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Celebrate IEW at Student Employment
No full textHead over to Student Employment for crafts, treat boxes, and an opportunity to enter a raffle prize! Take a break, take in a bit of culture, and maybe get a prize
Evaluating Kansai Airport\u27s PPP: Grounded Ambitions, Concession of Financial, Geotechnical, and Climate Risk
No full textThis study critically examines the Kansai Airports Group’s 44-year Public-Private Partnership (PPP) monopoly-type concession encompassing Kansai International (KIX), Osaka Itami (ITM), and Kobe (UKB) airports. The research uses financial analysis, Monte Carlo simulation, and NPV-at-risk modeling to evaluate how Japan’s fixed-term private monopoly model allocates financial, operational, and environmental risks. In 2015, there were significant developments in the promotion of the Private Finance Initiative (PFI) Act. It was revised in 2015; as a result, it finds that the underestimated long-term maintenance liabilities, especially Kansai airport (KIX) foundation subsidence, and lacked flexibility to manage exogenous shocks like COVID-19 and Typhoon Jebi. Comparative insights from various global models reveal structural weaknesses in Japan’s legal and institutional frameworks. The study also identifies investment disincentives linked to unclear terminal asset reversion and overly rigid performance monitoring. Drawing on international best practices, the research recommends embedding dynamic risk forecasting tools and renegotiation triggers to create more resilient, adaptive infrastructure concessions in Japan’s aviation sector
Monte Carlo Simulation Models to Enhance Air Cargo Network Resilience and Sustainability
No full textThis dissertation focuses on developing and validating an empirical set of simulation models for an air cargo network under large-scale disruption to test recovery strategies that improve resilience. Understanding and quantifying network resilience and recovery strategies is critical for ensuring sustainable operations. These operations become essential during disruptive events as air cargo is used to transport time-sensitive and critical goods. The collective research steps result in a Monte Carlo simulation framework to evaluate air cargo network resilience and identify strategies that enhance performance during disruptions.
The modeled system is a hub-and-spoke network centered on Memphis (MEM) with 23 domestic airports representing the geographic and operational diversity of the U.S. air cargo system. The key model inputs are scheduled flights and airport capacities paired with Gamma distributions (capacity variability) and Poisson processes (arriving and departing flight disruption). The distributions were calibrated to empirical data so that network behavior drives simulation outcomes. The resulting key performance metrics are realized flights, cargo throughput, and normalized performance at airport and network levels. For comparative analysis, the cargo throughput difference suggests a more practical metric than an airport or network resilience score. Of the three, the most comprehensive metric is realized flights, and it is the basis for performance and cargo throughput calculations. The models make theoretical contributions to the body of knowledge by developing a model measuring air cargo network resilience performance, offering a validated tool for strategy testing and evaluation, and providing methodological rigor for resilience analysis under uncertainty. This work also provides a method for translating the stochastic dynamics of air cargo networks into a usable model.
Three recovery strategies were selected and incorporated into the models to test their effectiveness against disruption. The strategies focused on the resource layer of the network: substitution, scalability, and repurposing. Substitution used varying levels of aircraft payload capacity, scalability increased realized flights, and repurposing incorporated the use of commercial aircraft for cargo purposes. The results showed that all three can provide varying levels of improvement for cargo throughput if the strategies are incorporated during lower levels of disruption (e.g., 25 to 50% disruption). Of the three recovery strategies, scaling the number of arriving and departing flights yields the greatest improvement for an air cargo network under disruption. The second was substituting aircraft for a larger payload capacity, and the third was repurposing commercial aircraft. Practically, these findings demonstrated that a structured approach to capacity and contingency planning can influence network resilience. These findings can also be translated into actionable playbooks for capacity management, fleet allocation, and surge planning. Ultimately, this method provides a means for operators to safeguard operational continuity by sustaining throughput and service during disruptions and accelerating time to recovery
Preparing Tomorrow’s Professionals: Industry-Informed AI Integration
No full textAs AI reshapes operations across aviation and aerospace, organizations are investing in ways to preserve data integrity, safeguard proprietary knowledge, and uphold critical professional competencies. This presentation shares emerging findings from a study that surveys and interviews industry professionals about their use of AI tools, their concerns about misuse, and the importance of secure, enterprise-controlled “walled garden” environments. The work explores how employers define appropriate, effective, and innovative AI adoption, particularly in roles requiring high-stakes decision-making, compliance, and technical acumen.
By analyzing organizational expectations around AI-related knowledge, skills, and abilities (KSAs), this research offers practical guidance for academic programs seeking to prepare students and early-career professionals for evolving operational realities. Connections will be made to recent research on AI in education, with implications for updating curricula, reinforcing ethical use frameworks, and fostering closer collaboration between academia and industry. This session supports the summit’s theme by highlighting how we can collectively pioneer the future of AI across technical domains in aviation and aerospace
RFID Security for IoT Airport Infrastructure
Get PDFRadio Frequency Identification (RFID), a methodology that uses active and passive tags to communicate through radio signals at different frequencies, has made its way to different implementations, including tracking of merchandise in transit, inventory management, access restrictions to authorized personnel, different data-sharing applications embedded with Near Field Communication (NFC). This technology has been mainly used in airports for tracking people and tracking luggage. In the tracking people line, was found that the main targets are the employees; to allow them to enter specific locations in the airport and give them permissions based on the role/hierarchy they have in the company and for passenger tracking and authentication in airports. In the line of luggage tracking, with the increasing number of incidents of missing or misplaced luggage, our focus in the following work will be on the implementation of a testbed of an Io RFID tracking system for luggage. This proposed project has 4 stages (objectives): 1. Prototyping and testing of RFID IoT infrastructure. 2. Generate a vulnerability assessment for the RFID IoT infrastructure. 3. Simulate cyberattacks and test the likelihood of compromise of the system’s integrity and availability. 4. Propose a series of recommendations for securing RFID IoT Systems that meet the characteristics of the systems evaluated in this research
Engineering Anisotropic Porosity in Green Parts from Binder Jet 3D Printing
Get PDFBinder Jet Additive Manufacturing (BJAM) is a promising metal additive manufacturing technique that enables the fabrication of complex geometries without the need for support structures. However, the inherent porosity in green parts, remains a challenge in achieving desired mechanical properties and performance. Instead of treating porosity as a limitation, this thesis explores engineering of anisotropic porosity engineering, where controlled variations in porosity in the green parts can be used to enhance functionality and efficiency in specific applications. The objective of this research is to investigate and analyze the key print parameters that influence green part density. By systematically varying layer thickness, binder saturation, and powder bed characteristics, experimental results reveal that layer thickness has the most significant impact on porosity. To complement the experimental work, a Discrete Element Method model is developed to simulate the single-layer powder bed formation process in BJAM. This computational approach provides insights into how powder particle interactions and binder infiltration contribute to porosity formation. The DEM results are correlated with experimental data to enhance process predictability and establish a framework for optimizing porosity control in BJAM
Mitigating Information Overload in Aviation Safety: AI-Driven Hierarchical Tagging and Summarization of NOTAM for Pre-flight Information Bulletin
Get PDFTo 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 with residual connections for long-text gradient propagation; (3) The AdamW optimizer is introduced to dynamically adjust learning rates, improving model convergence efficiency. Evaluated on real-world NOTAM records from airlines, the framework achieves a weighted F1-score of 98.8% in binary classification (Flight Advisory/Restriction) and 91.5% in multi fine-grained classification, outperforming baseline models such as ERNIE-CNN and ERNIELSTM. Ablation studies demonstrated the effectiveness of the domain adaptive masking strategy and dynamic learning rate mechanism. The framework provides an interpretable and scalable technical approach for aviation safety information processing, and its hierarchical feature extraction mechanism facilitates the subsequent simplified deployment scenarios of PIB
A Conceptual Framework to Bird Monitoring and Dispersion with Drones for Aviation Safety
Get PDFDrones have become increasingly popular in the past decade as their usage and applications have recently expanded to commercial and research. One such example is wildlife monitoring. Their autonomous capabilities have also improved with technological advancements. On the commercial aviation side, bird strikes are becoming an increasing safety concern with increased traffic. With improvements in drone technology, there is a possible future of drone integration in airports to improve safety. In this project, a conceptual framework for a new application for autonomous drones monitoring and dispersing birds in commercial aviation safety will be proposed, addressing the needs for this technology, describing the theoretical design, discussing challenges, and presenting further data opportunities for this technology. This proactive approach will show that this method of bird mitigation would be more beneficial in controlling bird strike hazards. Lastly, preliminary results based on the conceptual framework will be presented as a guide for further investigation
Commercial Aircraft Wing Structural Design for a High Aspect Ratio
Get PDFThis project focuses on the structural design of a wing optimized for a significantly larger aspect ratio, aimed at enhancing the efficiency and sustainability of commercial aircraft while ensuring structural integrity and fatigue resistance. As this project focused on the wing structure design, the internal components needed to be sized appropriately to sustain the loading experienced. Some of the important internal components sized and designed included spar caps, webs, stringers, ribs, and fuselage attachment brackets. Along with sizing, fatigue life analysis was completed to ensure the risk of structural failure was reduced over the lifespan of our aircraft. The designed wing structure supports the higher aspect ratio along with the resulting higher stresses and bending moments while maintaining necessary safety margins. This design will undergo Damage Tolerance Analysis and be certified according to FAA structural regulations. The structural design of the wing demonstrates a practical approach capable of balancing the new aspect ratio with the increased shear forces and bending moments, wing weight, and proper damage/fatigue analysis