Spatial disorientation is the singular most common factor in human-error aviation accidents, and over ninety percent of those accidents are fatal. Despite advances in aviation over the past one hundred years in both technology and training, spatial disorientation mishaps continue at a steady pace, even though other incidents declining in frequency. Because spatial disorientation is a highly complex phenomena that involves the vestibular system, the visual system, and cognitive factors such as workload and attention, predicting spatial disorientation is extremely difficult. Likewise, exactly replicating spatial disorientation for training purposes is challenging as well as extremely dangerous and costly. The goal of this study was twofold: to understand if innate abilities can predict propensity for spatial disorientation, and to investigate the efficacy of using story-based vignettes – narratives – to train spatial disorientation to increase schematic learning in pilots. Results demonstrated that performance on a spatial orientation task such as the Direction Orientation Task (DOT) is not a reliable predictor for spatial disorientation recognition based on self-report spatial disorientation frequency. In addition, though story-based vignettes demonstrated potential for increased cue recognition over a control training event, significant differences were not found in novel spatial disorientation recognition, critical cue identification, or confidence. These findings indicate that spatial disorientation could be a completely perceptual (bottom-up) task rather than one that is both top-down and bottom-up and implies future research into the ways we describe and measure spatial disorientation in order to understand it as well as train for it.

Unmanned Aerial System (UAS) is a versatile and essential tool for law enforcement, first responders, utility providers, and the general public. Integrating the UAS into the National Airspace System (NAS) poses a significant challenge to policymakers and manufacturers. A UAS Integration Safety and Security Technology Ontology (ISSTO) has been developed in the Web Ontology Language (OWL) to aid in this integration. ISSTO is a domain ontology covering aviation topics corresponding to flights, aircraft types, manufacturers, temporal/spatial, waivers and authorizations, track data, NAS facilities, air traffic control advisories, weather phenomena, surveillance and security equipment, and events, sensor types, radio frequency ranges, actions, and outcomes. As ISSTO is a domain ontology, it models the current state of UAS integration into the NAS and provides a comprehensive view of every aspect of UAS.

In recent years, the rapid advancement of Unmanned Aircraft Systems (UAS) has led to an increasingly complex National Airspace System (NAS), necessitating a comprehensive understanding of factors that impact pilot visual acquisition and detection of other aircraft (including manned fixed-wing, rotorcraft, and UAS). The objective of this study is to investigate factors that affect pilot performance in visually acquiring and detecting other manned-fixed wing aircraft and manned rotorcraft using a multi-method approach, incorporating qualitative and quantitative data analysis. A diverse sample of pilots with varying flight experience participated in the study. Participants were exposed to a series of flight test scenarios in a high-fidelity flight test campaign using different flight paths and detecting different types of aircraft, designed to replicate real-world airspace encounters with other aircraft. Post-flight interviews were conducted, and situational awareness questionnaires and NASA Task Load Index (NASA-TLX) were administered to capture insights on the pilots’ experiences. The goal was to determine the level at which aircraft characteristics, test subjects’ situational awareness and workload, flight conditions, and environmental conditions influenced visual acquisition and detection. All interviews were subjected to several cycles of meticulous coding and subcoding processes to discern both individual and co-occurring factors affecting visual detection capabilities. Additionally, a rigorous statistical analysis was executed on the data derived from the situational awareness questionnaires and NASA-TLX to extract quantitative insights into pilot-centric metrics influencing visual detection. The amalgamated results from both the qualitative and quantitative analyses were synthesized to construct a comprehensive representation of all variables influencing visual detection, in addition to delineating the parallels between factors that affect visual acquisition in both manned fixed-wing and rotorcraft detection scenarios.

This dissertation develops systems engineering and machine learning models for aviation maintenance support. With the constant increase in demand for air travel, aviation organizations compete to maintain airworthy aircraft to ensure the safety of passengers. Given the importance of aircraft safety, the aviation sector constantly needs technologies to enhance the maintenance experience, ensure system safety, and limit aircraft downtime. Based on the current literature, the aviation maintenance sector still relies on outdated technologies to maintain aircraft maintenance documentation, including paper-based technical orders. Aviation maintenance documentation contains a mixture of structured and unstructured technical text, mainly inputted by operators, making them prone to error, misunderstanding communication, and inconsistency. This dissertation intends to develop decision support models based on systems engineering and artificial intelligence models that can automate the maintenance documentation system, extract useful information from maintenance work orders, and predict the aircraft's top degrader signals based on textual data. The first chapter of this dissertation introduces the significant setbacks of the aviation industry and provides a working ground for the following chapters. The dissertation's second chapter develops a system engineering framework using model-based systems engineering (MBSE) methodology to model the aviation maintenance process using the systems engineering language (SysML). The outcome of this framework is the design of an automated maintenance system model that can be used to automate maintenance documentation, making it less prone to error. The third chapter of the dissertation uses textual data in maintenance work orders to develop a hybrid approach that uses natural language processing (NLP) and transformer models to predict the readiness of a legacy aircraft. The model was tested using a real-life case study of the EA-6B military aircraft. The fourth chapter of this dissertation develops an ensemble transformer model based on three different transformer models. The ensemble model leverages the benefits of three different transformer architectures and is used to classify events based on an aviation log-based dataset. This dissertation's final and fifth chapter summarizes key findings, proposes future work directions, and provides the dissertation's limitations.

Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not an easy task. Pilots look for different indicators: color variation on the ground because the difference in the amount of heat absorbed by the ground varies based on the color/composition, birds circling in an area gaining lift, and certain types of cloud formations (cumulus clouds). The above methods are not always reliable enough and pilots study the weather for thermals by estimating solar heating of the ground using cloud cover and time of year and the lapse rate and dew point of the troposphere. In this paper, we present a Machine Learning based solution for assisting in forecasting thermals. We created a custom dataset using flight data recorded and uploaded to public databases by soaring pilots. We determine where and when the pilot encountered thermals to pull weather and satellite images corresponding to the location and time of the flight. Using this dataset we train an algorithm to automatically predict the location of thermals given as input the current weather conditions and terrain information obtained from Google Earth Engine and thermal regions encountered as truth labels. We were able to converge very well on the training and validation set, proving our method with around a 0.98 F1 score. These results indicate success in creating a custom dataset and a powerful neural network with the necessity of bolstering our custom dataset.

The primary objective of this research is to formulate a methodology of assessing the maximum impact loading condition that will incur onto an aircraft’s landing gear system via Finite Element Analysis (FEA) and appropriately determining its corresponding structural and impact responses to minimize potential design failures during hard landing (abnormal impact) and shock absorption testing. Both static and dynamic loading condition were closely analyzed, compared, and derived through the Federal Aviation Administration’s (FAA) airworthiness regulations and empirical testing data. In this research, a nonlinear transient dynamic analysis is developed and established via NASTRAN advanced nonlinear finite element model (FEM) to simulate the worst-case loading condition. Under the appropriate loading analysis, the eye-bar and contact patch region theory were then utilized to simulate the tire and nose wheel interface more accurately. The open geometry of the nose landing gear was also optimized to minimize the effect of stress concentration. The result of this research is conformed to the FAA’s regulations and bound to have an impact on the design and development of small and large aircraft’s landing gear for both near and distant future.