Computational Engineering Commons^™

Optimization Of The Cross-Section Of Generic Flexible Bridges, Manal Kamal Zaki, Mina M. Helmy, Mark M. Tawadros

Journal of Engineering Research

This paper investigates the behavior of flexible bridges prone to aerodynamic instabilities due to wind. Optimal cross-sections that reduce aerodynamic forces are recommended. This is achieved by studying a variety of bridge sections with different widths of upper and lower sloping edges in addition to studying the upper and lower cramps of the deck. The wind velocities are also parameterized. Computations are developed based on the powerful computational fluid dynamics (CFD) method known as FLUENT and embedded in ANSYS software. For parametric study, a design of experiments (DOE) is performed based on the response surface methodology (RSM) combined with the …

An Integrated Approach To Enhance The Performance Of Rainfall Forecasting By Leveraging Stacking Based Machine Learning And Deep Learning Techniques, Umamaheswari P

Theses and Dissertations

Rainfall forecasting is critical for a variety of reasons, the most important of which is the substantial impact it has on many sectors of the community and the environment. It helps farmers with planting schedules, crop choices and irrigation techniques, all of which directly impact food production and agricultural yields. Rainfall forecasting is also vital in sectors such as hydroelectric power generation, since knowledge about water availability is essential for electricity generation. Accurate rainfall forecasts play very important roles in disaster planning and flood control. They enable authorities to take precautionary measures and, where necessary, plan for the evacuation of …

Deep Neural Network Models For Heatsink Performance Prediction And Optimization In Single Phase Immersion Cooling: Framework For Future Design Tools And Digital Twin Integration, Braxton J. Smith

Mechanical and Aerospace Engineering Theses - Archive

The rapidly rising computational power of modern computing components combined with the advanced packaging techniques being implemented has resulted in exponentially increasing thermal design powers (TDP) from CPUs and GPUs. Traditional air-cooling methods are approaching their effective cooling limits for many of these components, requiring lower supply air temperatures, higher supply air flowrates, and much larger heatsinks to remain feasible. Transitioning from air-cooling to single-phase immersion cooling offers numerous benefits in thermal performance, data-center size reduction, and energy efficiency. To leverage the merits of immersion cooling, the performance of a given heatsink must be predicted and optimized for best performance …

Virtual Environment Creation And Camera Calibration For Soft Target Identification And Assistance In Crowded Spaces With A Sensor Network And A Robotic Dog, Eltan Samoylov

Dissertations and Theses

Crowded places are increasingly targets of violence due to the increased accessibility and covertness of weapons, explosives, and other technology like drones. Addressing the challenges of protecting crowded places and assisting vulnerable individuals requires a multidisciplinary approach, taking inspiration from many different perspectives. Video surveillance of these crowded public facilities, such as train and bus stations, airports, shopping malls, and sports arenas, is very important to public safety, both for identifying threats/terrorist attacks and implementing evacuation plans.

The work of this thesis is part of a larger project aiming to explore the potential of using real-time computer vision and deep …

Methods To Improve The Computational Efficiency And Accuracy Of Second-Order Elastic Steel Frame Analyses, Nadine Faramawi

Electronic Theses and Dissertations

This paper investigates the derivation and performance of new stiffness coefficients. The estimated coefficients aimed to improve the geometric stiffness matrix representation and their application in elastic second-order analysis for steel frames. The newly developed (C1-C4) coefficients incorporate non-linear effects and reduce computational efforts to efficiently enhance the accuracy of second-order analysis. These coefficients are particularly beneficial for braced structures where they allow more refined analysis using fewer elements per member, especially as the load applied to the frame approaches the critical buckling load. However, for cases of unbraced frames, using these approximated coefficients showed no significant advantages in comparison …

Csc 22100 Software Development Laboratory Homework Assignment #1, Mitch Gershonowitz

Open Educational Resources

This is the first homework assignment for an Introduction to Java Programming course, which requires the students to write Java code using industry-accepted conventions. The assignment requires the student to write a Java method that accepts a date in an internationally-recognized format and returns a String object in Julian Date Format (JDF) with format YYDDD.

Csc 22100 Software Development Laboratory Homework Assignment #3, Mitch Gershonowitz

Open Educational Resources

This is the third homework assignment for an Introduction to Java Programming course, which requires the students to write Java code using industry-accepted conventions. The assignment requires the student to write a Java classes and interfaces that demonstrate usage of passing lambda expressions to methods, as well as demonstrating polymorphism and inheritance.

Direct Numerical Simulation Of Pulsating Flow In Curved Pipes: Insights Into Aortic Dissection In Humans, Gokul Anugrah Gopakumar

Theses and Dissertations--Mechanical and Aerospace Engineering

This dissertation investigates the hemodynamics of pulsating blood flow in curved vessels through direct numerical simulations (DNS), with the overarching aim of uncovering fluid dynamic mechanisms linked to the onset and progression of aortic dissection in humans. The work is structured in two parts: (i) fundamental studies in idealized geometries and (ii) preliminary investigations in patient-specific anatomies. In the first part, curved pipe models representing the aortic arch are used to isolate the effects of pulsation frequency, amplitude ratio, and curvature ratio on transitional and turbulent flow dynamics. The simulations reveal that curvature-driven centrifugal forces shift the peak velocity toward …

Cognitive Map Generation For Vision And Language Navigation, Alexander Sandoval Mesa

Dissertations and Theses

Visual-Language Navigation (VLN) presents significant challenges for autonomous agents, such as robots and virtual assistants, particularly in complex, dynamic environments where the seamless integration of visual perception and natural language understanding is critical. Traditional VLN systems often struggle with effectively aligning language instructions and visual scene understanding, limiting their adaptability and navigation efficiency.

This thesis proposes a novel Cognitive Map-based framework that addresses these challenges by transforming natural language navigation instructions into structured graph representations. The Cognitive Map consists of nodes representing waypoints, landmarks, decision points, and edges encoding spatial relationships and navigational actions. These maps are generated using Large …

Efficient And Test-Time Adaptive Visual Object Tracking In The Wild, Ram J. Zaveri

Graduate Theses, Dissertations, and Problem Reports (ETD)

Tracking a single object, given the location at the first frame, has been an ongoing challenge in the vision community for decades. Most recent approaches provide reasonably good performance, especially when benchmarked on in-distribution (ID) datasets, i.e., on the testing portion of the same datasets used for training. However, they incur high computational costs and hardware constraints, making their deployment in the wild for mobile, autonomous, and IoT applications still challenging. Efficient visual trackers address the efficiency aspect of such bottlenecks; however, they tend to overfit to their training distributions and lack generalization abilities, resulting in them performing well on …

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Mapping Of Product Sales Potential Based On Brands In The East Kalimantan Region Using Hybrid Analytical Framework, Achmad F O Gaffar Mr, Mulyanto Mulyanto Mr, Arief Bw Putra Mr, Muhammad Taufiq Sumadi Mr, Emmilya Umma Aziza Gaffar

Knowledge Engineering and Data Science

In geographically dispersed markets, operational costs should be reflected in sales planning to support accurate performance evaluation. However, such considerations are often neglected in practice. This study proposes a hybrid analytical framework to map brand-based product sales potential, with and without operational cost consideration, using historical sales data from PT Karya Inti Total Anugerah (PT KITA) in East Kalimantan. The framework integrates spatial, statistical, and machine learning techniques. Principal Component Analysis (PCA) is used to reduce the dimensionality of variables related to travel distance, total sales, and units sold, where travel distance represents the primary contributor to operational costs. K-Means …

Multiphysics Modeling Of Solid Oxide Fuel Cells For Gradient Minimization And Inductive Loop Analysis In Impedance Spectroscopy Using Machine Learning-Based Microstructural Property Estimation, Muhammad Usman Khan

College of Graduate Studies: Theses & Dissertations

Solid oxide fuel cells have significant advantages in renewable energy utilization due to their high efficiency, fuel flexibility, and low emissions. However, despite the numerous efforts of technology, thermal and current density gradients and impedance behavior fluctuations are still causing performance degradation. A combined computational framework that integrates machine learning and three-dimensional Multiphysics modeling is needed to investigate and optimize the performance of solid oxide fuel cells. A machine learning model, trained on synthetic microstructure data by percolation analysis, is used to predict important microstructural parameters like triple phase boundary density and geometric tortuosity. These are then employed in a …

Replicating The Functionality Of Ghost Knifefish Cerebellar Feedback Using Synthetic Nervous Systems, Sheldon Paul Cj Johnson

Graduate Theses, Dissertations, and Problem Reports (ETD)

Sensory inputs allow animals to perceive, react, and adapt to an environment. However, the sensory information received by the body, such as visual, auditory, and proprioceptive information, could become overwhelming, thus overloading the brain. Yet, animals can process all this information by canceling redundant signals from their surroundings, allowing them to be more sensitive to novel or unexpected signals in their environment. Each species (i.e., birds, fish, mammals) has its own way of using and filtering sensory information, from auditory to locomotion adaptivity. Cerebellar circuits contribute to sensory filtering in a variety of systems. In particular, research on Ghost knifefish …

Enhancing Risk And Crisis Communication With Computational Methods: A Systematic Literature Review, Madison H. Munro, Ross J. Gore, Christopher J. Lynch, Yvette D. Hastings, Ann Marie Reinhold

VMASC Publications

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational …

The Impact Of Skew On The Flexural Behavior Of Press-Brake-Formed Tub Girders, Matthew Allan Weatherholt

Graduate Theses, Dissertations, and Problem Reports (ETD)

The Short Span Steel Bridge Alliance (SSSBA) is a group of bridge and buried soil steel structure industry leaders who have joined together to provide educational information on the design and construction of short span steel bridges in installations up to 140 feet in length. Press-brake-formed-tub-girders (PBFTGs) were developed by a technical working group within the SSSBA in response to a rising demand for rapid infrastructure replacements for short span bridge applications. PBFTGs are produced from structural steel plate and can be finished as weathering steel or galvanized. After being cold bent to the appropriate shape, shear studs are welded …

Mitigating Information Overload In Aviation Safety: Ai-Driven Hierarchical Tagging And Summarization Of Notam For Pre-Flight Information Bulletin, Rutong Gu, Yefeng Qu, Dongling Chen, Yu Tang, Ailin Zhou

International Journal of Aviation, Aeronautics, and Aerospace

To address the challenges of aviation safety information overload in Pre-flight Information Bulletin (PIB) systems, this study proposes an intelligent classification framework (ERNIE-DPCNN) that integrates knowledge-enhanced semantic representation with a Deep Pyramid Convolutional Network. Traditional systems relying on rule-based filtering mechanisms suffer from inefficiencies in critical information identification and high risks of human misjudgment. The proposed framework achieves breakthroughs through three technical innovations: (1) An aviation domain-adapted ERNIE model is constructed, leveraging phrase-level masking strategies to enhance semantic representation of compound identifiers; (2) A Deep Pyramid Convolutional Network (DPCNN) is designed to extract multi-granularity features via hierarchical convolution-pooling architecture, optimized …

Hybrid Rans-Les Analysis Of Turbulent Flame Under Near Blow-Off Condition In A Multi-Stage Swirl Combustor, Brandon O'Brien

College of Graduate Studies: Theses & Dissertations

Advances in computational resources are making computational fluid dynamics (CFD) increasingly accessible for a variety of engineering applications. However, certain complex problems, such as simulating detailed flame kinetics, remain computationally prohibitive for many users. Developing cost-effective methods to simulate these challenges would greatly benefit applications involving flame dynamics, such as turbine engines or industrial burners. This study investigates the effect of main stage swirler intensity on near lean blow-off characteristics in a multi-staged swirl combustor using Ansys Fluent. The hybrid RANS-LES turbulence model, Stress Blended Eddy Simulation (SBES) coupled with Flamelet Generated Manifold (FGM) combustion model was selected to model …

Fast-Sparse-Spanner: A Practical Algorithm For Constructing Low-Stretch Sparse Geometric Graphs, Fnu Shariful

UNF Graduate Theses and Dissertations

When constructing geometric graphs (vertices are points and edges are line segments connecting point pairs) on pointsets, stretch-factor (worst-case detour between any point pair) is often considered a quality metric. A low stretch-factor (a quantity that is usually > 1) guarantees short paths between all vertex pairs. A geometric graph having a stretch-factor of t is known as a t-spanner. Creating low stretch-factor geometric graphs for large pointsets with a low number of edges is an open problem in computational geometry.

In this work, we have designed and engineered a new simple and practical (fast and memory-efficient) algorithm named Fast-Sparse-Spanner algorithm …

Solar Energy Prediction Using Advanced Hybrid Machine Learning Models, Jelawi A. Alqhtani

UNF Graduate Theses and Dissertations

Accurate short-term forecasting of solar power generation is critical for the reliable and cost-effective operation of renewable-based microgrids, where sudden weather-induced variability can compromise grid stability, battery scheduling, and energy trading decisions. Traditional physical and statistical models struggle to capture the complex non-linear relationships and localized weather effects, while individual deep learning architectures often exhibit systematic biases such as chronic under-prediction of peak generation. This thesis proposes a novel Cross-Feedback Ensemble framework that combines the complementary strengths of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1D-CNN) models through an iterative cross-feedback mechanism and a …