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 DEEPER DIVE INTO THE WATER: A COMPARISON OF HYDROLOGIC FEATURES AS VARIABLES IN PRECONTACT SITE LOCATION PREDICTIVE MODELS FOR THE VIRGINIA PIEDMONT(2024) Johnson, Jeffrey Wade; Palus, Matthew M; Anthropology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The use of predictive modeling in Cultural Heritage Resource Management (CHRM) archaeology has become commonplace since its foundational principals were established in the 1980s, but criticisms of the practice persist, often centered around their lack of theory and dehumanization of the archaeological record. Proximity to water, typically expressed in the United States as distance to streamline data from the National Hydrography Dataset (NHD), is one of the most utilized variables when creating predictive models for Precontact period sites, but how does the variable “distance to streamline” compare to other hydrologic variables? In this thesis I seek to answer the question “how do distance to stream confluences and distance to wetlands compare to distance to streamline when attempting to predict Precontact site locations in the Virginia Piedmont?”The publication Quantifying the Present and Predicting the Past: Theory, Method, and Application of Archaeological Predictive Modeling (Altschul et al. 1988) is considered foundational to the practice of predictive modeling in archaeology; it is referenced frequently in modern theoretical works and throughout this thesis. The approaches to creating archaeological predictive models are typically divided into two camps: models that utilize an inductive, or correlative, approach and models that utilize a deductive, or theory driven, approach. Rather than establishing distance correlations between wetlands and stream confluences with previously recorded site data, I utilize a deductive approach where I establish the importance of those variables through archaeological theory pertaining to subsistence and settlement patterns and test their value with site data. Inductive associational models are very good at showing that archaeological site distribution is strongly patterned, but they often lack the explanatory framework that would be useful for management decisions based on their findings. The Study Area the models are tested on is located within Orange County, Virginia near the town of Locust Grove, and encompasses about 686 acres. The Study Area contains two main streams, named Cormack Run and Mine Run, the confluence of those streams and other lower order streams, as well as wetlands located adjacent to the streams. Precontact occupations have likely occurred in this region for the past 12,000 years, if not longer. The test results demonstrate that models created using deductively derived variables perform well enough to justify their use in CHRM contexts, but also include the added benefit of an explanatory framework. The guidelines for archaeological investigations in Virginia allow for the use of predictive models when conducting inventory surveys, meaning the archaeological predictive models (APM) created for this thesis could be utilized in a real-world context. The primary focus of this thesis was to determine if using hydrologic features other than streams, specifically stream confluences and wetlands, to express the distance to water variable would improve the performance of an APM. I demonstrated that, yes, other hydrologic features may be better predictors of Precontact site locations in the Virginia Piedmont. Secondarily, I hoped to show that an APM created using a deductive approach would perform well enough to be considered appropriate for use in CHRM contexts. The high probability areas of all three of the APMs I created yielded Kg values high enough to be considered as having predictive utility. This demonstrates that the use of all three of the APMs I created could be considered appropriate to guide survey efforts in a CHRM context.Item OPTIMAL PROBING OF BATTERY CYCLES FOR MACHINE LEARNING-BASED MODEL DEVELOPMENT(2024) Nozarijouybari, Zahra; Fathy, Hosam HF; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation examines the problems of optimizing the selection of the datasets and experiments used for parameterizing machine learning-based electrochemical battery models. The key idea is that data selection, or “probing” can empower such models to achieve greater fidelity levels. The dissertation is motivated by the potential of battery models to enable theprediction and optimization of battery performance and control strategies. The literature presents multiple battery modeling approaches, including equivalent circuit, physics-based, and machine learning models. Machine learning is particularly attractive in the battery systems domain, thanks to its flexibility and ability to model battery performance and aging dynamics. Moreover, there is a growing interest in the literature in hybrid models that combine the benefits of machine learning with either the simplicity of equivalent circuit models or the predictiveness of physics-based models or both. The focus of this dissertation is on both hybrid and purely data-driven battery models. Moreover, the overarching question guiding the dissertation is: how does the selection of the datasets and experiments used for parameterizing these models affect their fidelity and parameter identifiability? Parameter identifiability is a fundamental concept from information theory that refers to the degree to which one can accurately estimate a given model’s parameters from input-output data. There is substantial existing research in the literature on battery parameter identifiability. An important lesson from this literature is that the design of a battery experiment can affect parameter identifiability significantly. Hence, test trajectory optimization has the potential to substantially improve model parameter identifiability. The literature explores this lesson for equivalent circuit and physics-based battery models. However, there is a noticeable gap in the literature regarding identifiability analysis and optimization for both machine learning-based and hybrid battery models. To address the above gap, the dissertation makes four novel contributions to the literature. The first contribution is an extensive survey of the machine learning-based battery modeling literature, highlighting the critical need for information-rich and clean datasets for parameterizing data-driven battery models. The second contribution is a K-means clustering-based algorithm for detecting outlier patterns in experimental battery cycling data. This algorithm is used for pre-cleaning the experimental cycling datasets for laboratory-fabricated lithium-sulfur (Li-S) batteries, thereby enabling the higher-fidelity fitting of a neural network model to these datasets. The third contribution is a novel algorithm for optimizing the cycling of a lithium iron phosphate (LFP) to maximize the parameter identifiability of a hybrid model of this battery. This algorithm succeeds in improving the resulting model’s Fisher identifiability significantly in simulation. The final contribution focuses on the application of such test trajectory optimization to the experimental cycling of commercial LFP cells. This work shows that test trajectory optimization is s effective not just at improving parameter identifiability, but also at probing and uncovering higher-order battery dynamics not incorporated in the initial baseline model. Collectively, all four of these contributions show the degree to which the selection of battery cycling datasets and experiments for richness and cleanness can enable higher-fidelity data-driven and hybrid modeling, for multiple battery chemistries.Item ASSESSING THE IMPACT OF ELECTROCHEMICAL-MECHANICAL COUPLING ON CURRENT DISTRIBUTION AND DENDRITE PREVENTION IN SOLID-STATE ALKALI METAL BATTERIES(2023) Carmona, Eric Alvaro; Albertus, Paul; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The relationship between mechanical stress states and interfacial electrochemical thermodynamics of Li metal/Li6.5La3Zr1.5Ta0.5O12 and Na metal/Na-β”-Al2O3 systems are examined in two experimental configurations with an applied uniaxial load; the solid electrolytes were pellets and the metal electrodes high-aspect-ratio electrodes. Our experimental results demonstrate that (1) the change in equilibrium potential at the metal/electrolyte interface, when stress is applied to the metal electrode, is linearly proportional to the molar volume of the metal electrode, and (2) the mechanical stress in the electrolyte has negligible effect on the equilibrium potential for an experimental setup in which the electrolyte is stressed and the electrode is left unstressed. Solid mechanics modeling of a metal electrode on a solid electrolyte pellet indicates that pressure and normal stress are within ~0.5 MPa of each other for the high aspect ratio (~1:100 thickness:diameter in our study) Li metal electrodes under loads that exceed yield conditions. To assess the effect of electrochemical-mechanical coupling on current distributions at Li/single-ion conducting solid ceramic electrolyte interfaces containing a parameterized interfacial geometric asperity, we develop a coupled electrochemical-mechanical model and carefully distinguish between the thermodynamic and kinetic effects of interfacial mechanics on the current distribution. We find that with an elastic-perfectly plastic model for Li metal, and experimentally relevant mechanical initial and boundary conditions, the stress variations along the interface for experimentally relevant stack pressures and interfacial geometries are small (e.g., <1 MPa), resulting in a small or negligible influence of the interfacial mechanical state on the interfacial current distribution for both plating and stripping. However, we find that the current distribution is sensitive to interface geometry, with sharper (i.e., smaller tip radius of curvature) asperities experiencing greater current focusing. In addition, the effect on the current distribution of an identically sized lithium peak vs. valley geometry is not the same. These interfacial geometry effects may lead to void formation on both stripping and plating and at both Li peaks and valleys. This work advances the quantitative understanding of alkali metal dendrite formation within incipient cracks and their subsequent growth, and pore formation upon stripping, both situations where properly accounting for the impact of mechanical state on the equilibrium potential can be of critical importance for calculating the current distribution. The presence of high-curvature interface geometry asperities provides an additional perspective on the superior cycling performance of flat, film-based separators (e.g., sputtered LiPON) versus particle-based separators (e.g., polycrystalline LLZO) in some conditions.Item Simulating membrane-bound cytoskeletal dynamics(2023) Ni, Haoran; Papoian, Garegin A.; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The cell membrane defines the shape of the cell and plays an indispensable role in bridging intra- and extra-cellular environments. The membrane, consisting of a lipid bilayer and various attaching proteins, mechanochemically interacts with the active cytoskeletal network that dynamically self-organizes, playing a vital role in cellular biomechanics and mechanosensing. Comprehensive simulations of membrane-cytoskeleton dynamics can bring insight in understanding how the cell mechanochemically responds to external signals, but a computational model that captures the complex cytoskeleton-membrane with both refined details and computational efficiency is lacking. To address this, we introduce in this thesis a triangulated membrane model and incorporate it with the active biological matter simulation platform MEDYAN ("Mechanochemical Dynamics of Active Networks"). This model accurately captures the membrane physical properties, showing how the membrane rigidity, the structure of actin networks and local chemical environments regulate the membrane deformations. Then, we present a new method for simulating membrane proteins, using stochastic reaction-diffusion sampling on unstructured membrane meshes. By incorporating a surface potential energy field into the reaction-diffusion sampling, we demonstrate rich membrane protein collective behaviors such as confined diffusion, liquid-liquid phase separation and membrane curvature sensing. Finally, in order to capture stretching, bending, shearing and twisting of actin filaments which are not all available with traditional actomyosin simulations, we introduce new finite-radius filament models based off Cosserat theory of elastic rods, with efficient implementation using finite-dimensional configurational spaces. Using the new filament models, we show that the filaments' torsional compliance can induce chiral symmetry breaking in a cross-linked actin bundle. All the new models are implemented in the MEDYAN platform, shedding light on whole cell simulations, paving way for a better understanding of the membrane-cytoskeleton system and its role in cellular dynamics.Item DETERMINING ELONGATION AT BREAK OF CABLE INSULATIONS USING CONDITION MONITORING PARAMETERS(2022) Gharazi, Salimeh; Al-Sheikhly, Mohamad; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Many United States nuclear power plants are seeking to renew life licenses to extend the operational life of the plant to an additional 20 or 40 years. Degradation of insulation and jacket of cables, which are originally designed for 40 years in the second round of operation, is a critical issue which can impair the safe and reliable function of cables and ultimately the plant. The main criterion for assessing the end of life of these insulations is defined when the elongation at break reaches 50% of its original value. However, measuring the elongation at break is done by tensile tests, which are destructive and need large samples; the feasibility of these tests is significantly limited on installed cables at nuclear power plants. A new model was developed to relate the changes in the activation energy corresponding to EAB in terms of the changes in the activation energies corresponding to non-destructive condition monitoring, NDE-CM, parameters. The coefficients of the model are obtained by normalizing the calculated activation energy of each CM parameter’s changes with the activation energy of EAB changes. Therefore, it is possible to estimate EAB values, in the present developed equations, from the substitution of activation energy corresponding to EAB changes with the correlated activation energy of the non-destructive condition monitoring parameters. Cable Polymer Aging database, C-PAD, which is provided by Electric Power Research Institute, and supported by the U.S. Department of Energy, along with experimental results done in the University of Maryland, UMD, laboratory was used as the database. While taking advantage of C-PAD database which contains condition monitoring parameters of insulation cables such as Elongation at break, Modulus and Density provided by many U.S. and international research institutes, extensive aging experimental results on two cables, each with two grades provided us with not only a database but also a better understanding of the aging mechanism. The published experimental results of cable insulations are used to validate the model. A good fit between the experimental and modeled results confirms the validity of the model.Item Discerning the roles of ocean acidification, eutrophication, and river alkalization in driving long-term pH trends in the Chesapeake Bay(2022) Guo, Yijun; Li, Ming; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rising anthropogenic CO2 in the atmosphere and oceanic uptake of CO2 have led to a gradual decrease in seawater pH and ocean acidification, but pH changes in estuaries and coastal systems are more complicated due to a multitude of global and regional environmental drivers. Increasing global fertilizer use due to agricultural production has led to a doubling of riverine nutrient loading since the 1950s, leading to widespread eutrophication in estuarine and coastal waters. Excessive nutrient loading stimulates primary production in the surface euphotic layer, which consumes CO2 and elevates pH, but unassimilated organic matter sinks and decomposes in bottom waters, producing CO2 and reducing pH. In the meantime, human-accelerated chemical weathering, such as acid rain and mining, has resulted in rising alkalinity in many rivers and basification in estuarine and coastal waters. To discern how these environmental drivers influence long-term pH trends in coastal waters, a coupled hydrodynamic-biogeochemical-carbonate chemistry model was used to conduct hindcast simulations of the Chesapeake Bay between 1951 and 2010. The model reproduced the observed chlorophyll increase and hypoxia expansion due to the increased nutrient loading. In contrast, low-pH bottom waters and acidic volume shrank from 1950 to 1980. GAM analysis of long-term pH trends in different regions of Chesapeake Bay revealed increasing pH in the upper Bay driven by the river alkalinization, a peak pH in the mid-Bay in the 1980s coincident with the peak nutrient loading and decreasing pH in the lower Bay driven by ocean acidification. Four scenario runs were performed to assess the individual effects of rising pCO2, river alkalinization, riverine nutrient loading, and climate change (warming and sea-level rise) on long-term pH changes in the Chesapeake Bay. The model results suggested that river alkalinization was more important than ocean acidification in driving the long-term pH changes in the estuary.Item ENERGY CONSUMPTION REDUCTION OF COMMERCIAL BUILDINGS THROUGH THE IMPLEMENTATION OF VIRTUAL AND EXPERIMENTAL ENERGY AUDIT ANALYSIS(2022) Bae, Ji Han; Ohadi, Michael; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)According to the U.S. Energy Information Administration (EIA), about 38 quads of the total U.S. energy consumption was consumed by residential and commercial buildings in 2017, which is about 39% of the total 2017 annual U.S. energy consumption (EIA, 2018). Additionally, the building sector is responsible for about 75% of the total U.S. electricity consumption as well as for about 70% of the projected growth in the U.S. electricity demand through 2040. It is clear that the potential for energy savings and greenhouse gas emissions reduction in existing buildings today remain largely untapped and that there is still much left to explore in respect to determining the best protocols for reducing building energy consumption on a national and even a global scale. The present work investigates the effectiveness of coupling an initial virtual energy audit screening with the conventional, hands-on, energy audit processes to more quickly and less costly obtain the potential energy savings for high energy consumption buildings. The virtual screening tool takes advantage of a customized cloud-based energy efficiency management software and the readily available building energy consumption data to identify the buildings that have the highest energy savings potential and should be given priority for performing onsite walkthroughs, detailed energy audits, and the subsequent implementation of the identified energy conservation measures (ECMs). By applying the proposed procedure to a group of buildings, the results of this study demonstrated that a combination of the software-based screening tools and a detailed experimental/onsite energy audit as necessary can effectively take advantage of the potential energy consumption and carbon footprint reduction in existing buildings today and that the low-cost/no-cost energy conservation measures alone can oftentimes result in significant savings as documented in this thesis. However, selection of the appropriate software was deemed critically important, as certain software limitations were observed to hinder the obtainment of some energy savings opportunities.Item Trajectory Optimization of a Tethered Underwater Kite(2021) Alvarez Tiburcio, Miguel; Fathy, Hosam; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation addresses the challenge of optimizing the motion trajectory of a tethered marine hydrokinetic energy harvesting kite in order to maximize its average electric power output. The dissertation focuses specifically on the “pumping” kite configuration, where the kite is periodically reeled out from a floating base station at high tension, then reeled in at low tension. This work is motivated by the significant potential for sustainable electricity generation from marine currents such as the Gulf Stream. Tethered systems can increase their energy harvesting potential significantly through cross-current motion. Such motion increases apparent flow speed, which is valuable because the instantaneous maximum power that can be harvested is proportional to the cube of this apparent speed. This makes it possible for tethered systems to achieve potentially very attractive power densities and levelized costs of electricity compared to stationary turbines. However, this also necessitates the use of trajectory optimization and active control in order to eke out the maximum energy harvesting capabilities of these systems. The problem of optimizing the trajectories of these kites is highly non-linear and thus challenging to solve. In this dissertation we make key simplifications to both the modeling and the structure of the optimal solution which allows us to learn valuable insights in the nature of the power maximizing trajectory. We first do this analysis to maximize the average mechanical power of the kite, then we expand it to take into account system losses. Finally, we design and fabricate an experimental setup to both parametrize our model and validate our trajectories. In summary, the goal of this research is to furnish model-based algorithms for the online optimal flight control of a tethered marine hydrokinetic system. The intellectual merit of this work stems from the degree to which it will tackle the difficulty of solving this co-optimization problem taking into account overall system efficiency and the full range of possible system motion trajectories. From a broader societal perspective, this work represents a step towards experimentally validating the potential of pumped underwater kite systems to serve as renewable energy harvesters in promising environments such as the Gulf Stream.Item CHARACTERIZATION AND ANALYSIS OF FLUIDIC ARTIFICIAL MUSCLES(2021) Chambers, Jonathan Michael; Wereley, Norman M; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Fluidic artificial muscles (FAMs) are a form of soft actuator that have been applied to an expanding number of applications, due to their unique characteristics such as low weight, simple construction, inherent compliance, and high specific force and specific work capabilities. With energy sourced from a pressurized fluid, contractile FAMs provide a uniaxial contractile force, while their morphing geometry allows them to contract in length. In a design environment where actuators have tight spatial requirements and must provide precise force and position control, it is becoming more important than ever to have accurate mathematical representations of FAM actuation behavior and geometric characteristics to ensure their successful implementation. However, geometric models and force analyses for FAMs are still relatively crude. Geometric models of FAMs assume a cylindrical geometry, the accuracy of which is suspect because there are no documented methods for effectively measuring FAM shape. Actuation force analyses are also relatively inaccurate unless they are adjusted to fit to experimental response data. Research has continually pursued methods of improving the predictive performance of these analyses by investigating the complex working mechanisms of FAMs. This research improves these analyses by first, making improvements to the experimental characterization of a FAM's actuation response, and then using the more comprehensive data results to test long-held modeling assumptions. A quantitative method of measuring FAM geometry is developed that provides 0.004 in/pixel resolution measurements throughout a characterization test. These measurements are then used to test common assumptions that serve as sources of uncertainty: the cylindrical approximation of FAM geometry, and assumption that the FAM's braid is inelastic. Once these sources of modeling error are removed, the model's performance is then tested for potential improvements. Results from this research showed that the cylindrical approximation of the FAM's geometry resulted in overestimations of the FAM's average diameter by 4.7%, and underestimations of the FAM's force by as much as 37%. The inelastic braid assumption resulted in a maximum 4% underestimation of average diameter and a subsequent 5% overestimation in force, while the use of softer braid materials was found to have the potential for much larger effects (30% underestimation in diameter, 70% overestimation in force). With subsequent adjustments made to the force model, the model was able to achieve a fit with a mean error of only 2.8 lbf (0.3% of maximum force). This research demonstrates improvements to the characterization of a FAM's actuation response, and the use of this new data to improve the fidelity of existing FAM models. The demonstrated characterization methods can be used to clearly define a FAM's geometry to aid in the effective design and implementation of a FAM-actuated mechanism, or to serve as a foundation for further investigation into the working mechanisms and development of FAMs.Item EVALUATION OF IMPACT OF NOVEL BARRIER COATINGS ON FLAMMABILITY OF A STRUCTURAL AEROSPACE COMPOSITE THROUGH EXPERIMENTS AND MODELING(2021) Crofton, Lucas; Stoliarov, Stanislav; Fire Protection Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Composites have become a integral part of the structure of airplanes, and their use within aircraft continues to grow as composites continue to improve. While polymer composites are an improvement in many facets to traditional airspace materials, their flammability is something called into question. The work performed for this study was to create a pyrolysis model for a particular aerospace composite, IM7 graphite fiber with Cytec 5250-4 Bismaleimide matrix (BMI), and three innovative composite barrier coatings that could be applied to the BMI to potentially improve its performance in fire scenarios. The composites were all tested individually, in a series of milligram-scale tests, and the test results were inversely analyzed to determine stoichiometry, chemical kinetics, and thermodynamics of their thermal decomposition and combustion. Gram-scale experiments using the Controlled Atmosphere Pyrolysis Apparatus II (CAPA II) were performed on the BMI by itself and then again with one of each of the composite barrier coatings applied in a defined thickness. This data were inversely analyzed to define the thermal conductivity of the sample and resolve it’s emissivity. It was found after fully defining a pyrolysis model for each composite material that the composite barrier coatings did not provide any benefit to the base composite BMI, and only added more fuel load which in turn contributed to a increase in heat release rate when computational simulations were run to mimic a airplane fuel fire.