ownership changes, supply-chain actors may choose to participate in the distributed ledger based on individual incentives and can recuse themselves from such participation later. The lack of complete participation may affect the designed functionality, and the consequences of lack of participation need to be understood. Using a comprehensive supply-chain model, it can be shown that blockchain for supply chain can reduce the prevalence of counterfeit electronics in the supply chain of critical systems by up to 70%. However, such a reduction requires near complete participation by all supply-chain stakeholders, which is not likely. Due to the relatively high cost of ownership transfer on a blockchain, and the indirect cost of supply-chain information disclosure, a high participation rate is not anticipated. Although blockchain can have benefits in other aspects of supply chain, it may not be a viable solution to combat the counterfeit electronics problem.Item Heat Transfer Augmentation of Microencapsulated Phase Change Material Slurry in Herringbone microchannel heat sink(2024) Dave, Anagh; Agonafer, Damena; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The advancement of technologies such as artificial intelligence (AI), the Internet of Things(IoT), and cloud computing has driven the miniaturization of electronic devices while significantly increasing their power densities and compactness. This evolution has led to higher heat fluxes that must be effectively managed to ensure device reliability and optimal performance. Effective thermal management is crucial, as inadequate cooling can result in thermal stresses, reduced efficiency, and potential component failure. While air-cooled systems were traditionally sufficient, the growing power demands of modern electronics have necessitated the adoption of more advanced cooling strategies. Microchannel heat sinks(MCHS), introduced in 1981, have been extensively studied for their ability to reduce junction temperatures. These heat sinks are part of various thermal management solutions, including single-phase liquid cooling, two-phase flow boiling, and jet impingement cooling in microchannels. Studies have shown that single-phase liquid cooling can effectively dissipate high heat fluxes of up to 1000 W/cm2. However, despite their effectiveness in heat dissipation, single-phase liquid cooling systems in microchannel heat sinks experience diminishing returns in efficiency due to high-pressure drops at higher volumetric flow rates. Two-phase or multiphase cooling strategies have also been studied to overcome these limitations. These approaches rely on the phase change of the coolant and leverage the high heat capacity to improve heat transfer efficiency while maintaining a high thermal-hydraulic performance. However, they face challenges related to the flow instabilities during boiling and diminished heat removal rate at critical heat fluxes, which can undermine reliability. A promising alternative involves suspending microencapsulated phase change material (MEPCM) particles in a thermal fluid to create a slurry. MEPCM slurries typically consist of a base fluid, like water, mixed with MEPCM particles that enhance effective heat capacity through their high specific and latent heat. These particles absorb and release heat during phase transitions, significantly improving heat dissipation and storage in thermal-fluid systems. However, despite their high thermal capacity, the relatively low thermal conductivity of MEPCM particles can hinder their ability to melt uniformly and transfer heat effectively. To address these challenges, this study investigates a novel heat transfer enhancement approach by incorporating herringbone microstructures within microchannels to induce helical mixing. The herringbone design facilitates out-of-plane mixing, which promotes the effective utilization of MEPCM particles and enhances heat transfer without the instabilities associated with traditional two-phase boiling. This creates a pseudo ’two-phase’ flow within the microchannel heat sink, allowing MEPCM slurries to achieve high thermal performance, balancing effective heat transfer with reduced flow instabilities and manageable pressure drops.Item DEVELOPMENT OF A NEAR-ISOTHERMAL COMPRESSION PROCESS UTILIZING LIQUID PISTON TECHNOLOGY FOR TRANSCRITICAL CO2 CYCLE(2024) Lee, Cheng-Yi; Radermacher, Reinhard; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Compressors are critical components in various industries and are commonly used in numerous applications. With the increasing concerns about global warming, there has been a significant focus on improving compressor efficiency, a subject of extensive research and innovation. This is particularly true for the heating, ventilation, air conditioning, and refrigeration industries since almost every household relies on air conditioning and refrigeration systems. Despite numerous proposed and investigated technologies for efficiency improvements, there remains substantial potential for further advancements. The compression process is often modeled as an isentropic process in which a significant portion of the work provided during compression is converted to heat, increasing the overall input power required. This inefficiency highlights the ongoing need for innovative solutions to reduce the input power of compressors. This dissertation primarily focuses on the experimental development and theoretical investigation of a near-isothermal liquid piston compressor in a transcritical CO2 cycle. The liquid piston compressor employs a liquid piston instead of traditional mechanical pistons to compress gas. This design offers high volumetric efficiency and allows for flexible compression chamber geometries. This innovative compressor design discharges compression heat and internal energy as a form of heat through a compressor-integrated gas cooler during compression. Three successively improved prototypes were constructed to validate the concept and enhance the system. A proof-of-concept test facility was fabricated to demonstrate the feasibility of this design. Furthermore, a complete refrigeration cycle system incorporating the liquid piston compressor was developed. Based on the experimental results, an improved second prototype was built and sent to the Helix Innovation Center of Copeland for field testing. The results show that the isothermal efficiency achieved is 93.5% in the proof-of-concept tests with a self-manufactured copper bare tube heat exchanger as the compression chamber. 90% isothermal efficiency was observed in the first system prototype with a microchannel heat exchanger, and 89 % isothermal efficiency in the second system prototype. The highest compressor coefficient of performance (COP) achieved was 1.82 in the second system prototype. This performance was observed under an average suction pressure of 3,800 kPa and a gas cooler pressure of 10,000 kPa under 35°C ambient temperature. Simulations revealed that the near-isothermal liquid piston compressor could achieve high isothermal efficiency by using heat transfer through the compression chamber and the chamber's thermal mass. This technology's potential applications extend beyond refrigeration, including compressed air energy storage, hydrogen storage, and compressed natural gas systems. These applications were investigated and discussed, highlighting this innovative compressor design's versatility and potential impact. The liquid piston compressor developed in this study exhibits substantial potential for reducing compression work, as supported by both experimental data and simulation modeling. Theintegrated gas cooler in the liquid piston compressor facilitates near-isothermal compression by effectively dissipating both compression heat and internal energy as a form of heat. This heat discharge enhances compression efficiency and improves overall system performance. Future work will prioritize selecting a hydraulic fluid with minimal solubility for CO2 to mitigate degassing issues during compression. Additionally, current market-available pumps do not adequately meet the requirements of the transcritical CO2 cycle. Therefore, developing a semi-hermetic pump will be crucial for the next generation of transcritical CO2 liquid piston compressors. Finally, integrating this pump with an optimized gas cooler and achieving a size comparable to traditional compressors will be essential to making the developed device commercially competitive.Item ML-ENABLED SOLAR PV ELECTRICITY GENERATION PROJECTION FOR A LARGE ACADEMIC CAMPUS TO REDUCE ONSITE CO2 EMISSIONS(2024) Zargarzadeh, Sahar; Babadi, Behtash; Ohadi, Michael; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Mitigating CO2 emissions is crucial in reducing climate change, as these emissions contribute to global warming and its adverse impacts on ecosystems. According to statistics, photovoltaic electricity is 15 times less carbon-intensive than natural gas and 30 times less than coal, making Solar Photovoltaic an attractive option among various methods of reducing electricity demand. This study aims to apply Machine Learning to predict future impact of solar PV-Generated electricity in reducing CO2 emissions based. The primary utility data source is from the University of Maryland's campus; with over half of the campus's energy consumption derived from electricity, therefore reducing electricity consumption to mitigate carbon emissions is paramount. 153 buildings on the campus were investigated, spanning the years 2015-2022. This study was conducted in four key phases. In the first phase, an open source tool, PVWatts was used to gather data to predict PV-generated energy. This served as the foundation for phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity on any period of time, leveraging machine learning to capture complex patterns in energy data for more accurate forecasts. The SHAP (SHapley Additive exPlanations) technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in the phase IV to project future CO2 emissions post-solar PV installation. This comparison facilitated the evaluation of different machine learning techniques for projecting emissions and assessing the university’s progress toward Maryland’s sustainability objectives. The ML-based tool developed in this study demonstrated that solar PV implementation could potentially reduce the campus’s footprint by approximately 18% for the studied clusters of buildings with the uncertainty level of about 1.7%, contributing to sustainability objectives and the promotion of cleaner energy use.Item EXCURSION IN THE QUANTUM LOSS LANDSCAPE: LEARNING, GENERATING AND SIMULATING IN THE QUANTUM WORLD(2024) Rad, Ali; Hafezi, Mohammad; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Statistical learning is emerging as a new paradigm in science. This has ignited interestwithin our inherently quantum world in exploring quantum machines for their advantages in learning, generating, and predicting various aspects of our universe by processing both quantum and classical data. In parallel, the pursuit of scalable science through physical simulations using both digital and analog quantum computers is rising on the horizon. In the first part, we investigate how physics can help classical Artificial Intelligence (AI) by studying hybrid classical-quantum algorithms. We focus on quantum generative models and address challenges like barren plateaus during the training of quantum machines. We further examine the generalization capabilities of quantum machine learning models, phase transitions in the over-parameterized regime using random matrix theory, and their effective behavior approximated by Gaussian processes. In the second part, we explore how AI can benefit physics. We demonstrate how classical Machine Learning (ML) models can assist in state recognition in qubit systems within solid-state devices. Additionally, we show how ML-inspired optimization methods can enhance the efficiency of digital quantum simulations with ion-trap setups Finally, in the third part, we focus on how physics can help physics by using quantum systems to simulate other quantum systems. We propose native fermionic analog quantum systems with fermion-spin systems in silicon to explore non-perturbative phenomena in quantum field theory, offering early applications for lattice gauge theory models.Item MOLD PROCESS INDUCED RESIDUAL STRESS PREDICTION USING CURE EXTENT DEPENDENT VISCOELASTIC BEHAVIOR(2024) Phansalkar, Sukrut Prashant; Han, Bongtae; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Epoxy molding compounds (EMC) are widely used in encapsulation of semiconductor packages. Encapsulation protects the package from physical damage or corrosion due to harsh environments. Molding processes produce residual stresses in encapsulated components. They are combined with the stresses caused by the coefficient of thermal expansion (CTE) mismatch to dictate the final warpage at room and reflow temperatures, which must be controlled for fabrication of redistribution layer (RDL) as well as yield during assembly. During molding process, EMC is continuously curing and the mechanical properties continue to evolve; more specifically, the equilibrium modulus and the relaxation modulus. The former defines behavior after complete relaxation while the latter describes the transient behavior. It is thus necessary to measure cure-dependent viscoelastic properties of EMC to be able to determine mold induced residual stresses accurately. This is the motivation for this thesis. In this thesis, a set of novel methodologies are developed and implemented to quantify a complete set of cure-dependent viscoelastic properties, including the fully cured properties. Firstly, an advanced numerical scheme has been developed to quantify cure kinetics of thermosets with both single and dual-reaction systems. Secondly, a unique methodology is proposed to measure the viscoelastic bulk modulus -K(t,T) of EMC using hydrostatic testing. The methodology is implemented with a unique test setup based on inert gas. The results of viscoelastic testing along with the shear modulus (G) and bulk modulus (K) master curves and temperature-dependent shift factors (a(T)) of fully-cured EMC are presented. Thirdly, a novel test methodology utilizing monotonic testing has been developed to measure two sets of equilibrium moduli of EMC as a function of cure extent (p). The main challenge for the measurements is that the equilibrium moduli could only be measured accurately only when the EMC has fully relaxed. The temperatures for complete relaxation typically occur above the glass transition temperature, Tg (p), where the curing rate is also high. A special measurement procedure is developed, through which the EMC moduli above Tg can be determined quickly at a near isocure state. Viscoelastic testing of partially-cured EMC is followed to determine the cure-dependent shift factors of EMC. The test durations have to be very long (several hours) and it is conducted below Tg (p) of the EMC where the reaction is slow (under diffusion-control) Finally, a numerical scheme that can utilize the measured cure-dependent viscoelastic properties is developed. It is implemented to predict the residual stress evolution of molded packages during and after molding processes.