Engineering Commons^™

Integrated Experimental, Numerical, And Machine-Learning Framework For The Analysis Of Spray Dynamics In Diesel And Gasoline Direct-Injection (Gdi) Engines, Yassine El Marnissi

Theses and Dissertations

The regulation of diesel and gasoline direct injection (GDI) during cold starts has become subject to stricter numerical limits by both the Environmental Protection Agency (EPA) in the US and the European Commission (EC). For diesel engines, the EPA’s Clean Trucks Plan (Model Year 2027+) sets a certification NOₓ limit of 0.035 g/bhp-hr and an in-use fleet average limit of 0.050 g/bhp-hr, along with a PM limit of 0.005 g/bhp-hr, representing an 82.5% reduction in NOₓ compared to the former 0.2 g/bhp-hr standard. For gasoline engines, US standards regulate cold-start pollutants such as hydrocarbons (HC) ranging from 0.03 to 0.08 …

Dynamic Urban Air Mobility Systems Modeling And Simulation, Amer M. Ibrahim

Tennessee State University Alumni Theses and Dissertations

The use of electrical vertical takeoff and landing (eVTOL) aircraft to provide efficient, high-speed, on-demand air transportation within a metropolitan area is an increasingly popular topic, which is expected to bring fundamental changes to cities. The concept of Urban Air Mobility (UAM) has the potential to make meaningful door-to-door trip time savings compared with other transportation. The capability to manage many of these eVTOL aircraft safely in a congested urban area presents an unprecedented challenge in air traffic management. In order to enable safe and efficient autonomous on-demand free flight operations in UAM, a computational guidance algorithm with collision avoidance …

Pria: Process Risk Identification And Analysis, Raymond Fasano

Nuclear Engineering ETDs

This dissertation proposes and evaluates two approaches, with-in a Process Risk and Analysis (PRIA) framework, that advance data-driven hazard discovery in complex cyber-physical systems. First, a transferability-based clustering method groups subspace-identified segments into operational modes, prioritizing cross-predictive performance over raw feature proximity. Second, a Neural Switching Linear Dynamical System (NeuralSLDS) model anchors learning in identified state-space models with Bayesian residuals, calibrated predictive uncertainty, and context-aware operating mode transitions. Together, these methods generate physics-informed, probabilistic world models that can support model-in-the-loop analysis of hazardous trajectories. The approach is validated on a simulated point-kinetics reactor and a centrifugal chiller dataset, demonstrating coherent …

Conditional Generative Adversarial Network Framework For Iot Anomaly Detection, Henry Onyeka

Tennessee State University Alumni Theses and Dissertations

The growing scale and complexity of Internet-of-Things (IoT) edge networks complicate anomaly detection, particularly in identifying sophisticated Distributed Denial of Service (DDoS) attacks and zero-day behaviors under highly dynamic and imbalanced traffic conditions. This thesis proposes SD-CGAN, a Conditional Generative Adverserial Network optimzied with Sinkhorn Divergence as a geometry-aware one-class framework for robust IoT anomaly detection. SD-CGAN trains solely on benign traffic flows to learn a stable representation of normal traffic. To address class imbalance and improve the variety of the sample, we combine SD-CGAN with CTGAN-based synthetic data augmentation. Replacing the adversarial objective function with Sinkhorn Divergence yields smooth …

Warehouse Reconfiguration With Ats Lab, Ed Cantor, Dana Pazhouhesh, Mathew Oshinski, Samantha Sanchez

Senior Design Project For Engineers

The ATS Lab Warehouse Reconfiguration project is a collaborative effort between Kennesaw State University’s Department of Industrial and Systems Engineering and ATS Lab, located in Marietta, GA. The goal of this project is to redesign the current warehouse layout to enhance operational efficiency, reduce travel time, and implement sustainable inventory management practices, including 5S and Kanban. Aaron Roob, ATS Operations Manager, and Franklin Hungerford, Continuous Improvement Manager, led this initiative. Our team, “Sick Sigma’s,” is composed of Ed Cantor, our Project Manager, who is working as an intern at ATS during this process; Dana Pazhouhesh (Process Engineer); Matthew Oshinski (Quality …

Bilstm-Based Zero-Aware Traffic Forecasting Using Lidar Data For Intersection Safety, Md Atiqur Rahman Mallick

Tennessee State University Alumni Theses and Dissertations

LSTM retains information from long-term data and is highly effective in generating time-based forecasts. Recently, BiLSTM has enhanced the precision of traffic predictions by assimilating information from both antecedent and subsequent directions via bidirectional data flow. BiLSTM demonstrates exceptional efficacy in the analysis of long-term traffic data. Our study presents a custom forecasting model that leverages advanced LiDAR sensor technology in combination with state-of-the-art deep learning techniques to improve road conditions. Eight LiDAR sensors were strategically deployed along the 26th Clarksville Pike corridor to collect high-resolution traffic data—including vehicle speed and count/flow—over the period from September 1, 2024 to November …

Privacy-Preserving Data Sharing For Learnable Encryption Artificial Intelligence In E-Health, Al Amin

Tennessee State University Alumni Theses and Dissertations

Healthcare AI requires large datasets for clinical-grade performance, yet privacy regulations prevent direct inter-institutional data sharing. While traditional federated learning (FL) enables collaborative training without centralizing data, it suffers from four critical limitations: accuracy degradation, gradient leakage vulnerabilities, high communication costs, and limited interpretability. Accordingly, the overarching research goal is to design a privacy-first FL framework for e-health that preserves clinical-grade accuracy on obfuscated data while substantially reducing information leakage, minimizing communication overhead, and enabling interpretable decision support without sharing weights, gradients, or raw pixels. This thesis systematically addresses these challenges through a three-method framework that progressively enhances privacy, efficiency, …

Design And Development Of Rechargeable Cement-Based Batteries For Energy Storage Within Infrastructure, Dandan Yin

Tennessee State University Alumni Theses and Dissertations

This dissertation presented the design, fabrication, and evaluation of rechargeable cement-based batteries (CBB) for energy storage within infrastructure. The study aimed to integrate electrochemical energy storage directly into cementitious materials, enabling multifunctional and sustainable building systems. Nickel–iron electrodes and cement-based solid electrolytes to form durable, solid-state systems. The electrochemical impedance spectroscopy, cyclic voltammetry, and galvanostatic charge–discharge tests，along with microscopic testing and mechanical testing analyses, were conducted to assess battery performance. The carbon fiber mesh CBB achieved stable cycling over 100 cycles with quasi-reversible redox behavior and a maximum areal energy density of 7.6 Wh m⁻². Nickel foam electrodes, with their …

Power Resilience In Utah's Water Sector: A Survey Of Stakeholder Practices, Preferences And Potential, Tyler Christopher Peterson

Theses and Dissertations

Drinking water and wastewater services are vulnerable to disruptions because they rely heavily on the electric grid. As a result, power outages can lead to water outages, creating cascading failures that compromise sanitation, firefighting, healthcare, and daily routines. Although an important topic, there are little to no studies of water and wastewater power resilience at a geographic or state level. In this study we investigate the extent of existing backup power and renewable energy integration as well as the motivations and barriers around their power resilience. We collect our data through semi-structured interviews with staff at 19 urban and rural …

Climate-Driven Flood Risk Mapping And Adaptive Strategy Modelling For Coastal Tamil Nadu, Sakthi Kiran Dr Mr

Theses and Dissertations

Background: Tamil Nadu’s coastal corridor (Cuddalore–Sirkazhi; ~2,740 km²) experiences recurring floods driven by climate change, rapid urban expansion, and inadequate drainage across low-lying deltaic terrain. Existing flood assessments remain largely reactive and rely on static methods that fail to incorporate future climate projections, demographic growth, and evolving urban patterns.

Methods: This research develops a dynamic, climate-resilient flood risk framework integrating (i) long-term hydroclimatic trend analysis (1984–2023), (ii) satellite-based flood hazard mapping using multi-temporal Sentinel-1A and RISAT-1A SAR, (iii) CORDEX REMO2015 (RCP 4.5)–driven HEC-RAS 2D hydrodynamic modelling, (iv) machine-learning-based susceptibility modelling (RF, DT, SVM), and (v) scenario-based evaluation of structural mitigation …

Mechanical Quality Of Hemp Fiber As Influenced By Tillage, Cover Crop, And Nitrogen Management In Regenerative Organic Systems, Parinaz Heydar, Alyssa Pierce, Gabriella Fioravanti, Ronald Kander

School of Design and Engineering Papers

Industrial hemp (Cannabis sativa L.) is an emerging crop for renewable fiber materials. For farmers, finding a balance between agronomic performance and economic return is crucial, especially when targeting specific markets like the textile industry, which values not just fiber quantity, but overall quality. This field study, conducted at the Rodale Institute in Kutztown, Pennsylvania, assessed the effects of tillage (till vs. no till), cover crop (with cover vs. no cover), and nitrogen (N) rate (0, 50, 100, 150 kg ha⁻¹) on hemp fiber yield, N concentrations in leaf and stalk, and mechanical performance under regenerative organic conditions. Fiber mechanical …

Investigation Of The Flow Field Morphology Of Film Cooling In Supersonic Flow, Umberto Sandri, Massimiliano Ferro, Alessio Picchi, Antonio Andreini, Bruno Facchini, Marc D. Polanka

Faculty Publications

Film cooling is widely implemented in highly thermally stressed gas turbine components. Its performance has been extensively investigated for several decades and many results are available in the literature. In conventional gas turbines, regions of supersonic flow are not prevalent and should generally be avoided. For this reason, results relative to film cooling in supersonic flow are limited. Nevertheless, a new interest related to Rotating Detonation Combustors (RDC) and supersonic turbines is growing. The implementation of those engine components in a gas turbine is likely to need film cooling for thermal protection. In this context, it becomes crucial to gain …

In-Situ Investigations Of Calcium-Magnesium-Aluminosilicates (Cmas) Infiltration Effects On Thermal Barrier Coatings Under Extreme Environments Replicating Jet Engines, Zachary Stein

Doctoral Dissertations and Master's Theses

Calcium-magnesium-aluminosilicate (CMAS) particulates, such as sand or volcanic ash, are ingested by gas turbine jet engines during operation. When operating within high abundance regions, these particulates negatively interact and degrade the high temperature thermal barrier coatings (TBC) protecting underlying superalloy turbine blades vital to engine operation. These CMAS particulates melt within the engine and infiltrate into the ceramic coatings. In electron-beam physical vapor deposited (EB-PVD) TBCs, the CMAS within the intercolumnar gaps stiffens the coatings and causes thermomechanical induced high stress concentrations, risking crack formation. While molten, the CMAS thermochemically alters and destabilizes the coating, also risking coating failure. Premature, …

Parameter Informed Reinforcement Learning For Vehicle System Identification, Nathan Schaff

Doctoral Dissertations and Master's Theses

Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control …

Influence Of Build Plate Location On Tensile Properties And Residual Stresses In Lpbf-Fabricated Stainless Steel 316 Components, Yareli Zelaya Pavón

Honors Program Theses and Research Projects

Additive manufacturing (AM), particularly Laser Powder Bed Fusion (LPBF), enables rapid production of complex metal components with minimal waste. Stainless Steel 316 (SS316) is widely used in engineering applications due to its corrosion resistance and mechanical reliability. This study investigates the influence of build plate location on tensile properties and residual stresses in LPBF-fabricated SS316. Twelve tensile specimens and nine unsupported bridge structures were fabricated at different build plate positions using a “machine model.” Tensile testing provided stress-strain data to evaluate peak stress, elastic modulus, and strain at break, while bridge curvature measurements quantified residual stress through warping. Results indicate …

The Analysis And Design Of A Circular Waveguide Coated With A Magnetized Ferrite, Hassan Ali Ragheb, Mariam Hossam

Electrical Engineering

Abstract: This paper investigates the cut-off frequency of a perfectly conducting circular waveguide coated with an externally magnetized ferrite shell. Wave equations in both the ferrite shell and the core inside the cylinder are solved resulting in a Fourier series expansions involving Bessel and trigonometric functions with unknown coefficients. The influence of the magnetization current on the permeability tensor is considered in the analysis. By applying boundary conditions, four characteristic equations are derived, and their vanishing determinants define the cut-off frequencies of the structure. A MATLAB program is developed to numerically solve the derived equations. Results are presented for various …

Experimental And Theoretical Characterization Of Molten Salt Thermal Conductivity, Jacob C. Numbers

Theses and Dissertations

Molten salts form an ideal heat transfer fluid for advanced energy generation systems, including molten salt reactors (MSR). Accurate characterization of molten salt thermal conductivity is needed to confidently predict transient thermal behavior and optimize salt properties. Measurements are hindered by salts' challenging material properties, which---in the case of the needle probe device---induced unaccounted physical changes that limited its accuracy and repeatability. Sparse thermal conductivity data in general is exacerbated by a limited fundamental understanding of molecular energy transfer in liquids. This work extends the experimental capacity for compositional studies using the needle probe and proposes a theoretical model for …

Mechanical, Durability, And Environmental Performance Of Limestone Powder-Modified Ultra-High-Performance Concrete, Yashovardhan Sharma, Meghana Yeluri, Srinivas Allena

Civil and Environmental Engineering Faculty Publications

Ultra-high-performance concrete (UHPC) delivers outstanding durability and strength but typically relies on high Portland cement content. This study evaluates a 20% cement replacement with limestone powder (LP) in UHPC and benchmarks performance under two curing regimes: moist curing (MC) and warm bath curing at 90 degrees C (WB). Metrics include workability, compressive and flexural behavior, shrinkage, freeze-thaw resistance, chloride transport (surface resistivity, RCPT), material cost, and embodied CO2. LP improved fresh behavior: flow increased by 14.3% in plain UHPC and 33% in fiber-reinforced UHPC (FR-UHPC). Compressive strengths remained in the UHPC range at 28-56 days (approximately 142-152 MPa with LP), …

Development And Validation Of 2nn-Meam Interatomic Potential For Sc And Al-Sc Alloys Thermodynamics Solidification And Intermetallic Ordering, Avik Mahata

Mechanical Engineering Faculty Publications

We present a second-nearest-neighbor Modified Embedded Atom Method (2NN–MEAM) potential for Scandium (Sc) and Aluminum-Scandium (Al–Sc) alloys that unifies cohesive, thermodynamic, and solidification behavior within a single transferable framework. The Sc component accurately reproduces cohesive energy, lattice constants, defect energetics, and the experimental melting point obtained from two-phase coexistence, demonstrating reliable description of both hcp and liquid phases. The Al–Sc binary interaction parameters were fitted using the L1₂–Al₃Sc reference and benchmarked against first-principles and calorimetric data. The potential reproduces the strong negative formation enthalpy of Al₃Sc (–0.45 eV atom⁻¹), correct relative stability …

Patent Searching With Uspto, Derwent Innovation And Lens.Org, Ibis Anette Moreno-Lozano

Day Family Research Lab Workshop Series

No abstract provided.

High-Resolution Lidar Observations Of Sedimentation-Induced Size Sorting Of Droplets Near A Laboratory Cloud Top, Fan Yang, Yong Meng Sua, Zipei Zheng, Jesse Anderson, Hamed F. Sadi, Jae Min Yeom, Suryadev Pratap Singh, Pei Hou, Will Cantrell, Ernie Lewis, Alex Kostinski, Raymond Shaw

Michigan Tech Publications

No abstract provided.

Advancing Sustainable Co2 Mitigation: Experimental And Computational Analysis Of Thermal Carbon Chitosan Sorbent For Automotive Exhaust Capture, Dalia A. Ali Dr., Amir Ahmed Elgamal Eng., Rania Rushdy Moussa Dr.

Chemical Engineering

This study investigated the efficiency of thermal carbon chitosan (TCCS) sorbent for CO2 capture from vehicle exhaust emissions within a designed adsorption system. TCCS was synthesized and meticulously characterized using a series of analytical techniques, including Brunauer-Emmett-Teller (BET) surface area analysis, Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Ther- mogravimetric Analysis (TGA), Energy Dispersive X-ray Spectroscopy (EDX), and Differential Scanning Calo- rimetry (DSC). The TCCS adsorbent showed high thermal stability and a heating value (HHV) of 23.5 MJ/kg. Adsorption isotherm study demonstrated that the maximum capacity of CO2 adsorption is 0.084 kg.CO2/kg. TCCS, as well …

Time Series Modeling Of Land Use And Land Cover Classification Using Remote Sensing, Ahmed A. F. Abd El Hamed, Fawzi H. Zarzoura, Mahmoud El-Mewafi

Mansoura Engineering Journal

Understanding land use / land cover changes is essential for planning sustainable development. It represents the transformation of the land due to human activities, which is related to sustainable development that aims to achieve the needs of the present generations without affecting the future. Land use / Land cover is studied in Al Alamein region, northern Egypt, with supervised classification methods using satellite images from Landsat 8 and Sentinel - 2A in 2023 and analyzes the accuracy assessment, which shows Maximum likelihood is better in both sensors achieving overall accuracy (91.80% and 91.02%) and kappa coefficient (0.814 and 0.8799). Then …

Nonlinear Excitation Control Of Multimachine Systems Via The Invariant-Set Design, Ehab Bayoumi

Mechanical Engineering

Power grids are inherently vulnerable to many uncertainties. All power networks are prone to instability because of the uncertainties inherent in the operation of power systems. Rotor-angle instability is a challenging issue, and if not properly managed, could give rise to cascading failures and even blackouts. This paper addresses the generator excitation system’s state feedback sliding mode control (SMC). The global system is divided into multiple subsystems to achieve decentralized control. A disturbance is defined as the influence of the system as a whole on a specific subsystem. The state-feedback controller is to be designed taking into account the disturbance …

Improving Road Safety Through Multimodal Deep Learning For Driver Drowsiness Detection, Hadel A. Hussain, Mohammed A. Subhi, Ahmed S. Al Tmeme, Ahmed D. Radhi, Marwan Ali Albahar

Iraqi Journal for Computer Science and Mathematics

One of the most common causes of road accidents globally is driver drowsiness and it needs solutions that are reliable and can be applicable in numerous real-life situations. We present this paper with the aim of developing a deep-learning system that is capable of reliably detecting drowsiness in diverse and varied conditions across different drivers, environments, and sensor types. Our system is known as Multimodal Attention Network (MMAN), which combines information of eye and head movement, heart-rate and breathing pattern, and vehicle-dynamics signal. MMAN has a gradient-reversal layer that enables the layer to be domain-adaptive such that it does not …

Deep Learning-Based Fog-Cloud Approach Intrusion Detection System In Iomt, Yahya Rbah, Mohammed Mahfoudi, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime Elbekkali

Iraqi Journal for Computer Science and Mathematics

The Internet of Medical Things (IoMT) creates an interconnected environment linking humans, devices, sensors, and systems, enhancing healthcare services through advanced technologies. Nonetheless, these IoMT devices are susceptible to cyberattacks, which can endanger patient safety and healthcare services. To identify and mitigate cyberattacks in IoMT, techniques such as threat intelligence, log monitoring, and intrusion detection systems are employed. As attackers evolve their strategies, there is a growing trend towards leveraging artificial intelligence to achieve more predictive and accurate attack detection. Since IoMT devices are inherently low-power, they require minimal computing resources. Existing intrusion detection systems are generally trained in the …

Multitaskvenationnet: A Multi-Task Deep Neural Network With Strip Pooling And Hybrid Upsampling For Leaf Vein Segmentation, Ishak Ariawan, Ahmad Ashari, Moh. Edi Wibowo

Iraqi Journal for Computer Science and Mathematics

Leaf vein segmentation is a critical task in plant phenotyping and species classification, yet it remains challenging due to the hierarchical, curvilinear nature of veins and interference from complex backgrounds. Existing methods face three key limitations. First, they lack directional context modeling, leading to blurred vein boundaries and the omission of fine venation. Second, they fail to effectively capture global dependencies, limiting semantic coherence across spatial regions. Third, they do not incorporate explicit mechanisms for detecting vein discontinuities, which is essential for complete topological understanding. To address these challenges, we propose MultiTaskVenationNet (MTV-Net), a multi-task deep segmentation framework that integrates …

Performance Of Adaptive Wingtips On A Tailless Supersonic Business Jet, Khushi Piparava

2025 Fall Honors Capstones Projects - Archive

The growing interest in supersonic business travel has renewed focus on efficient, low-drag configurations that can achieve high performance while maintaining stability and structural integrity. This research investigates the aerodynamic and structural implications of adaptive folding wingtips on a tailless supersonic business jet (SBJ) concept designed under the SkyBreaker senior design program. Using a conceptual aerodynamic model based on linearized supersonic theory, the Polhamus leading-edge suction analogy, and an empirical compression-lift correlation, the study evaluates how varying wingtip droop angles influence lift, drag, lift-to-drag ratio (L/D), and static stability. The analysis was performed parametrically in MATLAB, using geometry inputs derived …

A Review Of Bioactive Compounds Of Caesalpinia Sappan: Pre- And Post-Harvest Effect, Annisa Lugina Rachman, Santi Rosniawaty, Syariful Mubarok, Cucu Suherman, Yudithia Maxiselly

Jurnal Kultivasi

Sappanwood (Caesalpinia sappan L.) is a shrub or small tree that thrives in tropical regions and has been widely distributed across various areas. It has long been used as a natural dye in textiles, cosmetics, and herbal beverages due to its content of various bioactive compounds. This literature review discusses the secondary metabolites in sappanwood, their health benefits, and strategies to enhance product quality through pre-harvest and post-harvest treatments. This literature review was conducted by searching for relevant journals on Google Scholar using relevant keywords. Sappanwood contains diverse secondary metabolites, including flavonoids, phenolics, anthraquinones, triterpenoids, steroids, alkaloids, and tannins, with …

Analytical Assessment Of Pedestrian Crashes On Low-Speed Corridors, Therezia Matongo, Deo Chimba

Civil and Architectural Engineering Faculty Research

This study presents a comprehensive statewide analysis of pedestrian-involved crashes recorded in Tennessee between 2002 and 2025. We evaluated the influence of roadway, traffic, environmental, and socioeconomic factors on pedestrian crash frequency and severity with substantial components focused on lighting impacts including dark and nighttime. A multi-method analytical framework was implemented, combining descriptive statistics, non-parametric tests, regression analysis, and advanced machine learning techniques including the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the gradient boosting model (XGBoost). Results indicated that dark and nighttime conditions accounted for a disproportionate share of severe crashes—fatal and serious injuries under dark conditions reached over 40%, …