Digital Commons Network^™

Liquid Fuel Cloud Detonation Propagation And Dynamics, Taylor R. Brown

Graduate Thesis and Dissertation post-2024

Detonations are a form of combustion that produce significantly higher work per specific volume than traditional deflagration combustion, thus are an active area of interest for increased combustion efficiency and power generation. However, the highly unsteady nature of detonations complicates the ability to harness their energy in a fieldable technology, prompting the need for a more fundamental understanding of the detonation phenomena. Fundamental detonation research has overwhelmingly focused on gaseous reactants due to ease of experimentation, though liquid fuels are increasingly relevant as their high energy density makes them more suitable for real-world engine applications, and much of the research …

Laminar Burning Speed Of Dme-Hydrocarbon Mixtures, Ahmed F. Safdari

Graduate Thesis and Dissertation post-2024

Fossil fuels, when burned in the air, release large amounts of carbon dioxide (CO₂) and greenhouse gases (GHG) into the air, causing significant pollution. To reduce greenhouse gas emissions (CO₂, CO, and NOₓ), alternative fuels such as Dimethyl Ether (DME) are being explored for their potential to offer cleaner combustion solutions. This study investigates the laminar burning speed (LBS) DME blends with several alkanes and alkenes in air at atmospheric pressure (1 atm) across a wide range of equivalence ratios (Φ). LBS is essential for understanding combustion characteristics, including combustion efficiency, heat release rates, and chemical kinetics. Accurate …

Novel Symmetrical Components-Based Methods For Protection Of Systems With High-Level Penetration Of Ibrs, Kwasi Opoku

Graduate Thesis and Dissertation post-2024

The global electric power system is undergoing a transition which has resulted in the proliferation of inverter-based resources (IBRs), mainly renewable energy resources and battery energy storage. These offer the advantage of decarbonization targets in electricity generation while potentially improving resilience and overall efficiency of the electric grid. Furthermore, this has led to the development of inverter-interfaced microgrids at the sub-transmission, and especially, distribution levels.

An effective protection system is a prerequisite for any power system operation. However, the departure of modern power systems, in this regard, from conventional sources and topologies presents new challenges to effective protection from power …

A Deep Learning Framework For Last-Mile Delivery Enhancement Using Social Media, Valeria Laynes Fiascunari

Graduate Thesis and Dissertation post-2024

Over the past decade, people have been spending more time online. Almost anything can be done from a laptop or cellphone. This is one of the reasons why e-commerce has been in a constant boom, as it is easier to buy something online and have it delivered to the front door than to go to the store. As more people engage in this activity, e-commerce platforms' challenges are more complicated and need to be addressed faster. However, these challenges escape the company's scope when external factors influence the objective of optimized deliveries, for example, traffic issues or bad weather during …

Examining The Feasibility And Influencing Factors Of Large Language Models In Source Code Security Analysis, Jie Lin

Graduate Thesis and Dissertation post-2024

Large Language Models (LLMs) have recently emerged as promising tools for analyzing source code; however, their capabilities for automated vulnerability detection and localization remain underexplored, particularly given the widespread reliance on closed-source models which introduce significant privacy, security, and transparency risks. This dissertation rigorously examines the feasibility and key influencing factors determining the efficacy of open-source LLMs in source code security analysis. Initially, we investigate the impact of tokenized input length on vulnerability detection accuracy and explicitness across Java vulnerability datasets using ten distinct LLM architectures, identifying robust performance in specific models while others demonstrate considerable accuracy deterioration. Subsequently, we …

Multiscale Modeling And Experimental Evaluation Of Droplet Evaporation In High-Pressure Pulsed Spray Cooling For Thermal Management Applications, Fernando Soria Proano

Graduate Thesis and Dissertation post-2024

Droplet evaporation plays a vital role in spray cooling applications, helping to maintain safe operating temperatures for high-powered devices such as lasers and supercomputers. There exists an ongoing debate regarding the appropriateness of diffusion-limited versus kinetically limited models for describing this complex process. This work seeks to bridge the macro-scale evaporation, governed primarily by capillary forces, with the nano-scale interactions among the vapor, liquid, and solid phases through a comprehensive description of disjoining pressure.

By integrating principles from lubrication theory, heat conduction, diffusion, and statistical methods, a detailed model for evaporative mass flux is developed. This model incorporates various interactions, …

Algorithms And Benchmarking For Parallel Identity-By-Descent Segment Detection, Kecong Tang

Graduate Thesis and Dissertation post-2024

As genomic biobank initiatives continue to grow, the availability of large-scale genotype datasets, encompassing hundreds of thousands to millions of individuals, has transformed genetic research and biomedical discovery. However, the sheer volume of this data presents major computational barriers. Efficient and scalable methods are urgently needed to process and extract meaningful signals from biobank-scale data using modern multi-core architectures. One central task in this domain is the detection of identity-by-descent (IBD) segments, which underpins a range of applications including genealogical inference, disease mapping, phasing, and population structure analysis.

This dissertation addresses these challenges by presenting a sequence of contributions that …

Beam-Clip: Multimodal Alignment For Mmwave Beam Pattern Learning, Andrew P. El Kommos

Graduate Thesis and Dissertation post-2024

This thesis presents a novel approach to beam prediction in wireless communication systems using a multimodal masked CLIP (Contrastive Language-Image Pre-training). We introduce a two-phase training methodology that first aligns representations across multiple sensor modalities—GPS, Radar, LiDAR, and RGB images—through masked contrastive learning, followed by task-specific fine-tuning for channel power reconstruction. Our approach adapts CLIP’s pre-training strategy to the domain of wireless signal modeling, enabling the model to learn rich, transferable features that capture the spatial and contextual dependencies of the beam distribution. Notably, the pre-training stage provides a substantial boost to overall performance, significantly improving the model's ability to …

A Numerical Assessment Of Shock-Raindrop Interaction: An Investigation Of Cavitation Dynamics Driven By Internal Pressure Wave Focusing, Reed Forehand

Graduate Thesis and Dissertation post-2024

This dissertation investigates the formation and evolution of cavitation within liquid droplets subjected to shock wave interactions, with emphasis on internal pressure wave focusing as a fragmentation mechanism. A numerical framework is developed in which high-resolution Volume-of-Fluid (VoF) simulations resolve shock transmission into spherical, cylindrical, and cubic water droplets across a range of flow conditions. The resulting pressure histories at droplet centers are extracted and post-processed using the Rayleigh–Plesset equation to model the dynamics of spherical vapor bubbles.

The modeling framework is validated against established benchmarks, including canonical shock tube behavior, shock-droplet interaction studies, and experimental cavitation observations. A series …

The Elliptical Instability In Turbulent Flows: A Mechanistic Framework, David M. Smerina

Graduate Thesis and Dissertation post-2024

The conveyance of energy through the formation, interaction, and destruction of eddies over a wide range of spatial scales, from the largest scale where energy is injected to the smallest scales where the energy is dissipated through viscosity remains one of the most fundamental unsolved problems in fluid mechanics. This dissertation provides a comprehensive investigation spanning multiple flow regimes consisting of high-speed reacting flows and transitional boundary layer turbulence to establish the necessary conditions for turbulent flows to sustain cascades of energy to arbitrarily small scales while demonstrating how the nonlinear development of the elliptical instability leads to the emergence …

Detonation Morphology For Hypersonic Propulsion, Adam R. Kotler

Graduate Thesis and Dissertation post-2024

Standing detonations are uniquely stabilized in hypersonic flows. The portion of the detonation containing the stabilized shock front is remotely similar to non-reacting normal and oblique shocks and is uniquely differentiable from the latter by its coupling to a reaction front characterized by intense heat-release rates and fast chemical kinetics. The formation of an inert induction region characterized by slow-evolving chemical kinetics, subsequent development of a transition region within which these rates escalate, and terminal coalescence between shock wave and reaction front define the formation sequence of the standing detonation. Observations of this process are reported across multiple research groups. …

When Sediment Meets Strategy: Linking Shoaling Dynamics, Vessel Behavior, And Dredging Optimization, Matthew P. Davies

Graduate Thesis and Dissertation post-2024

Navigation channels are the economic arteries of U.S. maritime trade, yet their efficiency and safety are continually threatened by sedimentation and the escalating costs of dredging. Traditional tools used by the U.S. Army Corps of Engineers (USACE) to forecast shoaling and estimate dredging needs often fall short of capturing the complex interplay between channel condition, vessel behavior, and maintenance decisions. This dissertation develops and integrates three complementary innovations to address this challenge. First, a Parametric Linear Regression Methodology (PLRM) is introduced to improve shoaling predictions by blending long-term and short-term sedimentation rates with tunable weighting factors. This method achieves forecasting …

Examining Information Flows Within Transformer Models Using Transfer Entropy, Clayton Barham

Graduate Thesis and Dissertation post-2024

The rapid growth of artificial intelligence, in terms of both power and prevalence, motivates the need for new ways to study explainability in deep neural networks. This is especially true for transformer architectures and large language models, which are the driving force behind the current surge in artificial intelligence. The complexity of transformer architecture foils many pre-existing methods for studying explainability in deep neural networks. To address this gap in knowledge, this dissertation proposes a new method of studying explainability in transformer architectures that leverages insights from neuroscience, employing transfer entropy to map information flows between components of the transformer …

On Transient Dynamics And Persistent Organization Of Landscape Evolution, Aysan Hassanzadeh Bavojdan

Graduate Thesis and Dissertation post-2024

The evolution and organization of landscapes result from the competition between tectonic uplift, fluvial incision, and hillslope diffusion. Understanding how these processes interact under varying external conditions (i.e., extreme climatic events) is critical for predicting landscape dynamics and long-term geomorphic adjustment. Using a physically based landscape evolution model, we first investigate how variations in the fluvial incision coefficient (K) and soil diffusion coefficient (D) mimic different climatic conditions and control the transient and steady-state organization of landscapes. Results indicate that landscapes with the same non-dimensional index (defined as the ratio of the timescales of advective (fluvial) to diffusive (hillslope) processes) …

A Predictive Model Of Adsorption Of Cryogenic Liquids In Aerogel-Based Materials, Julie E. Foroosh

Graduate Thesis and Dissertation post-2024

Commodities that are gaseous at standard temperature and pressure are generally stored in thick-walled pressure vessels, or as cryogenic liquids in vacuum-jacketed tanks. An advantage of cryogenic storage is that more mass can be stored per unit volume. However, cryogenic tanks are often bulky and cannot be made into conformal geometries. Adsorption storage in porous and flexible materials, such as aerogel blankets, is a promising method for storing these commodities. Previous studies have shown that when the commodity is adsorbed from the liquid cryogen, instead of from a gaseous state, the mass adsorbed per unit volume of aerogel blanket is …

Modeling And Predicting The Thermal Behavior Of Si/Cnt Nano Composites Using Pinns, Olamide Osigbemeh

Graduate Thesis and Dissertation post-2024

Silicon/Carbon Nanotube (Si/CNT) composites have shown great performance as Thermal Interface Materials (TIMs) for cooling in advanced electronics. The heat transfer in these materials is highly influenced by the interfacial thermal resistance (ITR) at the interfaces between the CNT filler and the silicon matrix. The ITR poses a critical challenge that hinders the design and performance of the TIM. This research designs a Physics Informed Neural Network (PINN) that predicts the thermal behavior of Si/CNT through a forward and inverse solver since they are known networks in solving complex partial and ordinary differential equations. However, despite the advantages of PINNs, …

Towards Robust Deep Learning: Label Noise Modeling, Algorithms, And Applications, Diego Fernando Linares Gonzalez

Graduate Thesis and Dissertation post-2024

This thesis investigates robust and hybrid deep learning with emphasis on improving accuracy and generalization in challenging real-world settings, including learning under noisy supervision and applications to complex scientific data.

In the first part, we address instance-dependent label noise by using a tensor factorization–based methodology that models the noise transition process as an instance-dependent decomposition. We also utilize low-rank block-term structures to capture class-specific and instance-specific correlations. Moreover, we include a robust loss function and rank regularization to achieve stable optimization during model training. We validated this with experiments on benchmark datasets, like Fashion-MNIST, CIFAR-10, and CIFAR-10N, demonstrating higher accuracy …

A Framework For Machine Learning Surrogate Modeling In Physics-Based Reacting Flow Simulations, Jose O. Bobren-Diaz

Graduate Thesis and Dissertation post-2024

The design of next-generation combustion systems relies on computer simulations that capture the multidimensional and multiscale physics of turbulent reacting flows. These simulations are computationally intensive, requiring trade-offs to produce results within allowable time and cost constraints. These trade-offs often consist of general assumptions and modeling simplifications that approximate complex physical phenomena. Consequently, these models can limit both the accuracy and the regime of applicability to real systems since these approximations are often heuristic and inconsistent with the governing physics. Moreover, in today’s rapid design-to-prototype development phase, making timely and informed design decisions is important. This requires fast and reliable …

Advancing Heterogeneous Collaborative Perception For Autonomous Vehicles, Babak Ebrahimi Soorchaei

Graduate Thesis and Dissertation post-2024

Recent advances in computational and communication technologies have enabled deep neural networks (DNNs) and high‐speed vehicular wireless links, paving the way for cooperative perception and cognition in autonomous systems. By exchanging complementary observations, these methods overcome inherent sensor limitations, such as occlusions and restricted range, and thereby improve the detection of partially hidden targets. However, real‐world deployments face challenges from noisy measurements (e.g., GPS inaccuracies) and heterogeneous perception stacks: vehicles often rely heavily on visual detectors, yet there is no standardization ensuring uniform object‐detector architectures across a fleet.

In this dissertation, we first explore the design of a lightweight detector …

Communication-Aware Trajectory Prediction And Tracking For Connected And Autonomous Vehicles, Arash Raftari

Graduate Thesis and Dissertation post-2024

This dissertation presents a unified framework for communication-aware trajectory prediction and vehicle state tracking in connected and autonomous vehicle (CAV) systems. Unlike most deep learning–based forecasting models that assume full observability of surrounding agents, the proposed approach explicitly accounts for limited communication range, packet loss, and bandwidth constraints that characterize real vehicular networks.

A model-based, error-driven information-passing policy is introduced to extend situational awareness beyond one hop. Rather than relaying messages based on distance or freshness, each vehicle evaluates the utility of received content in reducing system-wide position tracking error (PTE). Using compact Gaussian Mixture Model (GMM) representations of motion …

A Multivariable Feedback Approach To Individual Pitch Control For Fatigue Load Reduction In Wind Turbines, Kazi Ishtiak Mohsin Mr.

Graduate Thesis and Dissertation post-2024

This dissertation investigates advanced control strategies to improve performance and reduce fatigue loads in both land-based and floating offshore wind turbines. The proposed approaches target fatigue load mitigation at critical component frequencies while ensuring rotor speed regulation and efficient power generation. The control strategies are implemented and validated using both our in-house modeling and simulation platform, CRAFTS (Control Oriented Reconfigurable Acausal Floating Turbine Simulator), and the industry-standard OpenFAST model.

For land-based turbines, torque actuation and collective blade pitch control are incorporated into a nonlinear controller for rotor speed regulation and power generation. To address fatigue load reduction, individual pitch control …

Attention-Based 3d-Convolutional Neural Network Model For Mechanical Property Predictions Using, Daniel E. Traczyk

Graduate Thesis and Dissertation post-2024

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process …

Load Modulated Balanced Amplifier (Lmba) With Antenna-Vswr Resilience For Energy- And Spectrum-Efficient Communications, Jiachen Guo

Graduate Thesis and Dissertation post-2024

The rapid evolution of 5G and forthcoming 6G networks demands higher data rates and lower latency within increasingly congested spectrum resources. Advanced modulation schemes such as high-order quadrature amplitude modulation (QAM) and orthogonal frequency-division multiplexing (OFDM) have been widely adopted to improve spectral efficiency; however, these techniques substantially increase the peak-to-average-power ratio (PAPR) of transmitted signals. To enable power amplifiers (PAs) to operate efficiently under this conditions, load-modulated balanced amplifiers (LMBAs) have been developed as an effective solution while inherently supporting broadband operation. On the other hand, the deployment of massive multiple-input multiple-output (MIMO) technology and highly integrated active-antenna systems …

Better Models For Mosquito Control: Impacts On Nontarget Insects And Joint Species Distributions Of Mosquitoes, Jacob D. Hart

Graduate Thesis and Dissertation post-2024

Since its inception, mosquito control has been vital for limiting the spread of vector-borne pathogens. Modern control efforts balance the protection of public health and the prevention of environmental harm. We addressed this from two directions: the study of mosquito adulticide effects on nontarget insects, and fine scale distribution modeling of mosquitoes to better target control efforts. We first investigated the effects of ultra-low volume (ULV) pyrethroid applications on nontarget insects in Central Florida and found no statistically clear evidence of reduction in abundance or diversity. We reported a slight and statistically unclear reduction of night-flying Lepidoptera, and effective reduction …

Bridging Physiology, Data Science, And Genomics To Illuminate Photosynthetic Variation In Sunflower, Rebekah E. Davis

Graduate Thesis and Dissertation post-2024

Photosynthesis is a critical metabolic process in plants involving ‘light reactions’ that capture solar energy to produce ATP and NADPH, and ‘dark reactions’ where these molecules power the enzyme Rubisco to synthesize sugars in the Calvin-Benson cycle. These processes serve as the primary energy source for plant growth and reproduction, displaying significant variation across species and environments. In Helianthus, distinct differences in photosynthetic traits are observed between annual and perennial species. Notably, carboxylation rates, photon capture, and maximum photosynthetic rates show strong phylogenetic signal (Pagel’s λ = 0.99) and are significantly higher in annuals. Photosynthesis is modeled here as a …

Change Point Monitoring In High-Dimensional Factor Models, Mahdi Mirhosseini

Graduate Thesis and Dissertation post-2024

This dissertation presents the Factor-Augmented Detection (FAD) algorithm, a robust methodological framework for online change point detection in high-dimensional data streams, grounded in classical factor model analysis. To tackle the challenges of monitoring large-scale datasets, the FAD algorithm decomposes data into a parsimonious set of common factors and sparsely changing idiosyncratic residuals, facilitating independent, targeted analysis of low-dimensional factor dynamics and sparse high- dimensional residual evolution.

Following decomposition, structural changes in the factor components are identified using established multivariate sequential monitoring techniques, specifically the Multivariate Cumulative Sum (mCUSUM) and Multivariate Exponentially Weighted Moving Average (mEWMA) procedures. Simultaneously, sparse alterations in …

Characterization, Imaging, And Modeling Of Thermal Dynamics And Failure Mechanisms Of Quantum Cascade Lasers, Alejandro M. Villalobos Meza

Graduate Thesis and Dissertation post-2024

As advancements in Quantum Cascade Laser (QCL) technology continue to push the upper limits of continuous-wave performance, effective thermal management remains at the forefront of design considerations. The compact and highly tunable active region that has made QCL technology so attractive also imposes challenges to heat dissipation during continuous wave operation. To address this ongoing challenge, this work demonstrates a set of characterization and modeling techniques which can be used to interrogate different aspects of the thermal and structural properties of QCLs. Emphasis is placed on a suite of electron microscopy techniques and their role in providing a window into …

Distribution And Morphology Of Spinal Afferent Innervation Of The Gastric Muscular And Submucosal Layers Of Rats: Anterograde Tracing, Andrew M. Kwiat

Graduate Thesis and Dissertation post-2024

Chronic pain afflicts over 25 million Americans, with approximately 2 million developing opioid addiction due to insufficient treatment options. Visceral chronic pain, particularly abdominal pain, poses a significant clinical challenge, yet its underlying neural mechanisms remain poorly defined. This thesis presents a comprehensive anatomical analysis of spinal afferent innervation in the muscularis externa and submucosal layers of the rat stomach—two tissue compartments implicated in visceral sensation and pain. Using anterograde tracing, dextran-biotin was injected into thoracic dorsal root ganglia (T7–T11) of male Sprague Dawley rats (3–5 months) to label spinal afferent axons in the stomach. Flat-mount preparations of muscular and …

Wavefront Sensing Using Nonlocal Metasurfaces, Arturo Martin Jimenez

Graduate Thesis and Dissertation post-2024

Many laser applications require wavefront control to compensate for aberrations caused by system misalignment, component fabrication errors, and propagation through inhomogeneous atmospheres. Aberrations caused by atmospheric turbulence remain particularly challenging to measure due to the temporally fluctuating wavefront distortions and large transverse phase variations in the beam. Conventional wavefront sensors, like the Schack-Hartmann (SHWS) are effective at low turbulence levels, but require relatively bulky optical elements. This thesis investigates the use of metasurface optical elements for use in wavefront sensors both in the low-turbulence limit and in deep turbulence conditions. Such metasurface-based wavefront sensors can achieve the functionality of complex …

Metasurface-Refractive Hybrid Lens Design, Ko-Han Shih

Graduate Thesis and Dissertation post-2024

Refractive optics are widely used in imaging systems while optical aberrations can limit their imaging performance and the typical solution to correct is cascading additional refractive optics with varying materials and shapes. Still, this scheme can result in bulky and costly lenses. Metasurfaces (MSs), with their compactness and ability to locally manipulate wavefronts, offer additional degrees of freedom in aberration correction. Integrating MSs with refractive optics creates MS-refractive hybrid lenses, enabling advanced optical performance while maintaining a compact design. Various methods have been proposed for designing aberration-correcting MSs in hybrid lenses, which often rely on predefined target phase profiles or …