Hosted by the Graduate Student Association (GSA) at North Carolina Agricultural and Technical State University, the 3rd Annual Graduate Research Symposium was held on April 1, 2025, in the Deese Ballroom of the Student Center. With the theme "Empowering Research, Driving Innovation", this symposium showcased the scholarly contributions of graduate students across a range of academic disciplines.
The event featured poster presentations in five categories: Completed Research, Ongoing Research, Research Proposals, Literature Reviews, and Innovative Methods. Graduate students from diverse fields—Engineering, Science and Technology, Agriculture and Environmental Sciences, Education, and more—shared their research through in-person poster sessions.
Awards were presented for Best in Category, Best Visual Design, Participant’s Choice, People’s Choice, and Best Communicator. All accepted abstracts are published in this official Book of Abstracts, which has been archived to celebrate the academic excellence and innovation demonstrated by NC A&T graduate scholars. Only select posters are included in this collection, based on student permissions.
This event was part of the university's Graduate and Professional Student Appreciation Week and reflects NC A&T’s commitment to fostering interdisciplinary collaboration, professional development, and research excellence.
-
Quinone-Based Cathodes: Enhancing Magnesium-Ion Batteries with Efficient Synthetic Strategies
Imoleayo Olorunyolemi and Aleksandrs Prokofjevs
As the demand for advanced energy storage solutions grows, the limitations of lithium-ion batteries (LIBs) including resource scarcity, safety concerns, and electrochemical constraints highlight the need for alternative battery technologies. Magnesium-ion batteries (MIBs) offer a promising solution due to their abundance, high volumetric capacity, and improved safety. However, challenges such as slow ion diffusion and cathode-electrolyte incompatibility require the development of optimized cathode materials. This study explores quinone-based organic cathodes, focusing on pyrene-4,5,9,10-tetraone (PTO), a polycyclic aromatic hydrocarbon with multi-electron redox activity and a high theoretical capacity (409 mAh g⁻¹). We introduce an efficient and cost-effective synthetic route for 1,3,6,8-tetrabromo- 4,5,9,10-pyrenetetraone (TBPT) and 1,2,3,6,7,8-hexabromo-4,5,9,10-pyrenetetraone (HBPT), intermediates for quinone-based metal-organic frameworks (MOFs) in MIB cathodes. Unlike conventional routes using expensive and hazardous oxidants such as sodium periodate (NaIO₄) and ruthenium trichloride (RuCl₃·xH₂O), our method employs readily available reagents, improving cost-efficiency, accessibility, and scalability. By leveraging these optimized synthetic strategies, this work aims to enhance the electrochemical performance, cycle stability, and practical viability of magnesium-ion batteries, contributing to developing next-generation energy storage technologies.
-
Impact of Collaborative Food Rescue Initiatives on Food Waste Reduction
Ricky Owusu, Shona Morgan Ph.D., and Sadan Kulturel-Konak Ph.D.
In the current food systems landscape, the concept of food rescue has become a powerful force due to concerns about waste and its effects on the environment and society. This paper explores the collaborative efforts of various stakeholders, such as retail donors, food banks, for-profit organizations, and waste management entities, in addressing the complex challenges of food security, sustainability, and waste reduction. Through a comprehensive framework, surplus food is rescued from various stages of the supply chain and redirected towards meaningful purposes, mitigating the environmental impact of food waste. However, despite these efforts, the global paradox of food waste alongside increasing food insecurity continues to exist. This study examines the dynamics of how food is allocated among stakeholders through Monte Carlo simulation in order to identify disparities and optimize the use of resources. By addressing disparities and improving collaboration, the paper aims to enhance the efficiency and sustainability of food rescue initiatives, ultimately reducing food waste and improving societal well-being. Key research questions explore the impact of for- profit food rescue on non-profit organizations. The Monte Carlo simulation results show that competition between food banks and for-profit organizations significantly reduces food waste, with for-profits acting as a crucial buffer to absorb surplus supply.
-
Reliability of Hardware/ Edge ML Accelerators.
Victor Oyadongh
This study evaluates the reliability of hardware accelerators for edge machine learning applications by examining how computing architectures and model optimizations affect system robustness under fault conditions. It begins with a detailed review of the literature surrounding the vulnerability of current acccelerator architectures namely; GPU, DSP, NPU. It then investigates whether platform-specific vulnerabilities lead to differences in user- visible errors during fault injection experiments by focusing on two distinct platforms — a Raspberry Pi 4 augmented with a Coral Edge TPU and a Jetson Xavier utilizing GPU acceleration. Additionally, the study compares the impact of optimization frameworks (TensorFlow Lite versus TensorRT) and assesses the relative resilience of specialized computational units (GPU, DSP, and NPU) in common ML operations such as matrix multiplication and convolution. Using controlled fault injection techniques with tools like Tensor-FI and BFA, ML applications are deployed on both platforms to analyze error propagation and performance degradation. The resulting comparative analysis aims to identify the most reliable hardware accelerator and computing model configuration for edge ML deployments, providing valuable insights for designing robust and resilient edge computing systems.
-
Reliability of Hardware/ Edge ML Accelerators.
Victor Oyadongh
This study evaluates the reliability of hardware accelerators for edge machine learning applications by examining how computing architectures and model optimizations affect system robustness under fault conditions. It begins with a detailed review of the literature surrounding the vulnerability of current acccelerator architectures namely; GPU, DSP, NPU. It then investigates whether platform-specific vulnerabilities lead to differences in user- visible errors during fault injection experiments by focusing on two distinct platforms — a Raspberry Pi 4 augmented with a Coral Edge TPU and a Jetson Xavier utilizing GPU acceleration. Additionally, the study compares the impact of optimization frameworks (TensorFlow Lite versus TensorRT) and assesses the relative resilience of specialized computational units (GPU, DSP, and NPU) in common ML operations such as matrix multiplication and convolution. Using controlled fault injection techniques with tools like Tensor-FI and BFA, ML applications are deployed on both platforms to analyze error propagation and performance degradation. The resulting comparative analysis aims to identify the most reliable hardware accelerator and computing model configuration for edge ML deployments, providing valuable insights for designing robust and resilient edge computing systems.
-
A Pipeline for Immersive Data Visualization for CAVE System
Abhinav Pendem and Koundinya Challa Ph.D.
Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi- dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making.
-
A Pipeline for Immersive Data Visualization for CAVE System
Abhinav Pendem and Koundinya Challa Ph.D.
Virtual Reality (VR) has transformed data visualization by enabling immersive and interactive experiences. CAVE (Cave Automatic Virtual Environment) systems provide a unique platform for exploring complex datasets in an intuitive, spatially immersive manner. However, a standardized pipeline for developing VR scripts tailored to CAVE-based data visualization remains underexplored. This paper presents a structured pipeline for creating and deploying immersive data visualization in CAVE systems, specifically using the WorldViz Prism CAVE environment. Our approach facilitates seamless integration of multi- dimensional datasets, enabling real-time interaction with 27 visual graphs within a single VR scene. The system allows dynamic graph manipulation, interactive exploration, and enhanced user engagement. By streamlining VR script development, this pipeline improves accessibility for researchers and practitioners, fostering a more intuitive understanding of complex datasets. Experimental results demonstrate the effectiveness of this approach in enhancing data interpretability and decision-making.
-
Engineering Quantum Point Defects in 2D Transition Metal Dichalcogenides for Next Generation Quantum Devices
Sean Persaud
Quantum Information Science and Engineering (QISE) seeks to develop next generation quantum technologies by leveraging superposition, entanglement, and coherence. This research aims to engineer quantum point defects in wideband gap monolayer transition metal dichalcogenides (TMDs), particularly Molybdenum Disulfide (MoS₂), to create stable, optically active spin defects for quantum sensing. The primary research question explores how atomic-scale defects in MoS₂ can be designed to function as coherent quantum emitters. We hypothesize that specific substitutional and vacancy-based defects will exhibit long spin coherence times and strong optical transitions, making them suitable for solid state quantum technologies. The objectives include computational modeling of defect states, controlled fabrication of defects, and advanced characterization to validate their quantum properties. Density functional theory (DFT) and quantum defect embedding theory (QDET) will be employed to predict defect stability, charge transition levels, and spin coherence. Experimentally, monolayer MoS₂ will be synthesized via chemical vapor deposition (CVD), with defects introduced using scanning tunneling microscopy (STM). Optical and spin properties will be assessed using photoluminescence spectroscopy, magnetic resonance techniques, and scanning tunneling spectroscopy. This research will provide critical insights into defect-host interactions in 2D materials, advancing scalable quantum sensing platforms
-
Engineering Quantum Point Defects in 2D Transition Metal Dichalcogenides for Next Generation Quantum Devices
Sean Persaud
Quantum Information Science and Engineering (QISE) seeks to develop next generation quantum technologies by leveraging superposition, entanglement, and coherence. This research aims to engineer quantum point defects in wideband gap monolayer transition metal dichalcogenides (TMDs), particularly Molybdenum Disulfide (MoS₂), to create stable, optically active spin defects for quantum sensing. The primary research question explores how atomic-scale defects in MoS₂ can be designed to function as coherent quantum emitters. We hypothesize that specific substitutional and vacancy-based defects will exhibit long spin coherence times and strong optical transitions, making them suitable for solid state quantum technologies. The objectives include computational modeling of defect states, controlled fabrication of defects, and advanced characterization to validate their quantum properties. Density functional theory (DFT) and quantum defect embedding theory (QDET) will be employed to predict defect stability, charge transition levels, and spin coherence. Experimentally, monolayer MoS₂ will be synthesized via chemical vapor deposition (CVD), with defects introduced using scanning tunneling microscopy (STM). Optical and spin properties will be assessed using photoluminescence spectroscopy, magnetic resonance techniques, and scanning tunneling spectroscopy. This research will provide critical insights into defect-host interactions in 2D materials, advancing scalable quantum sensing platforms
-
Safe Remote Operation of Connected Automated Vehicles
Tienake Phuapaiboon and Ali Karimoddini
Over the past few years, automated shared ride services have been increasingly tested on public roads, with several pilot projects taking place in North Carolina. Although these ride- sharing services can navigate structured traffic environments effectively, the autonomous systems remain prone to challenges in complex or atypical scenarios. Should the autonomous driving system fail, it may lead the vehicles to crash or stall, creating potential hazards and congestion on the roadways. This study introduces a human-in-the-loop fallback operation through the establishment of a remote-operated driving system. The proposed approach develops a reliable software and communication architecture to facilitate connectivity between Connected Autonomous Vehicles (CAVs) and the remote-operated driving station. The ROS2 framework and WebSocket protocol grant access to vehicle perception and control, ensuring robust communication with the remote driving control system. Additionally, the research outlines guidelines for the fallback procedure and safety measures to enhance the overall reliability of remotely operated CAVs.
-
Integrating Personalized Incentives for Enhanced Transportation System Management
David Quansah, Ridwan Tiamiyu, and Kayla Stevens
As the issue of urbanization accelerates, transportation networks face growing congestion challenges, leading to increased travel delays, environmental impacts, and infrastructure strain. Traditional Transportation System Management (TSM) strategies focus on optimizing traffic flow and enhancing infrastructure efficiency, but demand-side approaches—such as behavior incentives—offer promising alternatives. This research explores the integration of personalized incentives (such as monetary rewards, tokens, and gamification) within TSM to encourage off-peak travel and multimodal transportation choices. Preliminary hypotheses suggest that tailored incentives will significantly improve compliance and reduce peak-hour congestion. Furthermore, we hypothesize that integrating dynamic, user-specific incentives with an existing multimodal trip planner can significantly improve traffic distribution. To test this, we are designing behavioral experiments and developing simulation models to assess user responses. The experiments take into consideration both rational and non- rational user behaviors in decision making. Additionally, we present a survey of existing congestion mitigation strategies to inform our approach. The findings aim to inform the development of adaptive, user-centered solutions for reducing urban congestion while enhancing mobility and sustainability.
-
Integrating Personalized Incentives for Enhanced Transportation System Management
David Quansah, Ridwan Tiamiyu, and Kayla Stevens
As the issue of urbanization accelerates, transportation networks face growing congestion challenges, leading to increased travel delays, environmental impacts, and infrastructure strain. Traditional Transportation System Management (TSM) strategies focus on optimizing traffic flow and enhancing infrastructure efficiency, but demand-side approaches—such as behavior incentives—offer promising alternatives. This research explores the integration of personalized incentives (such as monetary rewards, tokens, and gamification) within TSM to encourage off-peak travel and multimodal transportation choices. Preliminary hypotheses suggest that tailored incentives will significantly improve compliance and reduce peak-hour congestion. Furthermore, we hypothesize that integrating dynamic, user-specific incentives with an existing multimodal trip planner can significantly improve traffic distribution. To test this, we are designing behavioral experiments and developing simulation models to assess user responses. The experiments take into consideration both rational and non- rational user behaviors in decision making. Additionally, we present a survey of existing congestion mitigation strategies to inform our approach. The findings aim to inform the development of adaptive, user-centered solutions for reducing urban congestion while enhancing mobility and sustainability.
-
Role of CD163+ Macrophages in Organic Dust Exposure: A Study on Porcine Blood and Lungs
Jenny Pakhrin Rana, Kristen L. Foust, Rohit S. Ranabhat, and Jenora T. Waterman
CD163, a scavenger surface receptor of alveolar macrophages (AMs), facilitates pathogen recognition, however, its specific role under organic dust (OD) exposure in swine confinement remains unclear. We hypothesized that continuous OD exposure within swine barns activates CD163+ AMs and promotes their proinflammatory responses. To investigate our hypothesis, macrophages were isolated from porcine bronchoalveolar lavage fluid (BALF) and blood, exposed to fluorescent-FITC-beads and stained with CD163 and CD14 markers. Supernatants and cells were analyzed for oxidative stress, inflammation, and phagocytic activity via immunoassays and flow cytometry. Data were analyzed using Two- way ANOVA (p-value < 0.05) Results elevated CD14+CD163+ expression with increasing phagocytic activity in indoor AMs. However, CD14+CD163+ expression was higher in blood macrophages of outdoor versus indoor pigs. Bead uptake by BALF AMs of indoor pigs produced higher nitric oxide and TNF-α levels. Conversely, interleukin (IL)-1β and IL-6 production was higher in BALF AMs of outdoor pigs. This study suggests exposure to OD in indoor environments may increase phagocytic activity by CD163+ macrophages, oxidative stress, and inflammatory markers, which potentially contributes to the pathogenesis of chronic bronchitis among agricultural workers. Future studies could focus on these implications for humans.
-
Role of CD163+ Macrophages in Organic Dust Exposure: A Study on Porcine Blood and Lungs
Jenny Pakhrin Rana, Kristen L. Foust, Rohit S. Ranabhat, and Jenora T. Waterman
CD163, a scavenger surface receptor of alveolar macrophages (AMs), facilitates pathogen recognition, however, its specific role under organic dust (OD) exposure in swine confinement remains unclear. We hypothesized that continuous OD exposure within swine barns activates CD163+ AMs and promotes their proinflammatory responses. To investigate our hypothesis, macrophages were isolated from porcine bronchoalveolar lavage fluid (BALF) and blood, exposed to fluorescent-FITC-beads and stained with CD163 and CD14 markers. Supernatants and cells were analyzed for oxidative stress, inflammation, and phagocytic activity via immunoassays and flow cytometry. Data were analyzed using Two- way ANOVA (p-value <0.05) Results elevated CD14+CD163+ expression with increasing phagocytic activity in indoor AMs. However, CD14+CD163+ expression was higher in blood macrophages of outdoor versus indoor pigs. Bead uptake by BALF AMs of indoor pigs produced higher nitric oxide and TNF-α levels. Conversely, interleukin (IL)-1β and IL-6 production was higher in BALF AMs of outdoor pigs. This study suggests exposure to OD in indoor environments may increase phagocytic activity by CD163+ macrophages, oxidative stress, and inflammatory markers, which potentially contributes to the pathogenesis of chronic bronchitis among agricultural workers. Future studies could focus on these implications for humans.
-
Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
Shaghayegh Shajarian
Modern network management is increasingly complex, requiring administrators to handle vast amounts of log data from diverse sources, leading to inefficiencies, errors, and operational challenges. In this work, we propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and human- in-the-loop process to automate network management tasks such as log analysis, troubleshooting recommendations, and documentation generation. This study aims to enhance network reliability, reduce operational complexity, and move forward to autonomous network management.
-
Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
Shaghayegh Shajarian
Modern network management is increasingly complex, requiring administrators to handle vast amounts of log data from diverse sources, leading to inefficiencies, errors, and operational challenges. In this work, we propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and human- in-the-loop process to automate network management tasks such as log analysis, troubleshooting recommendations, and documentation generation. This study aims to enhance network reliability, reduce operational complexity, and move forward to autonomous network management.
-
Predicting and Optimizing the Fair Allocation of Donations in Hunger Relief Supply Chains
Nowshin Sharmile, Isaac A. Nuamah, Lauren Davis, and Funda Samanlioglu
Non-profit hunger relief organizations rely on donors’ benevolence to combat food insecurity. However, fluctuations in donation quantity and frequency create challenges in ensuring equitable distribution. A hierarchical forecasting methodology is developed to predict monthly food donations across a multi warehouse food aid network. These forecasts are integrated into an optimization framework to guide fair allocation of supplies while accounting for supply chain coordination and flexibility. The approach highlights under- served regions within the network and provides actionable insights to reduce disparities between overserved and underserved counties, ultimately improving food access equity.
-
Effect of Different Irrigation Levels on the Growth and Yield of Tomatoes
Olabisi Somefun
Effective water management practices are essential for maximizing tomato yield while mitigating the risks associated with weather extremes and ensuring environmental integrity. The need for climate-smart irrigation management techniques in agriculture has increased to optimize water use efficiency and enhance crop productivity. Irrigation scheduling using precision agriculture technologies like soil moisture sensors is an effective and efficient water management strategy in crop production. Therefore, this study evaluated the effects of different irrigation levels on the performance of tomatoes. Irrigation levels were based on initiating irrigation when soil matric potential was at 15 kPa and 45 kPa. A control treatment was included based on the crop’s condition. The experiment was laid out in a randomized complete block design (RCBD) in raised beds on campus during the 2024 summer growing season, with data collected on agronomic and soil moisture content. The data generated in this study will be used to develop best management practices and guidelines on climate-smart irrigation management in North Carolina and similar regions. These guidelines will serve as recommendations for farmers to enhance irrigation and nutrient use efficiencies in vegetable cropping systems. Additionally, the study will increase the adoption of scientific irrigation scheduling using plant and soil moisture sensors
-
Effect of Different Irrigation Levels on the Growth and Yield of Tomatoes
Olabisi Somefun
Effective water management practices are essential for maximizing tomato yield while mitigating the risks associated with weather extremes and ensuring environmental integrity. The need for climate-smart irrigation management techniques in agriculture has increased to optimize water use efficiency and enhance crop productivity. Irrigation scheduling using precision agriculture technologies like soil moisture sensors is an effective and efficient water management strategy in crop production. Therefore, this study evaluated the effects of different irrigation levels on the performance of tomatoes. Irrigation levels were based on initiating irrigation when soil matric potential was at 15 kPa and 45 kPa. A control treatment was included based on the crop’s condition. The experiment was laid out in a randomized complete block design (RCBD) in raised beds on campus during the 2024 summer growing season, with data collected on agronomic and soil moisture content. The data generated in this study will be used to develop best management practices and guidelines on climate-smart irrigation management in North Carolina and similar regions. These guidelines will serve as recommendations for farmers to enhance irrigation and nutrient use efficiencies in vegetable cropping systems. Additionally, the study will increase the adoption of scientific irrigation scheduling using plant and soil moisture sensors
-
A Narrative Analysis of Black Women C-suite Leader's Career Success in STEM
Tiffani Teachey
This qualitative narrative study investigates the experiences of Black women who have successfully advanced to C-suite leadership positions in STEM organizations. Grounded in Social Cognitive Career Theory and Intersectionality Theory, it explores how intersecting identities, environmental factors, and personal strategies shape their career trajectories. The research aims to identify effective strategies, support systems, and organizational practices that facilitate their career advancement. By examining these narratives, this study seeks to inform organizational practices and policies that promote more inclusive leadership pathways for Black women in STEM, ultimately enhancing representation in these fields.
-
A Narrative Analysis of Black Women C-suite Leader's Career Success in STEM
Tiffani Teachey
This qualitative narrative study investigates the experiences of Black women who have successfully advanced to C-suite leadership positions in STEM organizations. Grounded in Social Cognitive Career Theory and Intersectionality Theory, it explores how intersecting identities, environmental factors, and personal strategies shape their career trajectories. The research aims to identify effective strategies, support systems, and organizational practices that facilitate their career advancement. By examining these narratives, this study seeks to inform organizational practices and policies that promote more inclusive leadership pathways for Black women in STEM, ultimately enhancing representation in these fields.
-
Enhancing Cargo Movement Automation: ROSBased Design and Control of an Autonomous Overhead Crane with Depth Camera Object Detection
Amanuel Tereda
The automation of cargo movement is crucial in industries like nuclear power plants, where precision, safety, and efficiency are essential. This research focuses on developing an autonomous overhead crane system integrated with the Robot Operating System (ROS) to enhance material handling in such high-risk environments. A comprehensive geometric analysis using SolidWorks optimizes the crane’s structure, while an advanced control mechanism ensures seamless coordination of its X, Y, and Z-axis movements along with the gripper operation. A depth camera is incorporated to detect and locate objects, allowing the system to dynamically adjust its gripping mechanism based on the object's position. The crane’s pulley system is modeled as a mass-damped second-order system, with a PID controller ensuring stable and efficient performance. Unlike existing studies that focus primarily on hardware improvements or basic automation, this research introduces a unified ROS-based control framework for real-time motion planning, precise motor actuation, and adaptive object handling. Additionally, a digital twin of the overhead crane is developed to simulate real-world dynamics accurately. The proposed system is validated through extensive simulations in SolidWorks and ROS, demonstrating improved accuracy, reliability, and scalability. This work significantly contributes to advancing autonomous material handling solutions for nuclear facility crane automation
-
Predicting Effects of Conservation Practices on Runoff, Sediment, and Nutrient Loads from a Commercial Cotton Field Using Machine Learning and Deep Learning
Arjun Thapa, Niroj Aryal, and Michele L. Reba
Estimating real-time sources of pollutants and evaluating the effectiveness of conservation practices in agriculture are crucial for prevention of water resources contamination. Pollutant load released from agricultural fields can be estimated using process-based model or data driven models. This study utilized machine learning (ML) models to predict water pollutant loads since they need fewer input features than process-based models. Hydro-meteorological data (e.g., temperature, rainfall, runoff) were collected from control and treatment fields (2016–2022), where cover crops and filter strips were used for pollution mitigation. Pollutant loads, including sediment, total phosphorus (TP), and total nitrogen (TN), were measured and used to train nine ML models: Multiple Linear Regression (MLR), K-Nearest Neighbors (KNN), Random Forest (RF), Extreme Gradient Boosting (XGB), Histogram Gradient Boosting (HGBt), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid CNN-LSTM model. Results showed that the hybrid model best-predicted runoff in the control field (R²=0.87) and KNN in the treatment field (R²=0.82). LSTM excelled in sediment prediction for both fields, while RF and ANN were superior for TP and TN predictions, respectively. Model performance declined from runoff to sediment to nutrient loads due to error propagation. Advanced models (e.g., LSTM, CNN, hybrid) outperformed conventional ML models, showing robustness.
-
High-Performance Journal Shaft Design for Extreme Altitudes
Yashitha Thuraka and John Kizito
Developing robust mechanical components for high-stress environments is a significant challenge in the aerospace industry. Traditional journal shafts struggle with structural integrity and lubrication at elevated rotational speeds and altitudes. Our solution introduces a self-pumping journal shaft with a taper, designed to enhance lubrication retention and minimize friction during highspeed operations. The taper ensures a precise fit, improving alignment and reducing slippage, crucial at altitudes around 50,000 feet. By incorporating fluid physics principles, the design optimizes hydrodynamic flow within the shaft, ensuring continuous lubrication and efficient bearing performance at high RPMs. The self-pumping feature leverages fluid dynamics to maintain lubrication, reduce wear, extend the lifespan of mechanical systems, and minimize downtime. Our research emphasizes the importance of lubrication and alignment for enhancing durability and efficiency in journal shafts. This innovation addresses the specific challenges of high altitudes and high rotational speeds, making it especially impactful for aerospace applications. Enhancing lubrication and bearing efficiency, our advanced journal shaft design improves aircraft system reliability and efficiency, sets new industry standards, and offers significant economic benefits to the multibillion-dollar aerospace sector.
-
High-Performance Journal Shaft Design for Extreme Altitudes
Yashitha Thuraka and John Kizito
Developing robust mechanical components for high-stress environments is a significant challenge in the aerospace industry. Traditional journal shafts struggle with structural integrity and lubrication at elevated rotational speeds and altitudes. Our solution introduces a self-pumping journal shaft with a taper, designed to enhance lubrication retention and minimize friction during highspeed operations. The taper ensures a precise fit, improving alignment and reducing slippage, crucial at altitudes around 50,000 feet. By incorporating fluid physics principles, the design optimizes hydrodynamic flow within the shaft, ensuring continuous lubrication and efficient bearing performance at high RPMs. The self-pumping feature leverages fluid dynamics to maintain lubrication, reduce wear, extend the lifespan of mechanical systems, and minimize downtime. Our research emphasizes the importance of lubrication and alignment for enhancing durability and efficiency in journal shafts. This innovation addresses the specific challenges of high altitudes and high rotational speeds, making it especially impactful for aerospace applications. Enhancing lubrication and bearing efficiency, our advanced journal shaft design improves aircraft system reliability and efficiency, sets new industry standards, and offers significant economic benefits to the multibillion-dollar aerospace sector.
-
Turbine Based Combined Cycle Exoskeletal Engine (TBCC-ESE) Compressor Blade Design, FEA, & Material Selection
Joshua Tucker, Mookesh Dhanasar Ph.D, Ajit Kelkar, and Kenroy Stewart
The Turbine Based Combined Cycle Exoskeletal Engine concept presents a creative approach to achieving high Mach number flight, starting from ground speed. Key design aspects that move away from traditional jet engine designs is having all the rotating turbomachinery mounted on rotating drums / discs, thereby removing the need for a central shaft. This research addressed the initial compressor blade design, FEA and material selection. These areas play a critical role in developing the low-speed engine of the TBCC ESE.