Design Strategy For Hybrid Thrust Air Bearings: Comparative Analysis Of Rigid Vs. Foil Bearings Considering Optimum Taper Angle And Orifice Location With Experimental Validation, Ehiremen Ebewele

Mechanical and Aerospace Engineering Dissertations - Archive

Gas foil thrust bearings (GFTBs) are contactless bearings that offer advantages such as lightweight construction and the ability to accommodate misalignments and geometric irregularities. However, their load capacity is lower than rigid or magnetic bearings. The structure and geometry of the foil significantly influence GFTB performance. The taper-flat design is the most used configuration due to its effectiveness and ease of implementation. Key parameters in designing this geometry include taper ratio, taper height, orifice size, and orifice location. These parameters must be optimized alongside manufacturing constraints to produce an effective GFTB. This study presents a design optimization investigation of various …

Experimental And Numerical Modelling-Based Optimization Of Additive Manufacturing Processes, Vishnu V. Ganesan

Mechanical and Aerospace Engineering Dissertations - Archive

ABSTRACT

Experimental and Numerical Modeling-Based Optimization of Additive Manufacturing Processes

Vishnu V Ganesan, Ph.D.

The University of Texas at Arlington, 2025

Supervising Professor: Dr. Ankur Jain

Experimental and numerical modeling play a pivotal role in advancing additive manufacturing technologies by enabling a deeper understanding of complex, multi-physics processes that govern part quality, performance, and reliability. These manufacturing techniques—ranging from Powder Bed Fusion (PBF) and Material Extrusion (MEX) to Automated Fiber Placement (AFP)—involve tightly coupled thermal, mechanical, and material phenomena that are challenging to capture through empirical observation alone. Experimental methods offer critical validation and insights into real-world behavior, while numerical …

Intelligent Microfluidic Systems For Precision Manipulation And Real-Time Recognition Via Dielectrophoresis And Deep Learning, Negar Danesh

Mechanical and Aerospace Engineering Dissertations - Archive

This dissertation introduces intelligent microfluidic platforms by combining advanced DEP-based manipulation with real-time visual feedback. A DEP device featuring circular corral traps and dual-plane electrodes enables precise submicron particle trapping, high-resolution particle separation, and cell-particle co-assembly. Simulations and experiments confirm enhanced electric field control and stable confinement. To enable adaptive operation in EWOD systems, a deep learning model (U-Net) was developed for real-time droplet meniscus segmentation. The model achieved 98% accuracy and remained robust under noisy, low-contrast conditions. A live video pipeline was implemented, enabling consistent frame-by-frame feedback for closed-loop control. Together, these innovations establish a foundation for autonomous, high-performance …

Nonlinear Bump Stiffness Model And Its Effect On Structural Stiffness And Nonlinear Rotordynamic Characteristic Of Foil Bearing, Woongeon Lee

Mechanical and Aerospace Engineering Dissertations - Archive

Bump foils are the most widely used in foil bearings, but the behaviors of bump foils are complicated, and their characteristics have been a focus of research for decades. Bump foils are usually modeled as stiffness and damping are accounted for through interactions with shaft eccentricity, loading, shaft speed and excitation frequency. These nonlinear characteristics of the bump foil of radial foil bearings can be observed during both manufacturing and operational processes because of their inherent structural properties such as bump geometry, forming process, age-hardening process and complicated contact behavior with bearing housing. These nonlinear characteristics are one of the …

Multi-Objective Design Optimization Of Hypoid Geared Rotor Systems, Xinqi Wei

Mechanical and Aerospace Engineering Dissertations - Archive

Hypoid gears represent one of the most generalized and complex forms of gearing, widely used for power transmission of skew shafts in vehicles, aviation, and marine transmission applications. Optimizing their performance remains challenging due to the complex tooth surface and contact behavior. Specifically, the design parameters of the tooth surface are multi-scale, interdependent, and subject to strong constraints, leading to strong nonlinearity and an ill-conditioned Jacobian matrix in the parameter identification model. Moreover, feasible and insensitive contact conditions are difficult to constrain due to the inherent complexity of local conjugate contact between the meshing surfaces. These challenges significantly increase optimization …

Computational Study Of Detonation Wave Propagation And Propellant Injection In Detonation Engines, Jayson C. Small

Mechanical and Aerospace Engineering Dissertations - Archive

This research investigates the physics of detonation waves and their application to detonation engines, with two primary objectives: (1) to characterize detonation wave propagation, specifically detonation speed and wavefront thickness, in stoichiometric propane-oxygen mixtures near quenching conditions, and (2) to examine the physiochemical processes during propellant injection under detonation engine conditions, focusing on flush-wall-mounted, fluidic-valve injectors.

A multi-fidelity computational approach was employed. Reduced-order modeling based on the Chapman–Jouguet and Zeldovich–von Neumann–Doring models were used to evaluate detonation parameters and select an appropriate chemical mechanism. High-fidelity, multi-dimensional simulations solved the unsteady, compressible, reacting flows with finite-rate chemistry, using Reynolds-Averaged Navier–Stokes equations …

Topology-Driven Performance Analyses In Consensus Algorithms For Multi-Agent Systems, Brandon Ayala

Mechanical and Aerospace Engineering Theses - Archive

This thesis presents a simulation-based analysis of consensus algorithms in multi-agent systems, focusing on how network topology influences convergence performance. Both first-order and second-order linear consensus dynamics were examined across a variety of network configurations, including undirected and directed versions of cycle graphs, star graphs, minimum spanning trees, and fully connected graphs. In addition to standard consensus problems, leader-follower network structures were introduced to explore the impact of leader placement on convergence behavior, and extensions to multi-leader systems were analyzed to study robustness and convergence under multiple reference inputs. Graph-theoretic properties such as connectivity, degree distribution, and Laplacian eigenvalues were …

Multi-Agent Differential Games Under An Altruistic Equilibrium, Craig Alan Lovell

Mechanical and Aerospace Engineering Theses - Archive

This work studies a multi-agent differential game with linear dynamics under the Berge equilibrium. The governing coupled differential equations for a two-agent and a three-agent game under the Berge equilibrium are derived. These games are simulated and compared to the Nash equilibrium. A sensitivity study is performed which validates that, under some criteria, the Nash equilibrium can be recovered from the Berge equilibrium. Policy fusion between the Berge and Nash equilibrium is explored in a two-agent game. A five-agent game under the Berge equilibrium is simulated and multiple teams of agents in this game are evaluated. Finally, a mixed game, …

Deep Neural Network Models For Heatsink Performance Prediction And Optimization In Single Phase Immersion Cooling: Framework For Future Design Tools And Digital Twin Integration, Braxton J. Smith

Mechanical and Aerospace Engineering Theses - Archive

The rapidly rising computational power of modern computing components combined with the advanced packaging techniques being implemented has resulted in exponentially increasing thermal design powers (TDP) from CPUs and GPUs. Traditional air-cooling methods are approaching their effective cooling limits for many of these components, requiring lower supply air temperatures, higher supply air flowrates, and much larger heatsinks to remain feasible. Transitioning from air-cooling to single-phase immersion cooling offers numerous benefits in thermal performance, data-center size reduction, and energy efficiency. To leverage the merits of immersion cooling, the performance of a given heatsink must be predicted and optimized for best performance …

High Frequency Oscillations As Biomarkers Of The Epileptogenic Zone In Children With Drug Resistant Epilepsy, Lorenzo Fabbri

Bioengineering Dissertations - Archive

Epilepsy surgery stands out as the most effective treatment for patients dealing with focal drug-resistant epilepsy (DRE). Its effectiveness depends on successfully removing or disconnecting the epileptogenic zone (EZ), which is the brain area crucial for seizure generation. The seizure onset zone (SOZ) serves as the best approximation of the EZ; this is the region where most seizures begin, identified through invasive electroencephalographic (iEEG) recordings. However, the unpredictable nature of seizures means they can take hours or even days to occur, consuming valuable human and financial resources. As a result, there is a pressing need for an interictal biomarker that …

Strategies For Enhanced Meg Data Analysis In Clinical Practice And Emerging Frontiers, Pegah Askari

Bioengineering Dissertations - Archive

Epilepsy and dementia are debilitating neurological disorders that pose substantial challenges for patients, caregivers, and healthcare systems. Advances in magnetoencephalography (MEG) and signal processing offer new opportunities to improve diagnostic accuracy, surgical planning, and treatment monitoring. This dissertation presents a unified body of work comprising artifact removal, automated event detection, and deep learning-based biomarker discovery. These approaches collectively enhance the clinical utility of MEG for diverse patient populations.

The first study addresses a significant technical obstacle in the management of drug-resistant epilepsy. Patients receiving responsive neurostimulation (RNS) have historically been excluded from MEG as the data is contaminated by device-related …

Functional Enhancement Of Pancreatic Islets Through Photobiomodulation For Potential Diabetes Therapeutics, Kelli Fowlds

Bioengineering Dissertations - Archive

Islet transplantation is a potential therapeutic route for type 1 diabetic patients facing chronic ketoacidosis and/or hypoglycemia unable to be properly regulated with standard insulin administration. However, successful engraftment is hampered by a multitude of factors. Harvested islets face rapid, substantial degradation due to hypoxia and nutrient depletion once isolated. Transplanted islets are additionally susceptible to the instant blood-mediated inflammatory reaction (IBMIR). This combination of factors leads to more than half of transplanted islets failing to engraft post-surgery. Many areas of research are dedicated to investigating alternative approaches or supplemental treatments to improve the success rate of engraftment and insulin …

Novel Electrophysiological Biomarkers In Pediatric Drug Resistant Epilepsy And Genetic Epilepsy Syndromes, Sakar Rijal

Bioengineering Dissertations - Archive

Pediatric epilepsies, particularly those that are drug-resistant or genetically driven, represent some of the most complex neurological disorders encountered in childhood. Central to their pathophysiology is a disruption in the delicate balance between cortical excitation and inhibition (E/I), often resulting from impaired GABAergic interneuron function. This imbalance manifests as aberrant network dynamics and altered neural oscillations, giving rise to seizures and long-term cognitive impairments. In this thesis, we developed a translational framework to identify electrophysiological biomarkers that (i) assess cortical E/I imbalance and (ii) map epileptogenic zones, with the aim of enhancing diagnosis, guiding surgical planning, and informing therapeutic monitoring …

Vendor-Independent B0 Shimming Framework With Application To Metabolic Mri In Human Gliomas, Mahrshi Jani

Bioengineering Dissertations - Archive

High and ultra-high field MRI and MRSI offer markedly improved signal-to-noise ratio and spectral dispersion, enabling in-vivo characterization of tumor metabolism with unprecedented detail. However, these benefits are tightly coupled to high demands on static magnetic field (B₀) homogeneity, particularly in the brain where susceptibility interfaces near the skull base and paranasal sinuses generate complex, higher-order field perturbations. In glioma patients, additional susceptibility variations arise from surgical cavities, hemorrhage, calcifications, and cystic components, further degrading B₀ homogeneity. As a result, shimming often become the main bottleneck limiting robust, whole-brain spectroscopic imaging and, consequently, our ability to map metabolic …

Engineered Biomimetic Muscle Graft For Skeletal Muscle Regeneration, Julia O. Aguirre

Bioengineering Theses - Archive

This study aims to develop a synthetic muscle graft that closely mimics the architecture, viscoelastic properties, and bio-signaling characteristics of natural skeletal muscle. By analyzing the native skeletal muscle microstructure, biochemical, and mechanical properties, we establish design parameters for a biomimetic scaffold. The engineered graft integrates tunable mechanical compliance, aligned microarchitecture, and signaling cues to promote cell recruitment, adhesion, proliferation, and maturation. To further enhance biofunctionality, an electrically conductive polymer was incorporated to improve the electrical conductivity, and the graft was loaded with the bioactive lipid signaling mediator “Prostaglandin E2” (PGE2) to support myogenesis during muscle regeneration. The grafts were …

Evaluate The Feasibility Of Recyclability Of The Plastic Modified Rap In Hot Mix Asphalt, Aashish Acharya

Civil Engineering Theses - Archive

EVALUATE THE FEASIBILITY OF THE RECYCLABILITY OF PLASTIC MODIFIED RAP IN HOT MIX ASPHALT

Aashish Acharya

The University of Texas at Arlington, 2025

Supervising Professor: Dr. MD Sahadat Hossain

Recyclability is the primary concern regarding the implementation of plastic-modified asphalt. Conventional Reclaimed Asphalt Pavement (RAP) is commonly reused; however, there are limited studies on RAP from plastic-modified mixes because field-aged material is not yet available. The use of recycled plastics in asphalt is mainly at the research stage and not widely used in practice. This creates a practical concern: if Plastic-RAP cannot be recycled, or if adding Plastic-RAP lowers the …

Integrating Off-Spec Scms Into Plc Blends, George A. Qubty

Civil Engineering Theses - Archive

Off-spec supplementary cementitious materials (SCMs) remain underutilized due to variability in reactivity and non-conformance with existing standards. This study evaluates the hydration behavior and fresh-state and mechanical performance of these materials in Portland limestone cement (PLC) systems. One Class F fly ash (FA), one bottom ash (BA), and two calcined clays (HACC and LACC) were evaluated as 20 % replacements to PLC. Hydration behavior was characterized using isothermal calorimetry and thermogravimetric analysis (TGA), while fresh-state and mechanical performance were assessed through flow table, Vicat setting time, load–crack mouth opening displacement (CMOD) flexural testing, and compressive strength testing. HACC increased compressive …

Sustainable Aviation Fuel Production From Plastic Waste On Bifunctional Catalysts, Kamryn Culp

Williams Honors College, Honors Research Projects

Catalytic hydrocracking was studied as a chemical recycling method for converting mixed plastic polypropylene (PP) and polyvinyl chloride (PVC) waste into sustainable aviation fuel-range hydrocarbons. Addressing both plastic pollution and aviation-related CO₂ emissions, bifunctional Ni- and Pt-based catalysts supported on Al₂O₃ and TiO₂ were evaluated under 30 bar hydrogen pressure at 250 °C. The inclusion of PVC, often regarded as a catalyst poison due to chlorine content, was examined at 1 wt% and 5 wt% to assess catalyst tolerance to it and resulted performance. Reaction rates and product selectivity were analyzed using gas chromatography. Results show that Ni/TiO₂ outperformed other …

Examining Correlations Between Inhibition Efficiency And Compound Properties For Corrosion Inhibitors On Carbon Steel Rebar In Concrete, Nicole Langenfeld

Williams Honors College, Honors Research Projects

This project will look to determine if correlations exist between the inhibition efficiency and particular chemical properties of different corrosion inhibitors for carbon steel rebar in concrete affected with chlorides. Examining the inhibition efficiency of different corrosion inhibitors and attempting to find correlations or a general rule of thumb for what denotes a better corrosion inhibitor would be valuable. This similar concept has been seen with Lipinski's rule of 5 where he created a general guide to predict if compounds were orally active in humans. Since little information is readily available and no known correlations exist for corrosion inhibitors on …

Automatic Hole Saw Testing For Mk Morse, Jason Watson, Justin Zapotoczny

Williams Honors College, Honors Research Projects

M.K. Morse has a hole saw tester they use for their hole saw product line. The tester currently requires constant operator attention for a process that could be automated. The goal of this project is to create a new hole saw tester that can run without the need for an operator's complete attention and give reliable data for the hole saws.

Handling Emerging Sources Of Non-Determinism: Proportional Control With Quantum Noise, And Lyapunov-Based Economic Model Predictive Control To Address Stability, Profitability, And Dynamics Of Nonlinear Systems Under Cyberattack, Keshav Kasturi Rangan

Wayne State University Dissertations

The fundamental objective of process control is to establish safe process operating conditions while ensuring minimal economic loss. One of the fundamental challenges of achieving this objective is non-determinism from various sources that can create unexpected process behavior. A traditional goal in industrial process control is the rejection of disturbances, which are time-varying process inputs that are not directly modified by the control system. Other sources of uncertainty can include measurement inaccuracies/noise and changes in process dynamics as a plant operates. Many techniques have been developed to ensure notions of stabilization and safety despite these sources of stochasticity for both …

An Intelligent Robotic System For Multi-Sensory Cognitive Fatigue Detection To Assist Persons With Paralysis In Activities Of Daily Living, Enamul Karim

Computer Science and Engineering Dissertations - Archive

Assistive robotics is a promising area for improving the quality of life of people with paralysis, specifically through assistance in Activities of Daily Living (ADLs). Current state-of-the-art assistive robotic systems do not have the capability to dynamically modulate their functionality according to the cognitive fatigue level of the user, which can negatively impact their effectiveness and usability in real-life settings.

This dissertation explores an adaptive robotic framework that adjusts its behavior depending on the cognitive fatigue level of users. The system operates in three different modes, and switches between Fully Controlled, Semi-Autonomous, and Fully Autonomous modes. The overall goal is …

Fair And Sustainable Machine Learning: A Holistic Approach To Data Quality, Efficiency, And Resource-Aware Training, Zahidur Rahim Talukder

Computer Science and Engineering Dissertations - Archive

The increasing reliance on distributed, privacy-sensitive data has driven the emergence of Federated Learning (FL) as a transformative paradigm for collaborative machine learning. By enabling multiple client devices to train a shared global model without transferring raw data, FL offers significant privacy advantages. However, real-world deployments of FL are constrained by critical challenges such as data heterogeneity, client unreliability, and hardware disparities. These factors lead to uneven model convergence, degraded global accuracy, and fairness issues that threaten FL's scalability and inclusivity in diverse environments.

This dissertation investigates these challenges and proposes three novel algorithmic frameworks to advance the state-of-the-art in …

Exploring Emerging Memory Technologies For Enhancing Data Center Applications, Zhen Lin

Computer Science and Engineering Dissertations - Archive

The rapid evolution of memory and storage technologies is fundamentally reshaping the design of operating systems and data management. Emerging devices such as persistent memory, NVMe SSDs, and Compute Express Link (CXL)--enabled hybrid memory modules introduce new opportunities for high-performance, cost-efficient data management, yet they also expose limitations in traditional software abstractions. File systems, originally designed for slow block-based devices, incur excessive overhead on ultra-low-latency media, while block-level caches suffer from metadata and eviction inefficiencies. Moreover, hardware-managed tiering provides transparency but restricts adaptability across workloads. These challenges highlight the need to rethink caching and tiered memory management across multiple system …

Scratching Behavior Of Copper-Niobium Nanolaminates, Matthew C. Peterson

Graduate Research Theses & Dissertations

Metal nanolaminates are known in material science for their unique mechanical properties and high strength due to their interface behavior. Cu/Nb nanolaminates specifically are studied for their uses in electronics due to their good conductivity and superior tensile strength compared to copper conductors, as well as their radiation resistance in radioactive environments. Currently, most molecular dynamics (MD) simulations of Cu/Nb simulations are axially loaded. This work attempts to characterize the scratching behavior of Cu/Nb nanolaminates with varying indenter size, temperature, speed, and layer thickness. The coefficient of friction (CoF), friction force, normal force, and material removal were then compared. A …

The Development Of Sustainable Synthetic Fiber Reinforced Concrete Mix Design With Low Maintenance Cost, Kentesha L. High

Civil Engineering Dissertations - Archive

This dissertation presents the mix design of polypropylene SYN-FRC as an alternative to traditional concrete mixtures in construction roadways. The primary scope of work was to develop and implement a pavement system at The University of Texas at Arlington, specifically designed for the Dallas area, to minimize long-term maintenance. Additionally, the project aimed for expeditious construction to avoid traffic closure during new construction and maintenance. It also involved evaluating the pavement performance, providing the City of Dallas with specifications for the mix design of SYN-FRC. The sustainability of the synthetic fiber mix design for concrete roadways, compared to traditional concrete, …

Photonic Crystal Devices For Chip Scale Sensing Systems, Yudong Chen

Electrical Engineering Dissertations - Archive

This thesis investigates the design and integration of photonic crystal (PC) structures for compact, high-performance optical platforms, with a focus on applications in gas sensing, on-chip lasers, and flat optics. Chapter 1 introduces the fundamental principles of PC design and simulation, highlighting their potential to replace bulky components in micro-gas chromatography (µGC) systems through miniaturization and integration. Chapter 2 explores PC-based nanobeam lasers, including the Lambda-Scale Embedded Active-Region Photonic Crystal (LEAP) laser, which demonstrates strong optical confinement and energy-efficient operation, with energy consumption as low as 8 fJ/bit. These laser designs are evaluated for their suitability in low-power, high-speed on-chip …

Advancing Machine Learning Approaches Through Robust Methodologies In Llm Code Generation, Adversarial Text Classification, And Unsupervised Learning, Anahita Samadi

Computer Science and Engineering Dissertations - Archive

This dissertation combines insights across text, code, and image modalities to advance the robustness, efficiency, and adaptability of machine learning models. Specifically, we address challenges like adversarial vulnerability in text, the impact of test strategies on code generation, and dimensionality in image representation in unsupervised learning domain. These efforts highlight pipelines for designing machine learning systems that are not only efficient, but also adaptable to complex environments. In addition, these efforts together help form the basis for a multimodal AI capable of thriving in medical applications that this dissertation prototypes for future efforts.

Optimizing Architecture And Software For Next-Generation Memory Systems, Lingfeng Xiang

Computer Science and Engineering Dissertations - Archive

The rapid advancement of memory technologies presents new challenges and opportunities for system software and architectural design. This dissertation investigates how to optimize modern computing systems for next-generation memory, mostly focusing on persistent memory and Compute Express Link (CXL)-based memory. First, we conduct a detailed characterization of Intel Optane DC Persistent Memory, identifying the distinct behaviors of its on-DIMM read and write buffers and analyzing their impact on application performance. These insights motivate optimizations that decouple read and write paths, revealing that random read latency—especially in pointer-chasing workloads—is a dominant performance bottleneck. Second, we present NOMAD, a page management framework …

Medical Ai: Solving Healthcare Challenges And Inspiring Ai Innovation, Xiaowei Yu

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence (AI) is transforming healthcare by enabling large-scale analysis of medical data and integrating multimodal information for more comprehensive diagnostics. I present my work addressing fundamental and challenging problems in developing state-of-the-art AI models for medical data analysis, including multimodal brain data and other medical datasets. Additionally, I design brain-inspired AI models by integrating insights from organizational principles of brain networks. Specifically, my research tackles three critical aspects: (1) AI in Computational Neuroscience, where I design deep learning models for brain network analysis to uncover the organizational principles of brain networks; (2) Brain-Inspired AI, where I integrate superior brain …