Computational Engineering Commons^™

Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee

Chemical Engineering Undergraduate Honors Theses

This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …

Signal Modeling Of High Purity Germanium Detectors, Brooke Parker Thibodeau

Honors Scholar Theses

Pulse shape analysis can be used to analyze radiation incident on a semiconductor diode detector. Finite element analysis (FEA) can be used to simulate high purity germanium detectors with user-specified geometry, impurity concentration, and bias voltage. Subsequent streamline calculation can be used to conduct pulse shape analysis for single and multiple site gamma-ray interactions within the detector volume. Cross sections of the detector can be visualized by contour plots of the electric field, drift velocity magnitude, and collection time, as well as t30 and t90 rise-time values.

Design Considerations Of A Gpu, Nicholas M. Devilliers

Electrical Engineering and Computer Science Undergraduate Honors Theses

With the current era of AI technology, the era of single instruction multiple data has become an increasingly viable solution to accelerate training. The problem is that while software to use GPUs and other hardware accelerators, designing GPUs and ASIC devices has become increasingly more expensive and there aren’t great examples of generic GPUs that anyone can use and modify. In this thesis, there are four design considerations that will be discussed and how they affect the result of a generic GPU. The four considerations that were talked about in the thesis are, word width, arithmetic type, number of stages, …

Urban-Rural Dynamics And Dui Fatalities In The Inland Empire: A Neural Network Analysis Of Traffic Safety Disparities, Armando Ceja-Lua

Electronic Theses, Projects, and Dissertations

This study examines the disproportionately high traffic fatality rates in California's Inland Empire region through neural network analysis of over 500,000 accidents (2013-2022). We argue that the Inland Empire's unique hybrid urban-rural landscape creates a multiplicative risk environment unlike other California regions. Our analysis reveals that San Bernardino County's fatality rate (1.920 per 100 million VMT) significantly exceeds neighboring regions, with alcohol-impaired driving fatalities (0.586) substantially higher than California's average (0.390). Neural network models (92% validation accuracy) identify pedestrian-involved collisions (correlation value 0.164) and alcohol involvement (0.075) as the strongest predictors of fatality in urban areas, while rural crash patterns …

False Information Attack Detection In A Connected Vehicle Environment With Quantum Inspired Long Short-Term Memory, Jean Michel M. Tine

All Theses

Wireless communication Systems enabling Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) data exchange, supported by technologies such as Cellular-V2X (C-V2X), has introduced significant cybersecurity challenges, particularly the threat of false information attacks that can compromise traffic safety and efficiency. In this thesis, the author focuses on identifying false information cyber-attack on V2I, in which vehicles are sending Basic Safety Messages (BSMs) to a Roadside Unit (RSU) and RSUs are collecting, processing and communicating data back to Connected Vehicles (CVs) to support different CV applications. Despite advances in anomaly detection using Long Short-Term Memory (LSTM) networks, these models often struggle with computational efficiency …

Mechanistic Insights Into Polymer-Assisted Graphene Exfoliation: The Roles Of Velocity, Adhesion, Cohesion, Temperature, Peeling Mode, And Edge Defect Via Coarse-Grained Molecular Dynamics, Linjiale Dai

All Theses

Graphene exfoliation is a critical step in the fabrication of high-quality graphene

layers. However, the underlying fracture mechanisms remain poorly understood. In this

work, I employed coarse-grained (CG) molecular dynamics (MD) simulations to

investigate how factors such as interfacial binding energy, substrate cohesion, temperature,

peeling mode, and edge defects influence the outcome of the exfoliation process. To model

polymer-assisted mechanical exfoliation, I used a finite-size system in which multilayer

graphene (MLG) is sandwiched between two thin polymer films. Leveraging the

spatiotemporal efficiency of the CG model, I performed fifty simulation iterations per

parameter set and analyzed the results from a …

Assessment Of H2s-Induced Cracking Susceptibility In Steam Line Pipes And Weld Zones During Geothermal Well Construction, Riene Kaelamanda Pragitta, Yudha Pratesa

Journal of Materials Exploration and Findings

The susceptibility of steam line pipes, especially in the HAZ (heat-affected zone) and weldment areas, to hydrogen sulfide in the geothermal industry is crucial to understand from the early stages, particularly during construction. The combination of tensile stress from residual stresses after welding and metallurgical phase transformation makes the joint areas vulnerable to sulfide stress cracking. This condition becomes even more extreme when the equipment operates during the well stimulation phase. This research assesses the severity of H₂S-induced cracking using NACE MR0175 and ISO 15156-1 standards, focusing on the effects of pH and partial pressure of H₂S (p_{H₂S …}

Spin Coater Design With Pid Algorithm Using Polynomial Regression Approach And Bias Tuning For Tio2 Deposition Process, Geo Surya Andika, Nofrijon Sofyan, Donanta Dhaneswara, Akhmad Herman Yuwono

Journal of Materials Exploration and Findings

The thin-film deposition technique using spin coating offers a cost-effective alternative to Chemical Vapor Deposition (CVD) and Physical Vapor Deposition (PVD). The spin-coating process requires precise control of the motor drive system to ensure that the rotational speed, measured in rotations per minute (RPM), aligns with the set point and remains stable. This study presents the design and development of a spin coater prototype to achieve uniform thin-film deposition. The control method employed utilizes a Proportional-Integral-Derivative (PID) algorithm, incorporating a polynomial approach with bias tuning. The PID control was chosen to achieve stable operation in a non-linear system. The performance …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

A Comparison And Investigation Of 2d Axisymmetric And 3d Models For The Wave Augmented Varicose Explosions Atomizer, Caleb Miller

Senior Honors Theses

Atomization of non-Newtonian, high-viscosity fluids is a challenging task with many applications. One method of atomizing these types of flows is the Wave Augmented Varicose Explosions (WAVE) atomizer. Using this framework, the study establishes an analysis methodology, investigates the relationship between 2D axisymmetric (2DA) and 3D models, and provides analysis of geometric, non-geometric, and non-dimensional parameter influences on the atomizer characterization in 2DA models. The study found that 2DA models can provide viable predictions of certain 3D model characteristics, established several mathematical models for parameter relationships among 2DA models, and identified certain metrics as inadequate predictors of atomizer characteristics in …

Advancing Solar Farm Resilience: Cfd-Driven Wind Load Optimization, Aly Mousaad Aly

Faculty Publications

This study investigates wind load design methods for ground-mounted solar panels and arrays by comparing Computational Fluid Dynamics (CFD) simulations with design standards. A case study of a solar farm impacted by Hurricane Maria examines the effects of elevation height, tilt angle, and variations in the American Society of Civil Engineers (ASCE) standards on wind loads and structural failure. Turbulence models, including Reynolds Stress Model, k–ε Model, and Large Eddy Simulation (LES), are used to analyze wind pressures, lift, drag, and peak pressures. Results show Phase 1 experienced higher wind loads than Phase 2 due to design differences. A cost-benefit …

A Formal Simulation Model For Discrete Rate Simulation, Thomas J. Tracey

Electrical & Computer Engineering Theses & Dissertations

Simulation is an essential tool for virtualizing systems by creating a representative model of real or hypothetical systems and observing how they change over time. Two predominant simulation paradigms include Discrete Event Simulation (DES) and Continuous Simulation, which both have their strengths and weaknesses. DES does not handle continuous state variables, while continuous simulation handles continuous state variables but encounters errors where these variables have discrete changes in their behavior. This difficulty between the two predominant simulation paradigms prompted the creation of a new simulation paradigm to cover this gap: Discrete Rate Simulation (DRS). DRS as a simulation paradigm focuses …

Investigating The Discrepancies Between Acoustic And Radar Measurements Of Water Level Using Data Analysis And Computational Modeling, Ohiedul Haque Mohammad Asad

Civil & Environmental Engineering Theses & Dissertations

Observations indicate that there is a discrepancy between water level measurements from NOAA’s acoustic gauges and radar gauges. NOAA’s acoustic gauges use a downward-looking acoustic sensor that measures the instantaneous distance from the sensor to the water level. To create a calm water surface for measurements, the sensor is placed in a PVC tube that penetrates the water surface to a certain depth. This tube is called the stilling well. In this thesis, discrepancies in water level measurements from acoustic and radar sensors were explored through data analysis and computational modeling. Specifically, the correlations between the error, which is defined …

Testing Autonomy: Hybrid Scenario Synthesis, Benjamin E. Hargis

Electrical & Computer Engineering Theses & Dissertations

Hybrid Scenario Synthesis merges static and adaptive techniques to generate interactions that rigorously assess autonomous performance under multi-factor testing. Multifactor scenarios employ multiple individual stimuli to rigorously test system responses in complex settings. Static Scenario Testing involves scripted test cases that simulate specific conditions or events. These scenarios represent typical situations an autonomous system might encounter. The benefits of static testing include early defect detection, focused review by trained experts, and efficiency. In multi-factor scenarios, however, statically defined scenario factors are not able to guarantee meaningful interactions as the presence of other factors may invalidate underlying assumptions regarding the system …

Unveiling The Transformative Power Of Unsupervised Machine Learning Through Clustering, Vishnu S. Pendyala

Open Educational Resources

Clustering methods demonstrated their transformative potential across various industries through image segmentation, anomaly detection, bioinformatics, and customer segmentation. The presentation explores these techniques in unsupervised machine learning, focusing on foundational clustering algorithms such as K-means, Hierarchical Clustering, and DBSCAN. Through an in-depth analysis of their underlying principles and computational intricacies, the presentation highlights how these methods have evolved to address complex, high-dimensional data problems. The presentation provides insights into how K-means remains a versatile tool for partitioning data in linear spaces. It delves into Hierarchical Clustering's unique approach to building dendrograms and capturing multi-scale data relationships, and how DBSCAN's density-based …

Enhancing Gas Cyclone Performance With Exit Diffusers, Omar Mohamed Mourad Dwidar, Yasser El-Shaer, Khairy Elsayed, Seyyed Hossein Hosseini, Goodarz Ahmadi

Journal of Engineering Research

This study evaluates seven cyclone designs to enhance pressure drop and collection efficiency (CE), using the conventional cyclone as a baseline. While the conventional design is efficient in particle collection, it experiences significant energy losses due to high values of pressure drops. This study introduces exit diffusers with various configurations—A, B, C, D, E, and F—to improve cyclone performance. Computational Fluid Dynamics (CFD) simulations were used to analyse the pressure drop, tangential velocity, and static pressure at different levels of each cyclone. The findings reveal that cyclone D achieves a 41.1% reduction in pressure drop but at the cost …

Collaborative Ai: Oer Materials For Exploring Ai As A Partner Rather Than A Tool, David Smith

Open Educational Resources

The Collaborative AI Open Educational Resource (OER) explores how artificial intelligence can act as a creative and analytical collaborator rather than a tool. Centered on the Balanced Blended Space (BBS) framework and the philosophy of the Center for Holistic Integration (CHI), the OER includes curriculum materials, theoretical models, and live research environments. It offers an interesting approach to blending physical, virtual, and conceptual spaces through shared human–AI agency and invites ongoing participation in interdisciplinary meta-projects.

Modelled Flooding Impacts On Lower Fish River Watershed, Sebastian Loschner

Shelby Hall Graduate Research Forum Posters

This study investigates the impacts of compound flooding in the Lower Fish River watershed, Baldwin County, Alabama, with a focus on the potential effects of sea level rise due to climate change. Coastal flooding, particularly in smaller watersheds, is a growing concern as it results from the interaction of multiple factors, including rainfall, tidal changes, and extreme weather events. Compound flooding, which involves multiple flood drivers, is expected to worsen with climate change, as increased precipitation and rising sea levels create heightened flood risks. However, existing research on compound flooding predominantly focuses on large-scale watersheds, leaving a knowledge gap in …

Evolution Of Lyrics Of Egyptian Songs In The 20th Century, Omar Abdelrafe

The Undergraduate Research Journal

Despite the extensive research by western countries on the song lyrics, almost nothing is known about the evolution of Egyptian song lyrics. The previous studies in the U.S. and U.K. revealed that lyrics were more pessimistic as time passed. In this study, 1891 Egyptian songs from 1920 to 2022 were analyzed using a sentiment analysis tool while searching for offensive words and retrieving their popularity scores from Spotify. The results aligned with those of the other regions in which the negativity of songs and the percentage of songs containing offensive words increased. However, there is a substantial increase in neutral …

Assessing Water Quantity And Quality In The Mississippi River Valley Alluvial Aquifer And Coastal Louisiana Through Integrated Airborne Electromagnetic And Borehole Data, Michael George Henin Attia Khalil

LSU Doctoral Dissertations

Numerical modeling has contributed significantly to the understanding of groundwater systems. Many challenges are associated with constructing groundwater models which include an accurate understanding of the geology and aquifer parameters estimation. Traditionally boreholes are a successful way to capture geological features, however, boreholes often have sparse data. Airborne electromagnetic (AEM) data allows for efficient and cost-effective surveying of large areas, providing valuable information about the subsurface electrical resistivity. By bridging the gap between boreholes, AEM data offers a broader view of the aquifer system's structure and heterogeneity. However, interpreting geophysical AEM data has uncertainties. Developing a framework to apply the …