Results for “aviation” — Page 3

NASA NTRS2013

Prospective Safety Analysis and the Complex Aviation System

Fatal accident rates in commercial passenger aviation are at historic lows yet have plateaued and are not showing evidence of further safety advances. Modern aircraft accidents reflect both historic causal factors and new unexpected "Black Swan" events. The ever-increasing complexity of the aviation system, along with its associated technology and organizational relationships, provides fertile ground for fresh problems. It is important to take a proactive approach to aviation safety by working to identify novel causation mechanisms for future aviation accidents before they happen. Progress has been made in using of historic data to identify the telltale signals preceding aviation accidents and incidents, using the large repositories of discrete and continuous data on aircraft and air traffic control performance and information reported by front-line personnel. Nevertheless, the aviation community is increasingly embracing predictive approaches to aviation safety. The "prospective workshop" early assessment tool described in this paper represents an approach toward this prospective mindset-one that attempts to identify the future vectors of aviation and asks the question: "What haven't we considered in our current safety assessments?" New causation mechanisms threatening aviation safety will arise in the future because new (or revised) systems and procedures will have to be used under future contextual conditions that have not been properly anticipated. Many simulation models exist for demonstrating the safety cases of new operational concepts and technologies. However the results from such models can only be as valid as the accuracy and completeness of assumptions made about the future context in which the new operational concepts and/or technologies will be immersed. Of course that future has not happened yet. What is needed is a reasonably high-confidence description of the future operational context, capturing critical contextual characteristics that modulate both the likelihood of occurrence of hazards, and the likelihood that those hazards will lead to negative safety events. Heuristics extracted from scenarios, questionnaires, and observed trends from scanning the aviation horizon may be helpful in capturing those future changes in a way conducive to safety assessment. What is also needed is a checklist of potential sources of emerging risk that arise from organizational features that are frequently overlooked. The ultimate goal is to develop a pragmatic, workable method for using descriptions of the future aviation context, to generate valid predictions of safety risks.

prospection↗

NASA NTRS1999

The Aviation System Analysis Capability Air Carrier Cost-Benefit Model

To meet its objective of assisting the U.S. aviation industry with the technological challenges of the future, NASA must identify research areas that have the greatest potential for improving the operation of the air transportation system. Therefore, NASA is developing the ability to evaluate the potential impact of various advanced technologies. By thoroughly understanding the economic impact of advanced aviation technologies and by evaluating how the new technologies will be used in the integrated aviation system, NASA aims to balance its aeronautical research program and help speed the introduction of high-leverage technologies. To meet these objectives, NASA is building the Aviation System Analysis Capability (ASAC). NASA envisions ASAC primarily as a process for understanding and evaluating the impact of advanced aviation technologies on the U.S. economy. ASAC consists of a diverse collection of models and databases used by analysts and other individuals from the public and private sectors brought together to work on issues of common interest to organizations in the aviation community. ASAC also will be a resource available to the aviation community to analyze; inform; and assist scientists, engineers, analysts, and program managers in their daily work. The ASAC differs from previous NASA modeling efforts in that the economic behavior of buyers and sellers in the air transportation and aviation industries is central to its conception. Commercial air carriers, in particular, are an important stakeholder in this community. Therefore, to fully evaluate the implications of advanced aviation technologies, ASAC requires a flexible financial analysis tool that credibly links the technology of flight with the financial performance of commercial air carriers. By linking technical and financial information, NASA ensures that its technology programs will continue to benefit the user community. In addition, the analysis tool must be capable of being incorporated into the wide-ranging suite of economic and technical models that comprise ASAC. This report describes an Air Carrier Cost-Benefit Model (CBM) that meets these requirements. The ASAC CBM is distinguished from many of the aviation cost-benefit models by its exclusive focus on commercial air carriers. The model considers such benefit categories as time and fuel savings, utilization opportunities, reliability and capacity enhancements, and safety and security improvements. The model distinguishes between benefits that are predictable and those that occur randomly. By making such a distinction, the model captures the ability of air carriers to reoptimize scheduling and crew assignments for predictable benefits. In addition, the model incorporates a life-cycle cost module for new technology, which applies the costs of nonrecurring acquisitions, recurring maintenance and operation, and training to each aircraft equipment type independently.

Gaier, Eric M.↗

NASA NTRS2001

Satellite Delivery of Aviation Weather Data

With aviation traffic continuing to increase worldwide, reducing the aviation accident rate and aviation schedule delays is of critical importance. In the United States, the National Aeronautics and Space Administration (NASA) has established the Aviation Safety Program and the Aviation System Capacity Program to develop and test new technologies to increase aviation safety and system capacity. Weather is a significant contributor to aviation accidents and schedule delays. The timely dissemination of weather information to decision makers in the aviation system, particularly to pilots, is essential in reducing system delays and weather related aviation accidents. The NASA Glenn Research Center is investigating improved methods of weather information dissemination through satellite broadcasting directly to aircraft. This paper describes an on-going cooperative research program with NASA, Rockwell Collins, WorldSpace, Jeppesen and American Airlines to evaluate the use of satellite digital audio radio service (SDARS) for low cost broadcast of aviation weather information, called Satellite Weather Information Service (SWIS). The description and results of the completed SWIS Phase 1 are presented, and the description of the on-going SWIS Phase 2 is given.

Kerczewski, Robert J.↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗

NASA NTRS2007

A Study to Estimate the Effectiveness of Visual Testing Training for Aviation Maintenance Management

The Air Commerce Act of 1926 set the beginning for standards in aviation maintenance. Even after deregulation in the late l970s, maintenance standards and requirements still have not changed far from their initial criteria. After a potential candidate completes Federal Aviation Administration training prerequisites, they may test for their Airframe and Powerplant (A&P) certificate. Performing maintenance in the aviation industry for a minimum of three years, the technician may then test for their Inspection Authorization (IA). After receiving their Airframe and Powerplant certificate, a technician is said to have a license to perform. At no time within the three years to eligibility for Inspection Authorization are they required to attend higher-level inspection training. What a technician learns in the aviation maintenance industry is handed down from a seasoned technician to the new hire or is developed from lessons learned on the job. Only in Europe has the Joint Aviation Authorities (JAA) required higher-level training for their aviation maintenance technicians in order to control maintenance related accidents (Lu, 2005). Throughout the 1990s both the General Accounting Office (GAO) and the National Transportation Safety Board (NTSB) made public that the FAA is historically understaffed (GAO, 1996). In a safety recommendation the NTSB stated "The Safety Board continues to lack confidence in the FAA's commitment to provide effective quality assurance and safety oversight of the ATC system (NTSB, 1990)." The Federal Aviation Administration (FAA) has been known to be proactive in creating safer skies. With such reports you would suspect the FAA to also be proactive in developing more stringent inspection training for aviation maintenance technicians. The purpose of this study is to estimate the effectiveness of higher-level inspection training, such as Visual Testing (VT) for aviation maintenance technicians, to improve the safety of aircraft and to make recommendations to management with regard to the value of such training.

Law, Lewis Lyle↗

NASA NTRS2024

Additional Aviation Sustainability Strategies

Emissions from the aviation sector are expected to grow significantly in the next few decades due to growing demand for aviation. The U.S. fleet size has increased significantly in the past 20 years, growing from 2,132 aircraft to 5,485 aircraft. Similarly from 1990 to 2019, aviation emissions more than doubled, growing faster than any other transportation mode. These emissions include carbon dioxide (CO2), as well as non-CO2 sources including nitrogen oxides (NOx), sulfur dioxide (SO2), water (H2O), and particulate matter (soot). However, this report focuses specifically on CO2 emissions because the impacts from CO2 are well-understood and long-lived. There are also many climate goals tied to CO2 emissions, including the US Aviation Climate Action Plan which is pushing for net-zero in 2050. CO2 is a greenhouse, or heat-trapping, gas that comes from the extraction and burning of fossil fuels, from wildfires, and natural processes like volcanoes. Greenhouse gases absorb heat radiating from the Earth’s surface and release it in all directions, including back toward Earth’s surface. By adding more CO2 to the atmosphere, humans amplify the natural greenhouse effect, causing global temperature to rise. Since the onset of industrial times in 1750, human activities have raised atmospheric CO2 by 50%. Considering this challenge, the United States has set a climate goal to achieve net-zero CO2 emissions in the U.S. aviation sector by 2050 to reduce the environmental impact of aviation. Net-zero emissions refers to the amount of greenhouse gases (GHGs), such as CO2, methane or SO2, that are removed from the atmosphere being equal to those emitted by human activity. This contrasts with carbon-neutrality, which refers to no net release of carbon dioxide to the atmosphere, especially through offsetting emissions, that could be accomplished through a series of actions. As of 2022, the goal of net-zero has been adopted by much of the aviation industry including multiple airlines and countries. To reach net-zero by 2050, sustainable aviation fuels (SAFs) are identified as the technology that could provide the greatest impact within this timeframe. However, certain challenges may limit the production and/or adoption of SAFs in the marketplace, such as competition for feedstocks, production capacity, and high cost. In this case, SAFs would be insufficient to meet the net-zero CO2 emission goals. Therefore, in addition to SAFs, other strategies will be needed to close the gap on emissions produced as a result of SAF shortfalls or to enhance the impact of SAFs on reducing emissions. NASA’s Aeronautics Research Mission Directorate (ARMD) aims to examine these additional pathways to explore how its research may best contribute to the realization of net-zero emissions by 2050. Specifically, this effort considers strategies that can reduce emissions in the aviation sector in combination with the use of SAFs and carbon offsets while still meeting projected passenger demand. The strategies considered are not only feasible but also could be operationally scalable by 2050. The goal of this report is to investigate additional strategies such as alternative flight and ground operations, providing insight into the CO2 emission reduction potential and feasibility of strategy implementation by 2050.

Carbon Emissions↗

DOE OSTI2024

High Performance Metal-Supported SOFC System for Range Extension of Commercial Aviation

The DOE ARPA-E REEACH program [1] has enabled this Phase 1 study that conceptualizes a commercial 154 passenger electric aircraft using renewable aviation fuel for range extension while meeting similar mission performance levels as current commercial aircraft. Commercial aviation accounts for about 2.5% of global CO2 emissions, and close to 5% of overall anthropogenic climate change due to the added accounting of contrail’s effects [2]. The sector has adopted a goal of carbon neutrality by 2050 [3]. 110 nations, and recently the new US administration, have embarked on work to address this important challenge. The US DOE has released a roadmap to enable the further development of sustainable aviation fuel (SAF) in the hopes that it could meet all of aviation’s fuel needs by 2050 [4]. However, SAF fuel price and sufficient future feedstock availability remains a concern [5]. Considering the first commercial biofuel flight demonstration 14 years ago [6] and that SAF still only comprises less than 0.1% of jet fuel use, the question arises as how it can ramp up to 100% use in the next 27 years. One option that has the potential to substantially reduce the need for SAF is to implement light-weight Solid Oxide Fuel Cells (SOFCs) that can efficiently utilize SAF or other sulfur-free hydrocarbon fuels. As opposed to lower temperature PEM (Proton Exchange Membrane) fuel cells that can use high-purity hydrogen, SOFC’s fuels are very flexible, ranging from low cost liquefied natural gas (LNG), renewable liquefied natural gas (RLNG), to SAF, to generate clean electrical power for use in aviation propulsion and auxiliary power units [7] [8]. Research progress is needed to enable SOFCs to achieve 3.0 kW/kg power density, making them viable for aviation [9]. The DOE’s Advanced Research Projects Agency – Energy (ARPA-E) has therefore initiated an aviation SOFC R&D program that is anticipated to dramatically reduce aircraft fuel use though the implementation of “Range Extenders for Electric Aviation with low Carbon and High efficiency” (REEACH) [1] for medium range commercial aircraft. This report summarizes the progress during phase 1 of the REEACH program.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2016

Aviation Safety Concerns for the Future

The Future Aviation Safety Team (FAST) is a multidisciplinary international group of aviation professionals that was established to identify possible future aviation safety hazards. The principle was adopted that future hazards are undesirable consequences of changes, and a primary activity of FAST became identification and prioritization of possible future changes affecting aviation. Since 2004, FAST has been maintaining a catalogue of "Areas of Change" (AoC) that could potentially influence aviation safety. The horizon for such changes is between 5 to 20 years. In this context, changes must be understood as broadly as possible. An AoC is a description of the change, not an identification of the hazards that result from the change. An ex-post analysis of the AoCs identified in 2004 demonstrates that changes catalogued many years previous were directly implicated in the majority of fatal aviation accidents over the past ten years. This paper presents an overview of the current content of the AoC catalogue and a subsequent discussion of aviation safety concerns related to these possible changes. Interactions among these future changes may weaken critical functions that must be maintained to ensure safe operations. Safety assessments that do not appreciate or reflect the consequences of significant interaction complexity will not be fully informative and can lead to inappropriate trade-offs and increases in other risks. The FAST strongly encourages a system-wide approach to safety risk assessment across the global aviation system, not just within the domain for which future technologies or operational concepts are being considered. The FAST advocates the use of the "Areas of Change" concept, considering that several possible future phenomena may interact with a technology or operational concept under study producing unanticipated hazards.

emerging risks↗

NASA NTRS2000

Aviation Weather Information Requirements Study

The Aviation Safety Program (AvSP) has as its goal an improvement in aviation safety by a factor of 5 over the next 10 years and a factor of 10 over the next 20 years. Since weather has a big impact on aviation safety and is associated with 30% of all aviation accidents, Weather Accident Prevention (WxAP) is a major element under this program. The Aviation Weather Information (AWIN) Distribution and Presentation project is one of three projects under this element. This report contains the findings of a study conducted by the Georgia Tech Research Institute (GTRI) under the Enhanced Weather Products effort, which is a task under AWIN. The study examines current aviation weather products and there application. The study goes on to identify deficiencies in the current system and to define requirements for aviation weather products that would lead to an increase in safety. The study also provides an overview the current set of sensors applied to the collection of aviation weather information. New, modified, or fused sensor systems are identified which could be applied in improving the current set of weather products and in addressing the deficiencies defined in the report. In addition, the study addresses and recommends possible sensors for inclusion in an electronic pilot reporting (EPIREP) system.

Keel, Byron M.↗

NASA NTRS2008

Distributed Aviation Concepts and Technologies

Aviation has experienced one hundred years of evolution, resulting in the current air transportation system dominated by commercial airliners in a hub and spoke infrastructure. While the first fifty years involved disruptive technologies that required frequent vehicle adaptation, the second fifty years produced a stable evolutionary optimization of decreasing costs with increasing safety. This optimization has resulted in traits favoring a centralized service model with high vehicle productivity and cost efficiency. However, it may also have resulted in a system that is not sufficiently robust to withstand significant system disturbances. Aviation is currently facing rapid change from issues such as environmental damage, terrorism threat, congestion and capacity limitations, and cost of energy. Currently, these issues are leading to a loss of service for weaker spoke markets. These catalysts and a lack of robustness could result in a loss of service for much larger portions of the aviation market. The impact of other competing transportation services may be equally important as casual factors of change. Highway system forecasts indicate a dramatic slow down as congestion reaches a point of non-linearly increasing delay. In the next twenty-five years, there is the potential for aviation to transform itself into a more robust, scalable, adaptive, secure, safe, affordable, convenient, efficient and environmentally friendly system. To achieve these characteristics, the new system will likely be based on a distributed model that enables more direct services. Short range travel is already demonstrating itself to be inefficient with a centralized model, providing opportunities for emergent distributed services through air-taxi models. Technologies from the on-demand revolution in computers and communications are now available as major drivers for aviation on-demand adaptation. Other technologies such as electric propulsion are currently transforming the automobile industry, and will also significantly alter the functionality of future distributed aviation concepts. Many hurdles exist, including technology, regulation, and perception. Aviation has an inherent governmental role not present in other recent on-demand transformations, which may pose a risk of curtailing aviation democratization .

Moore, Mark D.↗

NASA NTRSDate not supplied

NASA Aeronautics Sustainable Aviation Overview

We live in challenging but exciting times for aviation as the sector continues to emerge from the unprecedented impact of a global pandemic while addressing its contribution to global warming. This perfect storm provides opportunity and the drivers that can lead to the most substantial changes the aviation sector has seen since the convergence of jet engines and swept wings over 60 years ago. The reality is we are entering the dawn of a new era of sustainable aviation full of opportunities and challenges that must economically address the health and transportation needs of society with unprecedentedly low impact on local, regional, and global environments. Today, commercial aviation physically connects countries across the globe and is an integral, critical part of today’s expanding global economy. Commercial aviation relies almost entirely on subsonic transport aircraft to constantly move people and goods from one place to another across the globe. While an effective means of transportation providing an unmatched combination of payload, speed, and range, future subsonic aircraft must continue to improve to meet efficiency, environmental, and economic goals established around the world to sustain the substantial growth projected in this well-established market. Commercial supersonic transports have come and gone in the global market, but they are on the verge of reemerging. Vertical lift vehicles leveraging breakthroughs in autonomous systems and electrified propulsion are positioned for massive growth in local and regional markets leading to daily flights within metropolitan areas that may exceed that across countries. To succeed, these new aircraft must be sustainable from day 1. NASA Aeronautics has a long history of contributions to aviation, and today it continues to address the challenge of sustainable growth of the traditional air transportation system through the research and development of systems and technologies for future aircraft and airspace operations. Research programs over the last decade have set the stage to demonstrate key technologies offering step-change performance and environmental impact. Through a Sustainable Flight National Partnership, NASA will partner with industry, academia, and other government agencies to demonstrate the most promising technologies during the 2020s to enable revolutionary systems in the early 2030s. In parallel, NASA will explore systems and technologies for further benefits approaching zero environmental impact beyond the 2030s. Similarly, NASA programs are addressing the challenges of emerging markets enabled by vertical take-off/landing and low-boom supersonic cruise. This oral-only presentation will focus on opportunities and challenges in the traditional subsonic transport while touching on emerging vertical lift and supersonic commercial aviation markets. NASA’s recent contributions, ongoing research, and plans for sustainable aviation will be discussed, including airframe, propulsion, and integrated vehicle systems and technologies.

James A. Kenyon↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

DOE OSTI2025

Quantifying health benefits of sustainable aviation fuels: Modeling decreased ultrafine particle emissions and associated impacts on communities near the Seattle-Tacoma International Airport

Exposure to ultrafine particles (UFP, ≤100 nm) is an emerging health concern linked to premature mortality, with jet fuel combustion identified as a significant source of UFPs near airports. Sustainable aviation fuel (SAF) adoption has the potential to reduce aviation-related UFPs and may particularly benefit populations who reside nearby. However, assessing aviation-specific impacts on health remains challenging due to the lack of tools capable of addressing: fine-scale exposure evaluation, novel ambient pollutants, and groups with increased exposure or susceptibility. We develop and apply a method to estimate reductions in mortality associated with aviation-related UFP reductions at the Seattle-Tacoma (SEA-TAC) International Airport under SAF adoption scenarios, with a focus on near-airport communities. Using UFP exposure surfaces generated from AERMOD modeling, flight count data, and UFP measurements, we evaluated UFP reductions under various control scenarios. We estimated mortality reductions by combining this with population data, baseline mortality, and a hazard ratio of 1.012 (95 % confidence interval: 1.010, 1.015) per interquartile range increment of 2723 particles/cm 3 . Our analysis included 412 census tracts representing almost 1.5 million adults. Baseline aviation-related UFP exposures averaged 1145 (SD: 277) particles/cm 3 . The highest baseline concentrations and subsequent reductions under SAF scenarios were near SEA-TAC. Mortality case reductions averaged between 3.1 (95 % range: 2.5–3.7) for a 5 % UFP reduction to 31.0 (24.6–37.4) for a 50 % reduction, with corresponding mortality rate reductions of 0.2 (0.2–0.3) to 2.1 (1.7–2.5) cases per 100,000 people per year. Mortality rate reductions were larger among populations residing closer to SEA-TAC, including those that were Hispanic or Latino, below-poverty, and did not identify as White. Reducing aviation-related UFPs through SAF adoption could lead to lower mortality, particularly in near-airport communities. This reproducible approach can be adapted to other settings to evaluate health benefits from aviation-related UFP reductions.

Aviation-related air pollution↗

NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗

NASA NTRS2022

Sustainable Aviation Operations and the Role of Information Technology and Data Science: Background, Current Status and Future Directions

This paper reviews the achievements of the international community towards environmentally friendly aviation operations, also referred to as Sustainable Aviation Operations in the last 25 years and the aspirations and goals to limit the impact of aviation and climate in the future. The framework for achieving global progress is provided by the International Civil Aviation Organization. NASA and FAA supported research and development to advance ATM concepts, and implemented the technology, concepts, and procedures that were responsible for creating fuel efficient flights. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. Future developments in aviation operations require new concepts, procedure, modeling, and analysis techniques. There is an increasing interest in applying methods based on Machine Learning Techniques to problems in Air Traffic Management. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and the availability of a rich historical database provide opportunities to exploit the richness of data-driven methods. The promises and challenges in applying Machine Learning Techniques to Air Traffic Management are discussed in the paper along with the testing and trustworthiness required for adoption of the techniques in operations.

Sustainable Aviation, Data Science, Machine Learni↗

NASA NTRS2022

Powering the Future of Electric Aviation

Aviation is one of the largest contributors to climate change on this planet, responsible for nearly 5% of the global warming impact on Earth. As the aviation sector is projected to grow and as new aircraft are introduced, aviation’s impact on climate change will only increase. Without sustainable alternatives implemented in the near future, aviation could strongly contribute to making this planet uninhabitable. This paper examines the possibilities of electric aviation and other power sources in reducing the impact of aviation on climate change. Although there are many power sources for aircraft, electricity is the most effective and sustainable power source. Electric aviation, however, is still relatively new, and there are limitations with electric power, such as battery size, energy density, and charging. This paper dives deeper into the applications and factors to consider for the implementation of electric aircraft into the current society, as well as a potential solution to accelerate the process. Specifically, our research finds that hydrogen fuel cells, despite a few limitations, provide the amount of energy necessary to develop capable and sustainable electric aircraft in a manner that may be the most feasible.

electric aviation↗

NASA NTRS1993

US general aviation: The ingredients for a renaissance. A vision and technology strategy for US industry, NASA, FAA, universities

General aviation today is a vital component in the nation's air transportation system. It is threatened for survival but has enormous potential for expansion in utility and use. This potential for expansion is fueled by new satellite navigation and communication systems, small computers, flat panel displays, and advanced aerodynamics, materials and manufacturing methods, and propulsion technologies which create opportunities for new levels of environmental and economic acceptability. Expanded general aviation utility and use could have a large impact on the nation's jobs, commerce, industry, airspace capacity, trade balance, and quality of life. This paper presents, in viewgraph form, a general overview of U.S. general aviation. Topics covered include general aviation shipment and billings; airport and general aviation infrastructure; cockpit, airplane, and airspace technologies; market demand; air traffic operations and aviation accidents; fuel efficiency comparisons; and general aviation goals and strategy.

Holmes, Bruce↗