Engineering Commons^™

Short-Arc Angles-Only Initial Orbit Determination For Leo And Geo Objects Using A Genetic Algorithm, Joseph R. Piini

Master's Theses

An ever-growing interest in utilizing space for scientific, commercial, and defense applications has led to an exponential rise in orbital debris recent years. With this sharp increase in space objects comes a greatly increased risk of potential collisions, necessitating an increase in Space Situational Awareness. In order to best mitigate the risk of future collisions in an increasingly congested space environment, it is crucial to be able to accurately determine the orbits of these new objects for trajectory tracking. This process is initial orbit determination (IOD), and is especially difficult when only observation angles are available for short observation times. …

Dynamic Modeling And Simulation Of A Power Generation Steam Rankine Cycle For A Fluoride Salt-Cooled High-Temperature Reactor Nuclear Power Plant, William R. Ton

Master's Theses

Nuclear Fluoride Salt-Cooled High-Temperature Reactors (FHRs) have the potential to greatly improve nuclear technology in terms of safety and cost. By combining molten salt coolant technology from the Molten Salt Reactor (MSR) and TRISO fuel technology from the Very High Temperature Gas Reactor (VHTR), FHRs may operate at significantly lower pressures than Light Water Reactors (LWRs) and with a more robust fuel source that is excellent at containing fission products. However, as no FHR has been built, there are still many design questions to address. This thesis uses transient computer modeling and simulation to investigate how an FHR could potentially …

Real Time Markerless 3d Hand Tracking For Intuitive Robotic Arm Control, Dylan G. Featherson

Master's Theses

This thesis presents a real-time, markerless motion-capture system for intuitive control of a multi-degree-of-freedom robotic arm. Using two synchronized RGB cameras and stereo-vision triangulation, the system reconstructs the three-dimensional position of a user’s hand without the need for physical markers or wearable sensors. The reconstructed 3D coordinates are mapped directly to the end-effector position of the Quanser QArm, a four-degree-of-freedom educational manipulator. Developed entirely in Python, the system integrates stereo camera calibration, 3D hand tracking with MediaPipe, coordinate transformation, and inverse kinematics into a unified real-time control pipeline operating at approximately 200 Hz. Calibration is performed through a checkerboard-based stereo …

Origami-Inspired Pneumatic Soft Robotic Gripper, Conor J. Schott

Master's Theses

This thesis presents the design and evaluation of a vacuum-actuated, origami-based soft robotic gripper. A custom Sea Urchin crease pattern forms a lightweight folding skeleton that collapses radially under vacuum. The system emphasizes low cost and accessibility through laser cutting, 3D printing, and silicone molding. The Sea Urchin geometry was selected after digital and physical screening of candidate crease patterns for smooth folding, low strain concentration, and uniform inward motion. The folding skeleton is laser cut from 4 mil drafting film and folded into a compliant core. Four membrane configurations were developed: an origami gripper with a 36-inch balloon (Design …

Per- And Poly-Fluoroalkyl Substances Fate Across California Wastewater Treatment Plants: A Meta-Analysis Of Formation, Distribution, And Persistence, Elizabeth Reineke

Master's Theses

Per- and poly-fluoroalkyl substances (PFAS) are persistent contaminants that move through municipal and industrial wastewater systems with limited attenuation. As a result, they are increasingly detected in drinking water supplies. Despite growing regulatory concern, a systems-level understanding of PFAS behavior across multiple treatment facilities and watershed settings remains incomplete. This study compiled influent, effluent, and biosolids datasets from more than one hundred California wastewater treatment plants to evaluate PFAS occurrence, transformation, and partitioning across diverse treatment configurations. Analytical steps included calculating removal efficiencies, assessing precursor-to-product oxidation, evaluating co-contaminant correlations, and grouping facilities by process design to quantify configuration-specific fate patterns. …

Hallucination Techniques For Self-Supervised Synthetic Datasets For Mobile Robots, Wyatt D. Colburn

Master's Theses

Classical techniques in autonomous navigation struggle in tightly constrained spaces. Machine learning has been shown to perform better in these difficult environments but most techniques require large amounts of navigation experience for training. Using a new machine learning paradigm learning from hallucination (LfH), training data can be collected in a safe environment and not require supervision. Data is collected in real time while an agent performs a random walk in free space, supervision is not required as there are no obstacles for the robot to run into. After a random walk a post processing pipeline will hallucinate a safety corridor …

Time-Domain Symmetrical Components For Detection And Analysis Of Unbalanced Faults In Three-Phase Systems, Ryan Pospichal

Master's Theses

Unbalanced faults in three-phase power systems degrade performance, increase losses, and threaten equipment reliability. Traditional frequency-domain approaches using phasor analysis and symmetrical components offer valuable steady-state insights but respond too slowly for transient or rapidly changing conditions. This thesis investigates time-domain symmetrical components (ISCs) as a faster, physically interpretable method for detecting and analyzing unbalanced faults directly from instantaneous voltages and currents. Two ISC computation techniques were developed and compared: a polar-form method, applying one-third and two-thirds cycle time delays, and a rectangular-form method, which achieves equivalent phase shifts using only a quarter-cycle delay. Simulations in Microsoft Excel verified both …

Piezoelectric Energy Harvesting From Roadways: Challenges, Advances, And Future Directions, Heba Mohamed Gaber, Mohamed Abdel-Raheem

Civil Engineering Faculty Publications

As the global demand for renewable energy intensifies, piezoelectric energy harvesting from roadways has emerged as a promising avenue for sustainable power generation. This systematic literature review analyzes 61 peer-reviewed studies to assess the feasibility, performance, and potential of integrating piezoelectric systems into roadway infrastructure. While technology faces challenges, such as high installation costs, limited energy output, and a scarcity of thorough economic evaluations, findings suggest it holds considerable promise as a supplementary renewable energy source. The review analyzes the operational characteristics and efficiencies of various piezoelectric transducers, identifies key factors influencing system performance, and evaluates recent technological advances. It …

Bridging The Gap Between Network Science And Network Systems To Identify And Mitigate Cyber Risk: Identify And Mitigate Backdoor Attacks On Graph Neural Networks And On Complex Systems, Sabah Ettahri

Electrical & Computer Engineering Projects for D. Eng. Degree

This doctoral project aims to bridge the gap between graph theory and network science to identify and mitigate cyber risk, represented as a CY-Triangular Network that connects different networks. The CY-Triangular Framework is a cybersecurity system that integrates graph theory and network science through an interoperable learning approach. The objective of this project is to bridge the gap between two domains: network science and network systems. Accordingly, it examines one representative network from each field, focuses on a complex system network, and explores Graph Neural Networks (GNNs). The connection between these domains lies in graph theory. This research demonstrates that …

2025 Summer-Fall Graduate Recognition Program, Propulsion Research Center, Robert Frederick

PRC Graduate Recognition

This program includes the graduate recognition program for summer and fall 2025. Includes photographs, upcoming events, recent publications, and student profiles.

Porous Triboelectric Nanogenerator For Load Sensing Of Total Knee Replacement, Elham Mahmoudi, Adam Garry Redgrift, Emre Salman, Milutin Stanacevic, Ryan Willing, Shahrzad Towfighian

Mechanical Engineering Faculty Scholarship

This study addresses the critical need for self-powered, durable pressure sensors in Total Knee Replacement (TKR) implants to enable the collection of post-operative information. The scope of this research encompasses the design, development, and testing of a triboelectric nanogenerator (TENG) integrated into an instrumented knee implant for energy harvesting and pressure sensing. The authors’ unique approach involves utilizing a porous silicone rubber as a dielectric material. This allows the TENG to withstand forces up to 2000 N and generate a maximum power output of 18 μW. Theoretical modeling combined with experimental validation provides deeper insight into the fundamental operating principles. …

Bridging Cybersecurity Practice And Law: A Hands-On, Scenario-Based Curriculum Using The Nice Framework To Foster Skill Development, Colman Mcguan, Aadithyan Vijaya Raghavan, Komala M. Mandapati, Chansu Yu, Brian Ray, Debbie Jackson, Sathish Kumar

Electrical and Computer Engineering Faculty Publications

In an increasingly interconnected world, cybersecurity professionals play a pivotal role in safeguarding organizations from cyber threats. To secure their cyberspace, organizations are forced to adopt a cybersecurity framework such as the NIST National Initiative for Cybersecurity Education Workforce Framework for Cybersecurity (NICE Framework). Although these frameworks are a good starting point for businesses and offer critical information to identify, prevent, and respond to cyber incidents, they can be difficult to navigate and implement, particularly for small-medium businesses (SMBs). To help overcome this issue, this paper identifies the most frequent attack vectors to SMBs (Objective 1) and proposes a practical …

Multi-Modal Data-Efficient Learning For 3d Machine Vision, Zhimin Chen

All Dissertations

The rapid progress of 3D computer vision has enabled a wide range of applications in autonomous driving, robotics, and augmented reality. Despite this growth, training robust 3D perception models remains challenging due to limited labeled data, the complexity of integrating multiple modalities, and the inherently imbalanced and long-tailed nature of 3D datasets. This dissertation addresses these challenges by proposing data-efficient, multi-modal learning frameworks that improve the accuracy, generalization, and scalability of 3D scene understanding.

In the semi-supervised setting, this work presents novel approaches that combine limited annotations with large amounts of unlabeled data to enhance 3D object classification and retrieval. …

Synthesis And Characterization Of Chemically Recyclable Polypropylene-Based Covalent Adaptable Networks, Andrii Tiiara

All Dissertations

Polyolefins (POs) are inexpensive engineering materials with excellent physical and mechanical properties, accounting for nearly 60% of all thermoplastics. However, large-scale recycling remains limited, with only ~15% undergoing mechanical recovery. This dissertation addresses this challenge by fragmenting and functionalizing polypropylene (PP) chains to generate macromonomers and employing them, along with industrial reactive PP macromonomers, to synthesize, depolymerize, and repolymerize chemically recyclable polypropylene-based polyolefins (CR-POs). These CR-POs incorporate ester linkages that form covalent adaptable networks (CANs), maintaining a gel fraction of ~70% while remaining melt-reprocessable via extrusion and compression molding.

First, the fragmentation of PP is investigated by microwave irradiation. Microwave …

Robust Data-Driven Predictive Control Of Nonlinear Systems Under Modeling Uncertainty, Pegah Ghafghanbari

All Dissertations

Data-driven predictive control enables designing controllers directly from data, making it attractive for complex systems with hard-to-model dynamics. However, practical deployment is challenged by modeling inaccuracies and changing operating conditions. This dissertation develops predictive control frameworks that incorporate robustness and adaptability to address these issues in uncertain nonlinear systems.

The first part employs the Linear Parameter-Varying (LPV) framework, which represents nonlinear dynamics through simple linear form representation. To characterize the plant-model-mismatch often caused by limited data and numerical calculations, Bayesian Neural Networks (BNNs) are used, and their uncertainty estimates are integrated into two robust control approaches. The first is a …

Surface Reaction And Transportation Of Radium-226 In Marine Systems, Jiusi Wang

All Dissertations

Historical uses of radium (Ra)-containing self-luminous paint, such as on gauges and dials in naval ships, have led to the potential for such paint, and thus Ra, to be released into the marine environment. Radium-226 (²²⁶Ra) behavior in marine systems is not fully described in the literature, presenting a knowledge gap in the process of evaluating potential ecological impact of Ra in marine systems. This study explores various mechanisms contributing to Ra environmental transport in marine systems on a laboratory scale, including leaching, sorption, and diffusion. ²²⁶Ra leaching from watch hand dials containing radioactive luminous paint is …

Advancing Life Cycle Assessment Of Mass Timber: Environmental Performance, Circular Design And End-Of-Life Strategies, Muzan Williams Ijeoma

All Dissertations

Mass timber has emerged as a leading candidate for low-carbon construction, offering the dual benefits of reduced embodied emissions and biogenic carbon storage. Yet, fully realizing its sustainability potential requires a comprehensive and context-specific understanding of its environmental performance across the entire life cycle, from raw material production to building operation and beyond end-of-life (EoL). This dissertation advances life cycle assessment (LCA) practices for mass timber by addressing critical challenges across multiple phases of its life cycle. This dissertation is comprised of three published papers, and two submitted manuscripts. First, I conducted a critical review and meta-analysis of mass …

Hydrodynamics Of Perforated Heave Plates, Muhammad Usman

All Dissertations

Ocean waves are a clean and renewable energy source that can help reduce dependence on fossil fuels and combat climate change. To capture this energy, researchers have developed devices known as wave energy converters (WECs). One common type, called a two-body point absorber WEC, uses a buoy floating on the ocean surface connected to a submerged reaction plate, known as the heave plate, which lies underwater. The relative up-and-down motion between the buoy and the heave plate, driven by ocean waves, generates usable power via a power take-off (PTO) system. Traditionally, these submerged heave plates are solid structures. However, this …

Opposed-Piston Two Stroke Engine As A High Power Density Solution In Heavy Duty Applications: An Experimental Evaluation With Diesel And Ethanol, Ankur Bhatt

All Dissertations

With an increasing demand to reduce greenhouse gas (GHG) emissions, the conventional powertrain technology is at a saturation point. There is reinvigorated research interest in alternative powertrain designs that offer potentially higher efficiencies and lower engine-out emissions compared to conventional powertrains. The opposed piston two-stroke (OP2S) engine is one such alternative engine architecture that has demonstrated a reduction in engine-out emissions and increased efficiency compared to conventional four-stroke diesel engines. Like any two-stroke engine, the scavenging process and the composition of the internal residuals are predominantly governed by the pressure differential between the intake and the exhaust ports. Without dedicated …

Theory And Technology Of Enhanced Coal Seam Gas Production By Integrated Drilling, Hydraulic Punching And Fracturing In Coal Mine: Part 2 Technology, Shiyao Yu, Xianbo Su, Jinxing Song, Qian Wang, Zhenjiang You

Research outputs 2022 to 2026

To enhance the efficiency and cost-effectiveness of coal seam gas extraction in coal mines, this study introduces an integrated approach combining drilling, hydraulic punching, and fracturing technologies. This method optimizes drilling media tailored to specific coal seams and fine-tunes technical parameters during drilling and permeability improvement processes. Furthermore, it clarifies the relationships among the coal breaking rate, discharge of drill cuttings, jet velocity, rotation speed, and displacement. This ensures precise and effective permeability improvement in both soft and hard coal formations. To implement this technology, a set of high-performance drilling tool with a large inner diameter, high pressure resistance, and …

The Significant Impact Of Hot Isostatic Pressing On The Corrosion Behavior Of Ti-6al-4v Alloy Prepared By Electron Beam Powder Bed Fusion, Hongyu Zheng, Yun Bai, Wentao Hou, Zirong Zhai, Lai Chang Zhang, Shujun Li, Rui Yang

Research outputs 2022 to 2026

This work investigated the effect of hot isostatic pressing (HIP) on the corrosion behavior of Ti-6Al-4V samples prepared by electron beam powder bed fusion (EB-PBF). After the HIP treatment, the TiO2 content decreased but the growth rate of passivation films increased, leading to increase in internal defect density, thereby degrading the corrosion resistance. Amongst the different factors impacted by HIP including pore defects, β-phase, texture and morphology of α-phase, the morphology of α-phase played a dominant role in determining the corrosion performance of EB-PBF processed alloy.

Confinement-Dependent Transient Mi For The Hoek-Brown (H-B) Strength Criterion, Hao Li, Leo Pel, Zhenjiang You, David Smeulders

Research outputs 2022 to 2026

The growing global demand for mineral resources necessitates deeper excavation, where increasing in situ stresses pose significant geomechanical challenges. Consequently, an accurate strength assessment of deep-seated rock masses is crucial for ensuring the safety and reliability of underground excavations. The Hoek-Brown (H-B) strength criterion remains a widely applied rock strength criterion. However, its conventional form struggles to characterize the full nonlinear evolution of rock strength from pre-critical to critical conditions across varying confining stresses. To address this limitation, this study proposes a Confinement-dependent Transient mi Model (CTM) as an enhancement to the H-B criterion. By incorporating confinement-sensitive variations of mi, …

A Critical Review On Selecting Performance Evaluation Metrics For Supervised Machine Learning Models In Wastewater Quality Prediction, Hoda Khoshvaght, Ratish Ramyad Permala, Amir Razmjou, Mehdi Khiadani

Research outputs 2022 to 2026

While numerous machine learning (ML) models have been applied to wastewater treatment plant (WWTP) quality prediction tasks, significantly less attention has been paid to the selection and interpretation of performance evaluation metrics. Most studies rely on general-purpose regression metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination (R²). Although widely used, these metrics differ considerably in terms of interpretability, sensitivity to data anomalies, and suitability for dynamic, noisy environments like WWTPs. This review, based on a systematic literature analysis of 27 performance evaluation metrics, critically …

Enhanced Degradation Of Triclosan Using Aerated Hydrodynamic Cavitation: Turbulence-Based Modelling And Economic Evaluation, Mehdi Khiadani, Ensiyeh Taheri, Veena Bobade, Ali Fatehizadeh

Research outputs 2022 to 2026

Pharmaceutical and personal care product (PPCP)-contaminated wastewater, particularly with triclosan (TCS), poses a significant environmental concern due to TCS persistence, ecotoxicity, and endocrine-disrupting effects, necessitating the development of efficient and sustainable remediation technologies. In this study, the degradation of TCS using hydrodynamic cavitation coupled with air injection (aerated HC process) is investigated as a green and chemical-free treatment method for wastewater treatment. Initially, the optimum operating conditions of the standalone HC process were determined by varying the inlet pressure and flow rate, which were found to be 2.34 bar and 0.45 L/s, respectively, corresponding to the highest turbulence frequency level …

Experimental Study On Stability, Rheology, And Co2 Storage Capacity Of Polymer-Enhanced Foams In Carbonates, Muhammad Mushtaq, Mudassar Mumtaz, Emad W. Al-Shalabi, Waleed Alameri, Georgios N. Karanikolos, Stefan Iglauer

Research outputs 2022 to 2026

Polymer-enhanced CO2 foam (PEF) is an emerging technology that improves gas mobility control in chemical enhanced oil recovery applications. It also has significant potential for storing CO2 in geological formations, which can help mitigate anthropogenic emissions. Traditional surfactant-based foams are often weak and unstable, particularly under the high-salinity and high-temperature conditions commonly found in Middle Eastern carbonate reservoirs. However, adding a small amount of stable polymer to these foams can enhance their stability, rendering them more suitable for harsh environments. This study focuses on designing and thoroughly evaluating a PEF formulation under supercritical CO2 conditions at 95 °C temperature, 1500 …

Interface Engineering And Safety In Solid-State Batteries: Advancing From Human-Centered Insights To Ai-Driven Innovations, Elnaz Karimi, Stefan Iglauer, Muhammad Rizwan Azhar

Research outputs 2022 to 2026

Solid-state batteries (SSBs) represent a transformative advancement in energy storage, offering superior safety, higher energy density and extended cycle life compared to conventional lithium-ion batteries (LIBs). However, challenges related to interface engineering—particularly in ensuring stable electrochemical performance and preventing lithium dendrite formation—have hindered their widespread adoption and can compromise safety. Effective interface engineering is critical for mitigating interfacial resistance, enhancing mechanical stability and preventing thermal runaway, all of which are vital for improving battery reliability. The integration of artificial intelligence (AI) and machine learning (ML) in this context accelerates battery optimization by enabling predictive modelling of interfacial behaviour, material discovery …

Machine Learning Assisted Quality Control In Metal Additive Manufacturing: A Review, Zeqi Hu, Changlin Huang, Lechun Xie, Lin Hua, Yujie Yuan, Lai Chang Zhang

Research outputs 2022 to 2026

Additive manufacturing (AM) promotes the production of metallic parts with significant design flexibility, yet its use in critical applications is hindered by challenges in ensuring consistent quality and performance. Process variability often leads to defects, insufficient geometric accuracy and inadequate material properties, which are difficult to effectively manage due to limitations of traditional quality control methods in modeling high-dimensional nonlinear relationships and enabling adaptive control. Machine learning (ML) offers a transformative approach to model intricate process-structure-property relationships by leveraging the rich data environment of AM. The study presents a comprehensive examination of ML-driven quality assurance implementations in metallic AM. First, …

Performance Study Of A Woody Downdraft Gasifier: Numerical Investigation And Experimental Validation, Md Sanowar Hossain, Showmitro Bhowmik, Mujahidul Islam Riad, Md Golam Kibria, Barun K. Das, Sanjay Paul

Research outputs 2022 to 2026

Biomass gasification is an established and widely utilized renewable energy system. The research work aims to develop and construct a downdraft gasifier to investigate gasifier performance. The gasifier’s performance and cold gas efficiency were calculated for three volumetric airflow rates: 7.16 m3/h, 5.97 m3/h, and 4.78 m3/h. The efficiency was found maximum of 69.6% for an airflow rate of 7.16 m3/h. The oxidation zone and neck region of the gasifier reactor had the maximum recorded temperatures of 845 °C and 823 °C for Swietenia macrophylla (SM) and Mangifera indica (MI), respectively. A two-dimensional computational fluid dynamics (CFD) model for a …

Optimization Of Size And Siting Of Distributed Generation In Unbalanced Distribution Systems: A Literature Review, Pema Dorji, Stefan Lachowicz, Octavian Bass

Research outputs 2022 to 2026

Renewable energy sources (RES) are essential for meeting the rising global electricity demand while reducing greenhouse gas emissions from conventional generation. As traditional systems approach capacity saturation, the integration of RES into power grids becomes increasingly vital. However, the intermittent and variable nature of RES introduces significant technical, economic, and operational challenges. This review focuses on the optimal planning and integration of distributed generation in unbalanced distribution systems, which more accurately reflect real-world power network conditions. Emphasis is placed on siting and sizing strategies aimed at enhancing voltage stability, minimizing power losses, and reducing system costs and emissions. The review …

Application Of Plastic Waste As A Sustainable Bitumen Mixture—A Review, Nuha S. Mashaan, Thakur Chamlagai

Research outputs 2022 to 2026

Plastic waste is growing rapidly, while asphalt binders remain heavily reliant on petroleum bitumen. Incorporating recycled plastics into bitumen can divert waste and enhance pavement performance. This review compiles 251 experimental records from 56 studies to evaluate how plastic type, dosage, and processing conditions affect softening point, penetration, and viscosity. Across studies, plastics (PET, LDPE/HDPE/LLDPE, PP, and hybrids) consistently stiffen binders, reducing penetration and increasing softening point and viscosity, thereby improving rutting resistance while potentially raising mixing/compaction demands. Using grouped cross-validated machine-learning models (median baseline, ridge, random forest, XGBoost), we quantify the predictability of binder properties and show that nonlinear …