Engineering Commons^™

Applications Of Reservoir Simulation And Machine Learning In Subsurface Energy Systems For Decarbonization, Seyedmohammadmehdi Nassabeh

Theses: Doctorates and Masters

The transition to a low-carbon future necessitates innovative approaches to carbon management and hydrogen storage, particularly in the context of enhanced oil recovery (EOR) from hydrocarbon reservoirs. This study employs advanced analytics and machine learning techniques to optimize carbon management strategies. One key focus of this research is to evaluate the effectiveness of flue gas and CO2 in Water Alternating Gas (WAG) injection within a homogeneous fractured carbonate reservoir characterized by low porosity and permeability. A computational model was developed to depict the flow regime in the reservoir and simulate reservoir fluid behavior using Eclipse (E300) software, various hybrid EOR …

Lara : A Light And Anti-Overfitting Retraining Approach For Unsupervised Time Series Anomaly Detection, Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, Qingsong Wen

Research Collection School Of Computing and Information Systems

Most of current anomaly detection models assume that the normal pattern remains the same all the time. However, the normal patterns of web services can change dramatically and frequently over time. The model trained on old-distribution data becomes outdated and ineffective after such changes. Retraining the whole model whenever the pattern is changed is computationally expensive. Further, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light Anti-overfitting Retraining Approach (LARA) based on deep variational …

Methods Of Variability Reduction In Ramjet Fuel Injection Using Fluid-Structure Interactions, Egan J. Rigney

Honors Undergraduate Theses

The Liquid Jet in Crossflow (LJIC) method of fuel injection in ramjet engines is widely used and well-studied. However, they experience variation in their behavior at different altitudes and flight speeds due to the varied flow conditions following the inlet. This variation becomes more noticeable at high-temperature and high-speed airflow conditions. The optimized and consistent breakup of liquid fuel is a key component in ramjet engine design as there is a minimum surface area requirement of the droplets for combustion to occur. Local equivalence ratios also have a significant influence on the effectiveness of combustion. As such, researchers at the …

Quantized Average Agreement Algorithms With Error Correction For Digraphs, Shuaib A. Mughal

Honors Undergraduate Theses

Multi-agent systems have become more and more prevalent as technology increasingly gets integrated into our daily lives. Some of these technological systems are large in size; for example, the smart grid where multiple devices are used to monitor and control different aspects of the energy grid. Another example is a team of autonomous systems deployed for a specific task. When these systems are spatially distributed, an important component of distributed algorithms is the ability for the agents to reach consensus on the global state of the system. Reaching agreement enables the spatially distributed agent make decisions or determine the next …

A Signal Processing And Mechanical Design Approach To Understanding Triboelectric And Piezoelectric Nanogenerators’ Output, Nicholas Rose

Honors Undergraduate Theses

Scalable energy harvesters—capable of converting motion into electrical output—provide promising solutions to increasing energy demands. This thesis was inspired by the potential biomedical applications of piezoelectric nanogenerators (PENG) and triboelectric nanogenerators (TENG). In theory, these devices could convert mechanical energy from the body into usable electricity for self-powered electronics such as pacemakers or cochlear implants. In line with this goal, this project worked to design a sustainable and biocompatible PENG; however, early devices produced inconsistent voltage. These output inconsistencies motivated an exploration of device reproducibility, but characterizing piezoelectric output signals is a particular challenge in the field. Subtle to significant …

Behavior Analysis Of Simple And Complex Workloads, Davi D. Dantas

Honors Undergraduate Theses

Complex tasks have evolved rapidly in recent times due to tremendous advancements in computational power and data availability. With the ever-increasing presence of complex applications, this paper attempts to distinguish between complex and simple applications. This paper explored side-channel analysis as a method to differentiate complex workloads from simple workloads by monitoring system-level metrics such as power consumption, cache behavior, and memory accesses. By leveraging side-channel effects such as power analysis and memory access, this study seeks to establish unique hardware signatures for complex workloads. Using tools such as Intel Pin, the data will be collected from complex and simple …

Effects Of Changes In Strouhal And Reynolds Numbers On Beetle Wing Aerodynamics, Keith L. Thiha

Honors Undergraduate Theses

Insect flight has long been a subject of interest across the scientific community, largely due to the exceptional aerodynamic performance insects demonstrate relative to their size. Insects tend to have very high aerodynamic performance characteristics like thrust and lift generation compared to their size. This has many potential applications in improving aircraft performance, however the flow phenomena concerning insect wing aerodynamics is still an area of ongoing research. This study aims to investigate how variations in key nondimensional flow parameters—specifically the Reynolds number and Strouhal number—affect the aerodynamic performance of beetle wings. Strouhal numbers ranging from 0.2 to 0.6 were …

Exploring The Dynamics Of Cislunar Space: A Detailed Study Of Periodic Orbits And Their Applications In The Circular Restricted Three-Body Problem, Nicole Weeden

Honors Undergraduate Theses

Ever since NASA announced the Artemis program in 2019, the cislunar space, a region between Earth and the Moon, has attracted substantial research interest. Nonetheless, understanding the complex multipart physics that guides our solar system is not a new area of interest. The ThreeBody Problem was first formulated by Lagrange in ”Essai sur le Probleme des Trois Corps” in 1772 and had additional refinements done by mathematicians like Poincare. However, it was not until NASA’s Apollo program that the cislunar space gained traction, and the Circular Restricted Three-Body Problem (CR3BP) model became more prominent. By deriving and explaining the necessary …

3d Printing Of A Solid-State Battery, Maurizio A. Brozzi

Honors Undergraduate Theses

Solid-state battery research has gained significant attention in recent years, as it provides a unique solution for several common issues in conventional Li-ion batteries. Additionally, as additive manufacturing technologies continue to advance, they have the potential to fabricate solid-state batteries with new materials. The objective of this study is to explore the feasibility of 3D printing of a solid-state battery. By creating an initial PEO-PSF hybrid polymer matrix dissolved in DMSO and adding a lithium salt, the base electrolyte ink was formed. With the addition of conductive additives and active material, electrode inks were also produced. Super P was used …

Unconditional-To-Conditional Transfer And Optimization For Web-Based Skybox Gan, Crystal Kwong

Master's Projects

Generative adversarial networks (GANs) are known for their ability to generate high quality
images mimicking real life or even particular art styles. Yet for all their capability, casually
training a GAN on an average machine can be infeasible as GANs require an enormous amount
of time and data to train. Even with a trained GAN, model inference demands heavy
computations, making GANs difficult to deploy on applications. To address these limitations,
techniques such as transfer learning and quantization have been leveraged to speed up training of
GANs and lighten computational cost of GAN inference. This project aims to use such …

Phishing Detection Using Continual Learning And Large Language Models, Gopi Prajeev Battula

Master's Projects

Adaptive phishing detection remains crucial as the nature of cyber-attacks changes over time, which renders static models obsolete. This project extends phishing detection through the implementation of continual learning approaches, namely Elastic Weight Consolidation (EWC) and Learning Without Forgetting (LWF) with RoBERTa, a Large Language Model (LLM) and compares the results of these approaches against GPT-4o-mini, another LLM. Our approach begins with fine-tuning RoBERTa on multiple phishing datasets to establish an effective baseline. EWC is then implemented to preserve vital model parameters based on their importance measured by the Fisher Information Matrix, while LWF uses knowledge distillation to retain prior …

Disease Diagnosis Using Rag Llm With Smart Prompt Engineering, Qadeerullah Syed

Master's Projects

Although recent trends indicate that LLMs outperform traditional methods in solving complex problems with enhanced reasoning, there has been barely any progress in replicating the quality of diagnoses like those of actual human doctors. The identification of an accurate diagnosis with thorough reasoning is still a significant challenge, even with advanced AI models. The process of performing accurate diagnosis remains challenging due to a lack of transparency in state-of-the-art models existing today, a lack of explanation in the diagnosis process, an emphasis on results rather than reasoning, and a lack of foundational knowledge in models, along with limited exploration of …

Enhancing Recommender Systems Using Graph Neural Networks, Long Short-Term Memory And Textual Embeddings, Tianxiang Chen

Master's Projects

Recommender systems surround us. They shape what we watch, how we buy, and even what our future might look like next. The Amazon Review Dataset and Movielens, those two datasets will help this project explore how to improve recommender systems through the user’s preferences. Two methods were combined: sequence-based models and graph-based models. Sequence models, such as LSTMs and Transformers, look at the order of user actions to find patterns by their sequence. On the other hand, Graphbased models focus on relationships between users, items, and their attributes. Textual embeddings added depth and context. Both methods offer something special, according …

Ai Powered Legal Decision Support System, Alisha Rath

Master's Projects

The large volume of legal cases presented by judicial professionals has made it
challenging to study and predict results. With advances in research methods and
technology, predicting law cases in a more accurate manner has become an important
trend. Prediction tools based on AI may help manage a large number of legislative
texts and documents that cannot possibly be fully read, reduce the number of cases
to be seen, and give accurate outcomes of how cases may turn out. Now, when
we look into the current AI legal prediction tools in this domain, they mostly lack
efficiency and interpretability, the …

Coral Vision – Crustose Coralline Algae Detection With Computer Vision, Ryan Tseng

Master's Projects

Crustose coralline algae (CCA) are a group of red algae that are vital contributors to the health of coral reef ecosystems. Monitoring CCA abundance can serve as an indicator for coral reef health and improve reef conservation efforts. Autonomous Reef Monitoring Structures (ARMS) are artificial structures that can be deployed into coral reef ecosystems and retrieved to gather ecological data without harming reef structures. Traditional methods of calculating CCA abundance require manual analysis and are labor-intensive. Recent developments in computer vision and deep learning technology have provided an avenue to fully automate this task. This research aims to train a …

Multimodal Feature Fusion And Machine Learning For Adhd Detection Using Neuroimaging Data, Isabel Pham

Master's Projects

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopment disorder that can significantly affect a person’s attention, impulse control, and executive function. Currently, the traditional diagnosis method often relies on clinical assessments and observations. However, these methods can be subjective and lead to inconsistencies in diagnosis between individuals. To address this challenge, neuroimaging and machine learning (ML) are promising tools for providing a more objective diagnosis of ADHD. The goal of this project is to apply a multimodal approach in which structural and functional features of specific regions of the brain are used to develop a more accurate and objective …

Microplastics Detection, Characterization, And Cytotoxicity Evaluation Via Lab-On-Chip, Liyuan Gong

Open Access Dissertations

Microplastics (MPs) pose a growing challenge to the ecological system, prompting increasing research efforts in diverse directions. Accurate characterization of MPs is essential for understanding their properties, relying on efficient sampling and detection techniques. Additionally, studying the cytotoxic effects of MPs is critical for evaluating their impact on biological systems. Lab-on-chip (LoC) systems offer significant advantages for precise particle manipulation and compatibility with advanced analytical instrumentation, making them valuable for MP detection and characterization. Microfluidic-based on-chip cell-culturing systems provide a controllable, physiologically relevant microenvironment for more realistic in vitro tissue modeling. By leveraging advanced LoC concepts and integrating state-of-the-art machine …

Beyond The Phonon Gas Model (Pgm): Unraveling The Unique Mechanistic Processes Dictating Thermal Transport In Highly Anharmonic And Disordered Materials, Sandip Thakur

Open Access Dissertations

Thermal transport in nonmetallic solids has traditionally been described by the phonon gas model (PGM), in which heat is carried by weakly interacting phonon quasiparticles in an ordered crystalline lattice. However, this model breaks down in materials characterized by strong anharmonicity, dynamic disorder, or structural complexity, features prevalent in many next-generation materials used in energy conversion, optoelectronics, and thermal management. This dissertation investigates thermal transport in such complex materials, including metal halide perovskites (MHPs), covalent organic frameworks (COFs), metal organic frameworks (MOFs), and 2D-3D heterostructures, through large-scale molecular dynamics (MD) simulations and frequency-resolved spectral analyses.

In MHPs, the work reveals …

Comprehensively Understanding And Manipulating The Heat Transfer Mechanisms In Next-Generation Battery Materials, Connor Jaymes Dionne

Open Access Dissertations

Rechargeable batteries are paramount to the further development of green energy infrastructure as they allow for energy storage during peak production times, energy distribution during off-peak times, and provide the impetus to transition away from standard combustion engines in the transportation sector. The ability for rechargeable batteries to safely charge and discharge over multiple cycles is key to their successful application and long-term use. Moreover, the growing use of rechargeable batteries necessitates designs utilizing materials that are abundant and accessible to meet the ever-increasing demands required by the green energy and transportation sectors.

To address these needs, we first investigate …

Machine Learning Guided Insights Into Phonon Scattering Mechanisms For Tunable Thermal Transport In Materials From First-Principles, Niraj Bhatt

Open Access Dissertations

As device dimensions shrink in the current era of miniaturization, effective thermal management at the material level has become critical. In ultrasmall device lengths, traditional electronic heat transport becomes severely limited as boundary scattering effects curtail the electronic transport because the electronic mean free paths substantially exceed those of phonons. The resulting high-power- density devices generate thermal hot spots that compromise both performance and long-term reliability. This challenge has intensified the search for materials with superior phonon-mediated heat transport, a key requirement for effective thermal management in next-generation nanoelectronics. Accurately predicting thermal transport properties with near-experimental accuracy is therefore essential. …

Electro-Vascular Dynamics During Auditory Processing: Variation Along The Schizotypy Continuum, John P. Mclinden

Open Access Dissertations

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are complementary noninvasive neuroimaging modalities that have contributed greatly to our understanding of the mechanisms of auditory processing. As an emerging technology, fNIRS has produced promising insights into the mechanisms of auditory processing, although considerable work remains to characterize hemodynamic responses in auditory tasks. In addition, the complementary nature of EEG and fNIRS and the relationships between the signals they record have been underexplored. In particular, nonlinear interactions between these signals remain under-characterized, despite the potential benefit to future multimodal neuroimaging studies. A challenge in understanding these interactions is the influence of systemic …

Characterizing Heat Transfer Performance In A Slurry Bubble Column Reactor Equipped With A Real Heat Exchanger, Dalia S. Makki, Hasan Sh Majdi, Amer A. Abdulrahman, Abbas J. Sultan, Bashar J. Kadhim, Muthanna H. Al-Dahhan

Chemical and Biochemical Engineering Faculty Research & Creative Works

Abstract: This study examines the impact of equipping a real heat exchanger in a slurry bubble column SBC on instantaneous and local heat transfer coefficients IHTC and LHTC, as well as the overall heat transfer coefficient U, employing advanced heat transfer techniques. The experiments were conducted in a 0.15 m inner diameter Plexiglas SBC with varying gas flowrates Ug (0.14–0.35) m/s at several radial sites along the column's diameter (±0.18, ±0.46, and ± 0.74) and three axial locations (H/D = 2, 3 and 4). To simulate the industrial Fischer–Tropsch bubble column reactor FT-BCR, a real heat exchanger consisting of 18 …

Seasonality Of Cyanobacteria And Eukaryotes In Lake Geneva And The Impacts Of Cyanotoxins On Growth Of The Model Ciliate Tetrahymena Pyriformis, Niveen S. Ismail, Paul Seguin, Lola Pricam, Elisabeth M.L. Janssen, Tamar Kohn, Bas W. Ibelings, Anna Carratalá

Engineering: Faculty Publications

Toxic cyanobacteria are likely to be favored by global warming and other human impacts, posing significant threats to aquatic ecosystems. While cyanobacterial blooms in eutrophic lakes are widely investigated, the dynamics of cyanobacteria and the effects of their toxins and bioactive metabolites on the plankton communities in mesotrophic and oligotrophic lakes are less well understood. Here we investigated seasonal dynamics of cyanobacteria, eukaryotic algae and cyanotoxins in oligo-mesotrophic Lake Geneva—the largest and deepest lake in western Europe. High-throughput sequencing of the 16S rRNA genes in 143 samples along a water column revealed that Lake Geneva hosts diverse, co-dominant cyanobacterial genera, …

Pharmacological Or Genetic Inhibition Of Ltcc Promotes Cardiomyocyte Proliferation Through Inhibition Of Calcineurin Activity, Lynn A. C. Devilée, Abou Bakr M. Salama, Jessica M. Miller, Janice D. Reid, Qinghui Ou, Nourhan M. Baraka, Kamal Abou Farraj, Madiha Jamal, Yibing Nong, Todd K. Rosengart, Douglas A. Andres, Jonathan Satin, Tamer M. A. Mohamed, James E. Hudson, Riham R. E. Abouleisa

Markey Cancer Center Faculty Publications

Cardiomyocytes (CMs) lost during ischemic cardiac injury cannot be replaced due to their limited proliferative capacity. Calcium is an important signal transducer that regulates key cellular processes, but its role in regulating CM proliferation is incompletely understood. Here we show a robust pathway for new calcium signaling-based cardiac regenerative strategies. A drug screen targeting proteins involved in CM calcium cycling in human embryonic stem cell-derived cardiac organoids (hCOs) revealed that only the inhibition of L-Type Calcium Channel (LTCC) induced the CM cell cycle. Furthermore, overexpression of Ras-related associated with Diabetes (RRAD), an endogenous inhibitor of LTCC, induced CM cell cycle …

Advances In Magnetic Nanoparticles For Molecular Medicine, Xiaoyue Yang, Sarah E. Kubican, Zhongchao Yi, Sheng Tong

Markey Cancer Center Faculty Publications

Magnetic nanoparticles (MNPs) are highly versatile nanomaterials in nanomedicine, owing to their diverse magnetic properties, which can be tailored through variations in size, shape, composition, and exposure to inductive magnetic fields. Over four decades of research have led to the clinical approval or ongoing trials of several MNP formulations, fueling continued innovation. Beyond traditional applications in drug delivery, imaging, and cancer hyperthermia, MNPs have increasingly advanced into molecular medicine. Under external magnetic fields, MNPs can generate mechano- or thermal stimuli to modulate individual molecules or cells deep within tissue, offering precise, remote control of biological processes at cellular and molecular …

Functional Assessment Of Migration And Adhesion To Quantify Cancer Cell Aggression, Lauren E. Mehanna, James D. Boyd, Chloe G. Walker, Adrianna R. Osborne, Martha E. Grady, Brad J. Berron

Markey Cancer Center Faculty Publications

During epithelial-to-mesenchymal transition (EMT), cancer cells lose their cell–cell adhesion junctions as they become more metastatic, altering cell motility and focal adhesion disassembly associated with increased detachment from the primary tumor and a migratory response into nearby tissue and vasculature. Current in vitro strategies characterizing a cell’s metastatic potential heavily favor quantifying the presence of cell adhesion biomarkers through biochemical analysis; however, mechanical cues such as adhesion and motility directly relate to cell metastatic potential without needing to first identify a cell specific biomarker for a particular type of cancer. This paper presents a comprehensive comparison of two functional metrics …

Photoproof: A Mobile Application For Verifying The Authenticity Of Images, Pruthviraj Urankar

Master's Projects

The easy access to artificial intelligence (AI) technologies, such as deepfakes and generative adversarial networks (GANs), has facilitated the creation of highly realistic artificial images, thereby undermining the authenticity of photos in today’s digital age. Misinformation and manipulation are key dangers to digital content due to this advancement. Therefore, the need for reliable methods of photo verification and authentication has become increasingly important. This report presents a decentralized iOS app that uses blockchain to ensure photo authenticity. The app leverages Ethereum smart contracts and cryptographic hashing to securely log image metadata. When a user takes a photo, the app hashes …

Optimizing Analytics Storage Strategies For Search Engines And Wiki Platforms, Sujith Kakarlapudi

Master's Projects

Modern search engines and wiki platforms generate vast quantities of user inter-
/="/">action data such as page views, edits, clicks, and session events. This data must be stored and aggregated efficiently to enable scalable analytics and responsive querying. Yioop, an open-source search engine framework, serves as our primary case study, processing millions of such events to power its indexing and recommendation features. This report explores a shift from the conventional database storage based model to a log based model, in order to improve scalability and write efficiency. A size-limited, append-only logging facility was provided to log analytics events: the …

Traffic Forecasting With Vset-Nets: A Vgae Spatial Embedding For Temporal Networks Approach, Mrunmayee Dhapre

Master's Projects

Traffic forecasting is important for improving transportation systems by enabling better traffic management, congestion reduction, and urban planning. However, predicting traffic accurately is challenging due to the strong spatial dependencies between different road segments and the temporal changes in traffic patterns over time. Traditional time-series and graph models often struggle to capture both of these aspects effectively. In response, recent research has focused on temporal graph representation learning methods that jointly consider spatial relationships and temporal features in networks. This project proposes a hybrid model called VSET-Nets (VGAE Spatial Embedding for Temporal Networks) that employs Variational Graph Autoencoders (VGAEs) for …

Image-To-Text Transcription: Analyzing And Describing Visual Content, Zixiao Fan

Master's Projects

Image captioning, which provides a textual understanding of visual content, is the fundamental support for the advancement of Human-A.I. Interaction technology. In the hope of exploring the application of such technology, this project focuses on two specific goals. One is to directly explore the application of the image informationretrieving abilities, and the other is to dive into the specifics of the pipeline and components of image captioning models. As a result, this project presents a working app that exploits the text retrieval functionalities to enable image storage with functions like tagging and transcription. It also supports search functionality with a …