Every year there are thousands of aviation accidents along with hundreds of human deaths that happen around the world. While the data is sparse, it is well documented that many of these happen from emergency landings gone awry. While pilots can generally make great landings in clear daytime conditions, they are significantly handicapped when it comes to landing at night or amongst poor visibility conditions. Due to the nature of this problem and some of the large scale advances in software technology we propose a solution that provides a significant improvement from the status quo. Using transfer learning on neural networks to classify satellite images along with terrain elevation data from the USGS we are able to recreate maps that can readily direct pilots to locations that are relatively flat and lack structures or vegetation. Using San Luis Obispo as our data set we confirmed that we could correctly classify at least 93\% of landable terrain and then identified areas within that area that could safely be used to land a plane. We then transfer this data into a 3D rendering program that allows us to visualize what is happening. In additional to visualizing where the landing paths are we also create a landing algorithm that demonstrates how the plane will maintain its current glide path and navigate to a successful landing while avoiding obstacles.

<p>With timeliness and efficiency being critical in the aviation maintenance industry, the need has been growing for smart technological solutions that help in optimizing and streamlining the different underlying tasks. One such task is the technical documentation of the performed maintenance operations. Instead of paper-based documentation, voice tools that transcribe spoken logbook entries allow technicians to document their work right away in a hands-free and time efficient manner. However, an accurate automatic speech recognition (ASR) model requires large training corpora, which are lacking in the domain of aviation maintenance. In addition, ASR models which are trained on huge corpora in standard English perform poorly in such a technical domain with non-standard terminology. Hence, this thesis investigates the extent to which fine-tuning an ASR model, pre-trained on standard English corpora, on limited in-domain data improves its recognition performance in the technical domain of aviation maintenance. The thesis presents a case study on one such pre-trained ASR model, wav2vec 2.0. Results show that fine-tuning the model on a limited anonymized dataset of maintenance logbook entries brings about a significant reduction in its error rates when tested on not only an anonymized in-domain dataset, but also a non-anonymized one. This suggests that any available aviation maintenance logbooks, even if anonymized for privacy, can be used to fine-tune general-purpose ASR models and enhance their in-domain performance. Lastly, an analysis on the influence of voice characteristics on model performance stresses the need for balanced datasets representative of the population of aviation maintenance technicians.</p>

Whereas commercial aviation is attempting to achieve the reduction of its substantial carbon footprint, general aviation’s (GA) climate change contribution is negligibly small, which is why the sector is facing other sustainability challenges mainly entailing the operation of dated technology and aircraft, increasing regulatory constraints, rising costs, noise emissions, and popular discontent, as well as remaining the last mobility sector in the world to still use leaded fuels. Throughout recent years, there have been remarkable sustainability trends in GA as well as heightened efforts to improve its emissions profile (noise, pollutants, CO2) and environmental reputation, for instance by the increased use of electric aircraft, especially for private pilot training. From a sociotechnical perspective, this mixed-methods study highlights current sustainability challenges and trends in GA as well as potential pathways towards more sustainable GA and private pilot training. Eight in-depth semi-structured interviews with Swiss and international GA stakeholders were complemented with a bilingual representative quantitative online survey (N=427) among Swiss GA stakeholders, a comparative CO2 analysis showing the emissions advantages and feasibility limits of supplementing private pilot training with lessons using electric aircraft, as well as participant observation. The data show that most Swiss GA stakeholders have increased environmental awareness and are concerned about sustainability and the environment both, in flight and other activities. Although the majority advocates for sustainable development in GA there are not one but many challenges and obstacles to a more sustainable GA. The largest challenges are the abatement of noise emissions and the facilitation of the leaded aviation gasoline (AVGAS 100LL) phaseout. The most pertinent obstacles towards sustainable GA innovation are said to be bureaucracy, overregulation and reluctance in the civil aviation authorities, high costs, averseness to risk and innovation, as well as a trend of decline in GA activity due to continuous demographic change. No single sustainability pathway but rather a mix of immediate and long-term sustainability measures was identified. Despite its current limitations, electric aviation proves to be one of the most feasible pathways to sustainable private pilot training. For more sustainable GA, the use of more fuel-efficient planes and available unleaded fuels, propeller, and muffler retrofits, as well as is feasible short- and midterm measures. In the long run, electric and hybrid aviation as well as bio- and synfuels are likely to become attractive options for GA. The study shows the importance of sustainable development in GA and private pilot training, not because it will majorly contribute to climate change mitigation, but because it will ensure the improvement of its negative environmental reputation and societal acceptance, which will be vital to ensuring the survival of the GA sector.

The General Aviation Airport Choice model is an attempt to model General Aviation (GA) travel patterns in the US in order to provide a means of assessing the impact of General Aviation activities on the National Air Space system. The model will also serve as part of transportation planning tool to help assess the viability of deploying NASA's Small Aircraft Transportation Systems (SATS) aircraft as a competitive mode of transportation for intercity travel. The General Aviation Airport Choice model developed estimates General Aviation (GA) person-trips and number of aircraft operations given trip demand in the form of GA person trips from counties. A pseudo-gravity model is embedded in the model to distribute the inter-county person-trips to a prescribed set of airports in the US. The airport-to-airport person-trips are split into person-trips by three aircraft modes (single, multi and jet engine) using an attractiveness factor based on average occupancy, utilization and a distance distribution factor for each aircraft type and the number of aircraft based at each airport. The person-trips by aircraft type are then converted to aircraft operations using occupancy factors for each aircraft type. The final output from the model are aircraft operations trip-tables by aircraft type between the airports in the model. The GA trips are estimated in order to provide a means of assessing the impact of GA activities on the National Airspace System. The model output may be used to assess the viability of GA aircraft serving as a competitive mode of transportation for intercity travel. / Master of Science

The Strategy Simulator project, funded by the Federal Aviation Administration (FAA), strives to find a tax structure that will support the National Airspace System (NAS) and maintain revenue neutrality, where taxes can be adjusted and the FAA can still attain the same revenue amount if taxes had not changed. Virginia Tech's role in the project is to analyze the effects of different tax structures on passenger demand. Virginia Tech focuses on ticket and segment taxes and runs different tax scenarios through the Transportation Systems Analysis Model (TSAM) and the TSAM Aggregation for the Strategy Simulator (TASS) model. TSAM provides a more microscopic analysis of demand by including spatial representation and mode choice in the model. TASS is a work in progress that aggregates the TSAM analysis in order to reduce computation time so that scenarios can be tested quickly. Based on data from literature review, TSAM results provides the smallest combined percent error for demand and revenue, followed by TASS, then the Strategy Simulator. TSAM and TASS also provide a detailed analysis of demand behavior in response to tax changes. In general, demand decreases as taxes increase, and demand increases over the years due to a fare scaling factor applied to reduce fares over the years. Revenue increases both over increasing taxes and over the years, indicating that increases in taxes does not harm revenue collection and actually increases revenues for the ticket and segment taxes tested. Revenue increases over the years because demand increases over the years, and the revenue generated from this increased demand more than makes up for decreased fares. / Master of Science

This paper was planned with the object of covering the history and development of air transportation from its incep- tion to the present time. The mythology concerning aviation has been taken up at the beginning and this has been followed by a discussion of the earliest experiments in flying. The activities of the Wright Brothers began about 1900 and in 1909 they sold the Wright Flyer" to the United States Govern- ment. The development of pre-war and wartime airplanes is next taken up. The World War was significant in airplane history. During this period airplanes were improved tremendously, because of necessity. Following this period, development of aviation ceased for a short time, due to an over abundance of airplanes left over from the wartime production. After this, the manufacture and improvement of airplanes began in earnest. The most recent records of various types of airplanes under different conditions are given, to show the improvements made;

No description available.

The replacement of lead additives in aviation fuels with non-leaded alternatives is of great interest to the aviation community. Ethanol, which may be derived from renewable resources, has been suggested as an octane-enhancing additive for general aviation fuel. Besides the consideration of the additive?s performance in meeting the requirements of aircraft engine combustion characteristics, there is also a concern about the material compatibility of ethanol fuels with respect to fuel system components consisting of metallic and polymer materials. This research project focuses on the effects of ethanol based fuels on the corrosion of metal components in fuel systems of general aviation aircraft. The types of metals that are studied include aluminum, brass and steel. Blends of gasoline that were tested were mixed with 10% to 85% ethanol by volume. The potential for corrosion to occur was determined through conventional weight loss methods and electrochemical measurements. By applying the principles of kinetics to the electrochemical reaction, the rate of corrosion was determined. Potentiodynamic polarization electrochemical techniques were used to determine the corrosion rates of metals exposed to various ethanol based fuels. Tafel extrapolation and linear polarization were correlated to corrosion rates. Impurities of water, chloride ion, and hydrogen ion were studied for their effect on the corrosion rate. Planned-interval tests were performed to investigate the change of the corrosive characteristics of metal and the change of the corrosiveness of the ethanol based fuels. Finally, DCI-11 from Octel-Starreon, was chosen to study the mechanism of commercial corrosion inhibitors. An observation was made that ethanol based fuels can create corrosion problems to some metal aircraft components. The inhibitor tested was effective in protection for fuel containing high percentages of ethanol. A correlation has been developed between observed metallic corrosion and fuel conductivity. The use of a corrosion inhibitor, conductivity measurements, and non-destructive component inspections should be incorporated in any future regulations intended to approve the use of ethanol based fuels in the existing fleet of general aviation aircraft.

No description available.

<p>  </p> <p>Aircraft noise is one of the most significant environmental concerns for the aviation industry, and it adversely affects the physical and mental health of community members who are in close proximity to airports. The operations and expansion of airports and land use planning are affected because of the community’s adverse reaction to such annoyances. Aircraft operations and fleet mix information are required when airport managers and stakeholders execute the Aviation Environmental Design Tool (AEDT) to compute the noise metrics; however, these data are unavailable from over 2,000 United States non-primary General Aviation (GA) airports that lack full-time air traffic control facilities or personnel. </p> <p>This study developed a low-cost noise assessment technology for non-towered GA airports. The Automatic Dependent Surveillance-Broadcast (ADS-B) messages were obtained using an inexpensive ADS-B receiver. A barometric pressure calibration was applied to improve the aircraft operations estimation. A fleet mix database was created by linking the collected ADS-B data to an FAA-registered aircraft database containing U.S.-registered aircraft information (such as types of aircraft and engines). Specific aircraft information was obtained by filtering the International Civil Aviation Organization (ICAO) identification code from the obtained ADS-B records. A set of 20 advanced aircraft performance parameters was constructed to determine the operation mode and corresponding power setting. The corresponding noise levels were determined using the EUROCONTROL Aircraft Noise and Performance (ANP) database.</p> <p>The testing and validation results from the case study at the Purdue University Airport (ICAO Code: KLAF) demonstrated the developed low-cost approach could identify aircraft noise events, and the accuracy of modeled noise data was assessed with an average error of 4.50 dBA. Therefore, the developed approach appears to be an affordable means of monitoring aircraft noise at non-towered GA airports.  </p>