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A Baseline Study Of Artificial Intelligence Use Among Chemical Engineering Undergraduates At The University Of Arkansas, Natalie J. Erickson

Chemical Engineering Undergraduate Honors Theses

In recent years, artificial intelligence (AI) has shifted from a novel innovation to a tool used in daily life, particularly within higher education. While anecdotal evidence suggests high levels of student engagement with AI, empirical data regarding usage patterns remains limited. This baseline study investigates AI integration among undergraduate chemical engineering students at the University of Arkansas. Through an anonymous survey (N=68; comprising 20 freshmen, 14 sophomores, 16 juniors, and 18 seniors), researchers assessed usage frequency, student comfort levels, and their evolving relationship with AI tools. The findings reveal that 75% of respondents utilize AI for coursework at least once …

From Kernel To Carbon Footprint: Using Life Cycle Analysis To Teach Sustainability In Chemical Engineering, Neal Z. Kinney

Chemical Engineering Undergraduate Honors Theses

Many chemical engineering departments in the United States are currently lacking developed sustainability coursework, leaving many students without a developed understanding of topics such as life cycle analysis (LCA). [1] Therefore, there is a need for engaging and flexible educational tools that can be easily incorporated into course frameworks. This study develops an interactive learning module, expanding upon the work from Pollution Prevention Through Popcorn, a study developed by Rowan University. [2] The module combines experimental analysis of popcorn popping methods with life cycle assessment using OpenLCA to evaluate the environmental impacts of common types of popcorn packaging. Results of …

Demonstration Of The Relationship Between Irwin Spire Quantity And Boundary Layer Depth In The Chemical Hazards Research Center Wind Tunnel, Jace D. Beckwith

Chemical Engineering Undergraduate Honors Theses

Atmospheric Dispersion Models (ADMs) are computer-based simulations used to model gas releases for enforcing regulatory compliance, designing facilities, and responding to emergencies. Because ADMs rely on complex mathematical models, they require experimental validation to ensure accuracy. Wind tunnels offer a controlled, repeatable method for gathering validation data. The Chemical Hazards Research Center (CHRC) at the University of Arkansas operates an ultra-low speed wind tunnel for this purpose. A key feature of this tunnel is its simulated Atmospheric Boundary Layer (ABL). The simulated ABL is a turbulent layer in which wind speed increases logarithmically with height from near zero at the …

Design And Reliability Analysis Of A Radiation-Tolerant On-Board Computer System For Martian Surface Missions, Jack Ryan

Master's Theses

Space environments present complex challenges for electronic devices, perhaps most notably in the form of radiation effects; the natural protections provided by Earth’s atmosphere and magnetosphere are largely absent in deep space and extraterrestrial environments, making single-event effects (SEE) a critical concern. Although radiation-hardened components offer near-immunity to SEE, they possess tremendous drawbacks in both cost and performance. To circumvent such issues, this thesis investigates the feasibility of leveraging a commercial-off-the-shelf (COTS) device, the AMD KRIA K24 system-on-module (SOM), for use in Martian surface missions.

Detailed models were used to predict SEE rates in the system, and system-level fault tree …

Automating Building Feature Recognition In Digital Twin Models Using Computer Vision, Jaron Harbison

Undergraduate Honors Capstone Projects

Standard 3D scans treat all surfaces as generic geometry, but Wi-Fi signals don't. A signal drops by a different magnitude through a wooden door than a concrete wall. Without labeling these materials, Internet Service Providers (ISPs) cannot accurately predict "dead zones". This project addresses that gap by creating a machine learning model that identifies structural and architectural features. This includes features such as walls, doors, windows, and columns directly from RGB-D (Red, Green, Blue, Depth) point cloud data. 

I constructed the model on a custom U-Net architecture featuring a symmetric encoder-decoder structure with skip connections, trained on the StructScan3D dataset …

Ai-Based Framework For Elucidating The Ring Opening Energy Of Cycloalkanes, Evan Griffin

Open Access Theses & Dissertations

Cycloalkanes are a prominent component of jet fuels due to their high volumetric energy density and lower soot yield compared to aromatics. However, the structural nuance of cycloalkanes can make predicting their combustion kinetics challenging. The present study aims to develop a quantitative structure-property relationship of the ring opening energy (ROE) of cycloalkanes. A total of 253 cycloalkanes were curated from the ZINC15 database, ranging from monocycloalkanes to tetra-cycloalkanes. The 3-D cartesian coordinates of the closed shell and open shell diradical states for all curated cycloalkanes was generated with RDKit in Python and used MMFF94 to optimize the molecular geometries. …

Process Optimization Of Directed Energy Deposition Via Dmaic Methodology And Lean Manufacturing Principles, Erick Gerardo Molina Castaneda

Open Access Theses & Dissertations

Additive Manufacturing (AM), particularly Direct Energy Deposition (DED), has gained increasing relevance in industrial and research environments due to its capability to manufacture complex geometries and repair high-value components. However, despite its technological advantages, DED processes often lack standardization, clear documentation, and structured workflows, especially in academic laboratories. This research aims to optimize and standardize DED processes through the application of Lean Manufacturing principles using the DMAIC (Define, Measure, Analyze, Improve, Control) methodology. The study was conducted at the Keck Center for 3D Innovation, focusing on pre-printing and printing activities. Key issues were identified. Improvement actions included the implementation of …

Effective Prediction Of Martensite Start Temperature By Hybrid Machine Learning And Ethical A.I. In Materials Science, Agniprava Banerjee

Open Access Theses & Dissertations

The martensite start temperature Ms governs heat treatment design and mechanical property development in martensitic steel alloys. Accurate prediction of Ms from chemical composition alone would accelerate alloy development, yet existing empirical equations lack accuracy for multi-component steels and purely data-driven models provide no physically principled behavior outside their training domain. This dissertation develops a hybrid machine learning-physics framework for predicting Ms in steel alloys from composition. The MAP Cambridge dataset (1,020 cleaned records, 15 alloying elements) is processed through a five-stage cleaning pipeline and a 21-feature physics augmented representation, including the Ghosh-Olson Ms thermodynamic prediction as an explicit feature. …

Aerodynamic Parameter Estimation For A Scaled F-16: A Simulation-Based Sensitivity Analysis, Kathle C. Tischner

All Graduate Theses and Dissertations, Fall 2023 to Present

The physical motion of an aircraft is determined by the resulting forces and moments acting on the aircraft. These forces and moments can be defined as a mathematical equation where the control deflections, rotation rates, and orientation of the aircraft are multiplied by individual constants and summed together to define the entire force or moment. This method results in a highly accurate model that defines the motion of the aircraft at low angles of attack and sideslip. Initial calculations of the aerodynamic model are created based on analytical estimations based on the physical properties of the aircraft. Additional refinement of …

First-Principles Study Of Uranium Mononitride, Roy Noe Herrera Navarro

Open Access Theses & Dissertations

This thesis presents a first-principles investigation of uranium mononitride (UN), withemphasis on the connection between uranium 5f electron correlation and defect stability. Electronic-structure calculations were performed within density functional theory using the Vienna Ab initio Simulation Package. Exchange-correlation effects were treated within the generalized gradient approximation, and a Hubbard correction was applied to the uranium 5f manifold. The adopted Hubbard parameter was motivated by first-principles linear-response analysis and considered in the broader context of DFT+DMFT descriptions of correlated uranium systems, including recent interpretations of UN as a Hund-metal system. Bulk UN was studied in unit-cell and 2 x 2 x …

Additive Manufacturing Of Roman Concrete-Inspired Self-Healing Polyvinyl Alcohol-Polylactic Acid Blends: Influence Of Composition, Processing Temperature, And Raster Pattern On Print Quality And Mechanical Performance, Stephanie Moreno

Open Access Theses & Dissertations

This study investigates development of Roman concrete-inspired self-healing polymer blends based on polyvinyl alcohol (PVA) and polylactic acid (PLA) for the additive manufacturing (AM) process of fused filament fabrication (FFF). The motivation for this work stems from the need to enhance durability and functionality of biodegradable polymers while addressing environmental concerns associated with plastic waste. Inspired by the multifunctional and hierarchical architecture of Roman concrete. PVA is integrated as a hydrophilic, sacrificial phase similar to lime clasts, embedded inside a PLA-rich structural matrix to promote physical interaction and water-activated self-healing behavior. Binary PVA:PLA blends with ratios of 5:95, 10:90, 20:80 …

Faster Than The Speed Of Bram: In-Memory Computing For Next Generation Fpgas On The Edge, Nathaniel Joseph Fredricks

Graduate Theses and Dissertations

Traditional computer systems are hitting the Memory Wall as machine learning applications are bottlenecked by the bandwidth between separate memory and compute units. Current FPGA architectures are able to bypass this bottleneck with block RAMs (BRAMs) that provide on-chip, in-fabric storage. However, their potential as the foundation of computing components is often overlooked; machine learning accelerators implemented on FPGAs face additional delays when transferring data between memory and compute units. These penalties arise from BRAM bandwidth limitations and movement of data through the reconfigurable fabric. To support FPGA-based accelerators on the edge and break the Memory Wall, reconfigurable architectures must …

Design And Optimization To Advance Silicon Carbide Modules For Medium Voltage High Power Applications, Ahmed Ismail

Graduate Theses and Dissertations

The commercial availability of the first 10 kV silicon carbide (SiC) MOSFET removes the principal semiconductor barrier to medium-voltage power conversion, enabling converter topologies that are structurally inaccessible to silicon IGBT solutions at this voltage class. This dissertation presents the characterization infrastructure, device-level data, and preliminary system-level validation needed to advance the deployment of the third-generation 10 kV SiC MOSFET in converter applications. A custom-built 10 kV double-pulse test platform is developed with a fully documented component-sizing methodology, custom medium-voltage magnetics, and a measurement infrastructure capable of high bandwidth waveform capture at kilovolt common-mode potentials. On this platform, the third-generation …

Mitigating Inertial Measurement Unit Drift In Quadcopter Trajectory Tracking Using Kalman Filter Sensor Fusion With Global Positioning Measurements, Juan Sandro Caballero Aguilar

Graduate Theses and Dissertations

Unmanned Aerial Vehicles (UAVs) like quadcopters are widely used in civilian and military applications such as farming, photography, search and rescue missions, and autonomous navigation and motion. The performance and stability of these systems depend on accurate state estimation, which provides real-time information about the vehicle’s position, velocity, and orientation. However, achieving reliable state estimation is challenging due to sensor noise, measurement uncertainty, and inaccurate modeling. Common navigation sensors such as the Global Positioning System (GPS) and Inertial Measurement Units (IMUs) exhibit complementary characteristics. GPS provides absolute position measurements but suffers from noise, multipath effects, and low update rates, while …

Asynchronous Polymorphic Logic Locking, Kelby Haulmark

Graduate Theses and Dissertations

This work presents Asynchronous Polymorphic Logic Locking (APLL), a logic locking methodology that integrates polymorphic logic within the Multi-Threshold NULL Convention Logic (MTNCL) paradigm. APLL achieves Boolean satisfiability-attack resilience through a fault-based logic stripping approach followed by logic restoration, while leveraging the analog, dual-functionality of polymorphic gates to impede reverse engineering and removal attacks. In contrast to comparable SAT-resistant logic locking techniques, APLL provides inherent resistance to reverse engineering, reducing the viability of a broad class of attacks that rely on access to the locked netlist. A complete automated design flow is developed, enabling the transformation of combinational circuits into …

Recovery And Evaluation Of A Germanium Infrared Camera, Michael Paszek

Graduate Theses and Dissertations

Short-wave infrared imagers made of germanium are a promising way to extend silicon-compatible imaging beyond the visible spectrum. They also use mature CMOS fabrication and readout electronics. This thesis details the recovery, initialization, and evaluation of a germanium focal plane array (FPA) infrared camera system using both a sealed TriWave camera and a modular prototype platform. Functionality was restored to this legacy hardware, and repeatable testing workflows were established for evaluating packaged infrared imagers under controlled indoor and outdoor conditions. The prototype used a custom software interface named Merlott for evaluation, and it followed a standard sequence that included checking …

An Investigation Of Data Granularity In Rag Pipelines For Personalized Medicine, Paritosh Pandey

Master's Theses

Generative AI, exemplified by large language models like the OpenAI GPT and Meta LLaMA families, can produce diverse content in response to prompts. This capability offers a promising solution to challenges in precision medicine, which seeks to tailor treatments to individual clinical profiles but often struggles with data collection, cost, and privacy concerns. By generating realistic, privacy-preserving patient data, generative AI has the potential to transform patient-centric healthcare. With such motivation, this research develops a comprehensive Generative AI pipeline emphasizing data granularity for accurate prediction of personalized treatments. The pipeline features a central Large Language Model interacting with a Machine …

Design And Development Of A Passive Dust Protection System For Lunar Docking Operations, Simon Thengvall

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

Lunar docking systems face a unique set of environmental conditions that can create significant challenges for mechanical design. Among these, the abrasive nature of lunar regolith can impair the kinematic function of traditional terrestrial mechanisms as well as contaminate critical spacecraft components such as environmental seals, causing them to fail. For crew-rated docking systems, failure of these systems not only threatens mission success, but also crew safety, so dust protection is crucial for docking components. To address the concerns of regolith intrusion, the Simple External Attachment for Lunar Openings (SEALO) is developed as a key architecture for mechanism evaluation when …

Investigation Of Low-Pressure Mercury Lamp System Configurations And Xenon Excimer Lamp System Development As Vacuum Ultraviolet Sources For Optimal Photolytic Degradation Of Poly-/Perfluoroalkyl Substances, Eman Zuhair Alhamdan

All Dissertations

PFAS are persistent synthetic chemicals, with unique properties that enable them to easily enter and accumulate in water sources. Their public health impacts have raised concern and highlighted the need for effective treatment. Numerous techniques for PFAS removal from water have been studied, few have proven the ability of breaking down PFAS rather than separating and concentrating it. Many of these methods are chain-length dependent, showing slower degradation rate of short-chain PFAS and requiring substantial amounts of energy, and chemical agents that may produce byproducts.

Among available PFAS destruction methods, direct photolysis by high-energy UV with wavelength below 200 nm …

Experimental And Computational Investigation Of The Effects Of Neck Musculature During Head Impacts In American Football, Anthony P. Marino

All Dissertations

Anthropometric test devices (ATDs) are human surrogate models used to represent the human body response in the context of compliance and safety testing. These devices are used in multiple industries, including automotive and aerospace, and have recently been incorporated into impact testing for the evaluation of helmet performance in American football. The Hybrid III 50^th percentile male ATD head and neck assembly is the most commonly used surrogate for these tests, although it was developed for automotive frontal crash scenarios that induce an inertial force on the body, leading to head and neck motion primarily in the sagittal plane. …

Versatile Exoskeleton Control Paradigms For Agile Human Locomotion: Task-Agnostic And Stability-Augmented Assistance, Miao Yu

All Dissertations

Lower-limb exoskeletons have shown great potential for assisting human locomotion, but designing controllers that provide assistance across diverse locomotor tasks and under external perturbations remains a major challenge. Many existing controllers for steady-state walking rely on pre-defined reference trajectories, which constrain voluntary human motion and limit adaptability across tasks. Moreover, they usually assume stable walking, while their performance under unstable conditions remains largely unexplored. In addition, existing gait stability augmentation controllers are often reactive rather than proactive to unstable conditions. In this dissertation, we propose control frameworks that preserve voluntary human motion while addressing two major challenges: providing task-agnostic assistance …

Quantifying The Value Of Adaptability: Bridging Real Options Analysis And Adaptable Building Design, Miranda Grice

All Dissertations

As the demands of the built environment continually evolve, buildings are expected to meet changing occupancy needs, or otherwise they become obsolete. Despite positive research on the benefits of adaptable building design, there is a knowledge gap regarding the long-term economic benefits of these designs. Practical methods are needed to determine if premium costs associated with adaptable designs are justified by the economic benefits inherent in adaptability. Therefore, this dissertation provides a discussion on the application of the financial theory of options analysis to the design of adaptable buildings. Additionally, a model and accompanying tool are presented as a solution …

Modernizing Plastics And Composites Manufacturing Using Hybrid Modeling Techniques, Luke Shannon

All Dissertations

Plastics and composites manufacturing encompasses a broad family of processes, including injection molding, resin transfer molding, compression molding, and extrusion, that together account for the vast majority of polymer and composite component production worldwide. These processes are inherently challenging due to the multi-physics nature of polymer processing, the vast possible combinations of resins, reinforcements, and additives, and the strong dependence of final part properties on process history. Among these processes, injection molding is one of the most widely deployed, producing high-volume components across automotive, medical, consumer electronics, and packaging industries. However, accurately monitoring and predicting process behavior in injection molding …

Multiscale Architecture Governs Stability In Suction-Actuated Variable Stiffness Catheters, Sheridan Lee

McKelvey School of Engineering Graduate Student Theses & Dissertations

Endovascular procedures require devices with widely varying mechanical properties: flexibility for navigating tortuous vessels and rigidity for stable therapeutic delivery. Suction-actuated variable stiffness sheaths address this challenge by incorporating axial wire string arrays that mechanically couple under suction to increase flexural rigidity. However, prototype devices achieve stiffening ratios below theoretical predictions, suggesting that interlayer mechanics and string positioning play a critical role. This study investigates whether interweaving polytetrafluoroethylene (PTFE) tape within the string array enhances flexural rigidity modulation and curvature stability. Prototype devices with varying wrap configurations were fabricated and evaluated using flexural testing, curvature stability testing, and mathematical modeling. …

Design Of An Extensible And Scalable Data Acquisition System For Pulse Shape Discrimination, Prince John

McKelvey School of Engineering Graduate Student Theses & Dissertations

This thesis presents the design and development of a highly scalable, end-to-end data acquisition (DAQ) system for nuclear physics experiments that can be deployed in configurations ranging from a few to thousands of detector channels. The system is built as an extensible platform composed of modular 16-channel chipboards that support a wide range of scintillator and detector types and perform real-time, on-board data sparsification and pulse-shape processing. Three versions of the chipboard have been fabricated to date.

The DAQ architecture is based on a family of analog pulse-shape-processing application- specific integrated circuits (ASICs) developed by the IC Design Laboratory at …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Erratum For "Soil Freezing Characteristic Surface For Partially Frozen Soils", Antai Dong, Xiong Zhang, Ming Xiao

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The following corrections should be made to "Soil Freezing Characteristic Surface for Partially Frozen Soils" by Antai Dong, Xiong Zhang, and Ming Xiao, https://doi.org/10.1061/JGGEFK.GTENG -13924: In the third sentence of the Abstract, the value for suction when completely dried is incorrect. It should be 106 kPa. The label at the end of the suction axes in Figs. 1(a) and 2 is similarly inaccurate. Corrected versions of Figs. 1 and 2 are provided herein. To preserve the published version of record, these details have been corrected only in this erratum. (Figure Presented)

Evaluation Of Radar-Derived Polarimetric Precipitation Estimates For Extreme Rain Events Using A Dense Network Of Rain Gauges, Bong Chul Seo, Witold F. Krajewski, James A. Smith, Alexander V. Ryzhkov

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The study evaluates radar-based quantitative precipitation estimations (QPEs) for 10 extreme rain events that occurred between 2013 and 2019 in the Kansas City metropolitan area, United States. These precipitation estimates were derived at hourly and approximately 0.5-km scales using two polarimetric QPE algorithms}one based on specific attenuation (A) and the other on specific differential phase (KDP)} for the study area covered by two overlapping radars in Topeka, Kansas, and Kansas City, Missouri. The polarimetric QPE assessment for extreme rain events was motivated by improved flood forecasting and precipitation frequency analysis. The analysis utilizes ground reference observations from a dense network …

A Federated Learning Framework For Data-Sovereign Predictive Maintenance In Distributed Smart Manufacturing, Md Sazol Ahmmed, Sriram Praneeth Isanaka, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Featured Application: The proposed federated learning framework can be applied in distributed smart manufacturing environments where multiple factories or production facilities collaboratively develop predictive maintenance models without sharing sensitive operational data. This approach is particularly useful for industrial networks involving geographically distributed plants, contract manufacturing partners, and multi-site production systems where data sovereignty and avoidance of raw data sharing are critical. Predictive maintenance enables early detection of machine failures and reduces unexpected production downtime. However, conventional approaches typically rely on centralized data collection and model training which introduce challenges related to data sovereignty, communication overhead and data ownership. To address …

Machine Vision For In Situ Measurement And Control Of Wire Stickout In Lwded Process, Braden Mclain, Remy Mathenia, Todd Sparks, Frank Liou

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This work presents a machine-vision–based measurement and control framework for laser wire directed energy deposition (LWDED) processes. A visible-light camera system is used to capture meltpool images, from which a novel vision algorithm extracts the wire–meltpool interface location. By utilizing a camera that is rigidly mounted to the deposition head, the vision algorithm provides a relative measurement of the distance between the nozzle tip and the workpiece, also referred to as wire stickout. A proportional-derivative (PD) control strategy is implemented using the measured stickout as feedback to adjust deposition feedrate. Results show that the control system successfully compensates for improper …