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At least 127 records · Page 7

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NASA NTRS2015

Analysis of Aviation Safety Reporting System Incident Data Associated with the Technical Challenges of the System-Wide Safety and Assurance Technologies Project

The Aviation Safety Program (AvSP) System-Wide Safety and Assurance Technologies (SSAT) Project asked the AvSP Systems and Portfolio Analysis Team to identify SSAT-related trends. SSAT had four technical challenges: advance safety assurance to enable deployment of NextGen systems; automated discovery of precursors to aviation safety incidents; increasing safety of human-automation interaction by incorporating human performance, and prognostic algorithm design for safety assurance. This report reviews incident data from the NASA Aviation Safety Reporting System (ASRS) for system-component-failure- or-malfunction- (SCFM-) related and human-factor-related incidents for commercial or cargo air carriers (Part 121), commuter airlines (Part 135), and general aviation (Part 91). The data was analyzed by Federal Aviation Regulations (FAR) part, phase of flight, SCFM category, human factor category, and a variety of anomalies and results. There were 38 894 SCFM-related incidents and 83 478 human-factorrelated incidents analyzed between January 1993 and April 2011.

Withrow, Colleen A.↗

NASA NTRS2017

NASA Aviation Safety Reporting System (ASRS)

The NASA Aviation Safety Reporting System (ASRS) collects, analyzes, and distributes de-identified safety information provided through confidentially submitted reports from frontline aviation personnel. Since its inception in 1976, the ASRS has collected over 1.4 million reports and has never breached the identity of the people sharing their information about events or safety issues. From this volume of data, the ASRS has released over 6,000 aviation safety alerts concerning potential hazards and safety concerns. The ASRS processes these reports, evaluates the information, and provides selected de-identified report information through the online ASRS Database at http:asrs.arc.nasa.gov. The NASA ASRS is also a founding member of the International Confidential Aviation Safety Systems (ICASS) group which is a collection of other national aviation reporting systems throughout the world. The ASRS model has also been replicated for application to improving safety in railroad, medical, fire fighting, and other domains. This presentation will discuss confidential, voluntary, and non-punitive reporting systems and their advantages in providing information for safety improvements.

Connell, Linda J.↗

NASA NTRS2016

NASA Aviation Safety Reporting System (ASRS)

The NASA Aviation Safety Reporting System (ASRS) collects, analyzes, and distributes de-identified safety information provided through confidentially submitted reports from frontline aviation personnel. Since its inception in 1976, the ASRS has collected over 1.4 M reports and has never breached the identity of the people sharing their information about events or safety issues. From this volume of data, the ASRS has released over 6,000 aviation safety alerts concerning potential hazards and safety concerns. The ASRS processes these reports, evaluates the information, and provides selected de-identified report information through the online ASRS Database at http:asrs.arc.nasa.gov. The NASA ASRS is also a founding member of the International Confidential Aviation Safety Systems (ICASS) group which is a collection of other national aviation reporting systems throughout the world. The ASRS model has also been replicated for application to improving safety in railroad, medical, fire fighting, and other domains. This presentation will discuss confidential, voluntary, and non-punitive reporting systems and their advantages in providing information for safety improvements.

Connell, Linda↗

NASA NTRS2019

Evaluation Studies of a 800W Solid Oxide-Based Fuel Cells Stack for Electrical Power in Aviation

As both NASA and the aeronautics industry recognize the need for higher fuel efficiency and lower carbon emissions in both commercial airline and private aviation applications, development of all-electric or hybrid electric aircraft have garnered renewed interest in the aviation community. For the particular example of the hybrid-electric option, the solid oxide fuel cell (SOFC) is an attractive option for the power source, due to its potential to utilize aviation fuels thereby having minimal impact to aviation infrastructure. SOFC stack performance depends upon many factors, one of the most important is the way the oxidant and fuel gases are delivered to the fuel cells. System modeling of various aircraft configurations for FUELEAP (Fostering Ultra-Efficient, Low-Emitting Aviation Power) point to the need to operate SOFC stacks at high current densities. This creates challenges in the thermal profile of the stacks with potential to create large thermal gradients and hot spots. This study investigates two types of commercial solid oxide fuel cell stacks, the cross flow and co-flow gas designs, both convectively cooled with cathode air. High fuel utilization factors were also employed under varying electrical loads expected from the demands of flight. In addition, performance, range of operation and endurance were investigated under conditions of high current loads and thermal cycling. Evaluations include the study of gas kinetic using electrochemical spectroscopy. Testing took place at the facilities of NASA Glenn using a commercial test system (FuelCon AG, Magdeburg Germany). These studies are crucial to the Glenn Research Center's ability to conduct research, evaluation and development of the next-generation SOFC based stacks for cutting-edge energy technologies for aerospace applications. This study supports NASA's Convergent Aeronautics Solutions' (CAS) FUELEAP project.

Goldsby, Jon C.↗

NASA NTRS2019

Application of Machine Learning Techniques to Aviation Operations: Promises and Challenges

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. This paper reviews the current-state-of-the art in applying MLT to aviation operations, its promises and challenges. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This paper compares the methodology used in and issues to be addressed in applying either model-driven or data-driven methods. Some aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for data-driven methods. The application of MLT to aviation operations falls into three categories: (a) based on the lack of a physics-based model, MLT is the favored approach, (b) marginal difference between regression methods using physics-based models and MLT and (c) better results using a blend of physics-based methods combined with MLT. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗

NASA NTRS2018

Catalyzing Disruptive Mobility Opportunities Through Transformational Aviation Power

Lightweight, efficient power production has been a pacing technology for aviation. Advances in airborne electric propulsion technology have enabled new aviation concepts in markets that previously were not dominated by aviation systems, largely because electric propulsion allows for efficient, integrated propulsion/aerodynamic/control solutions that were previously not practical with combustion-based power architectures. These new markets include automated package delivery, urban air mobility, and short-haul transportation. As these markets evolve, the impact of using electricity for airborne propulsion on an expanding mission set, as well as the sheer amount of energy consumed, will begin to challenge the energy harvesting and distribution paradigm as it exists today. Left unaddressed, these challenges could stymie the evolution of these new markets and mobility options. This paper identifies some of the potential systemic issues associated with the expanded use of electric propulsion and explores the requirements associated with alternate aviation power architectures. The recommended path includes the development of a new hybrid-electric aviation power architecture that can be used in conjunction with a portfolio of evolving battery-electric and combustion-based systems.

Borer, Nicholas K.↗

NASA NTRS2020

Observations on the Application of Machine Learning Techniques to Aviation Operations

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories: (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗

NASA NTRS2020

Application of Machine Learning Techniques to Aviation Operations: A Case Study

There is an increasing interest in applying methods based on Machine Learning Techniques (MLT) to problems in aviation operations. The current interest is based on developments in Cloud Computing, the availability of open software and the success of MLT in automation, consumer behavior and finance involving large database. Historically aviation operations have been analyzed using physics-based models and provide information for making operational decisions. This talk describes issues to be addressed in applying either model-driven or data-driven methods. Aviation operations involving many decision makers, multiple objectives, poor or unavailable physics-based models and a rich historical database are prime candidates for analysis using data-driven methods. The issues are illustrated by a detailed example and summary of current research in the area. The application of MLT to aviation operations falls into two categories 58; (a) based on the lack of a physics-based model, MLT is the favored approach and (b) marginal difference between regression methods using physics-based models and MLT. Further research is needed in the selection of MLT to critical aviation operations. As always, the best approach depends on the task, the physical understanding of the problem and the quality and quantity of the available data.

Sridhar, Banavar↗

NASA NTRSDate not supplied

Dynamic Channel Assignments for Efficient Use of Aviation Spectrum Allocations

The demand for voice and data communications continues to rise with the emergence of new aerial vehicles into the airspace and the continued growth of aviation operations throughout the National Airspace System (NAS). Recent studies have shown that the anticipated growing demand for spectrum resources will exceed the capacity of existing aviation spectrum allocations. Further, airspace configurations, via assignment of fixed channel allocations within standard service volumes, do not allow for the dynamic and efficient distribution of spectrum resources based on airspace demand; as a result, a new approach to aviation spectrum management is needed to support the forecasted needs of new airspace users. The National Aeronautics and Space Administration (NASA) is investigating applications of artificial intelligence (AI), machine learning (ML), and other advanced concepts to solve a dynamic constraint satisfaction problem which is analogous to the frequency assignment problem faced by aviation. Procedures and strategies for dynamic channel allocation can be borrowed from other large-scale mobile services (i.e., 4G/5G applications) and can provide a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the strict quality of service prescribed by aeronautical standards.

Communications↗

NASA NTRSDate not supplied

Dynamic Channel Assignments for Efficient Use of Aviation Spectrum Allocations

communications↗

NASA NTRS2022

Assessing Several Non-Traditional Data Sources for Value in Aviation Safety

The NASA System-Wide Safety (SWS) project and its predecessor projects have been developing Machine Learning (ML) algorithms for commercial aviation safety for many years. These algorithms have been applied to Flight Operations Quality Assurance (FOQA); radar track data (e.g., Threaded Track); and safety reports, including Aviation Safety Reporting System (ASRS) and Aviation Safety Action Plan (ASAP). SWS is working with partners to get access to other data that air carriers provide, such as maintenance data, and has been assisting carriers in working with other data, such as Line Operations Safety Audit (LOSA) data, using manual methods. However, the project has discussed whether there are other data that are not traditionally used in aviation safety analysis that may be useful. This paper discusses four sets of data and models that are not traditionally used in aviation safety but that have shown promise for such use. In the future, we plan to incorporate such data into ML algorithms to use with data that we have used before and determine the additional benefit that is actually achieved under different contexts from the inclusion of these non-traditional data sources.

Nikunj C. Oza↗

NASA NTRS2023

An Analysis of Barriers Preventing the Widespread Adoption of Predictive and Prescriptive Maintenance in Aviation

The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.

Christopher Teubert↗

DOE OSTI2024

Sustainable Aviation Fuel Blending and Logistics

Worldwide, aviation accounts for 2% of all manmade carbon dioxide emissions and 12% of all transportation CO2 emissions. In 2023, the U.S. accounted for 27% of the world jet fuel consumption. The aviation industry has set sustainability goals, and mandates. Sustainable aviation fuel (SAF), made from nonpetroleum feedstocks, significantly reduces aviation emissions. SAF must be blended with petroleum-based jet fuel prior to its use in aircraft. Jet fuel quality standards and certification documents are essential to fuel performance, operability, and safety and are the primary driver in determining locations for blending. Several locations were evaluated including terminals, airports, refineries, and greenfield/brownfield sites. While all the locations evaluated are technically capable of blending fuel, there are practical considerations that make terminals the optimal location for blending. The report includes background on jet fuel and SAF use, fuel quality standards, modes of transport for both conventional jet fuel and SAF, and terminal information.

09 BIOMASS FUELS↗

NASA NTRSDate not supplied

Green Aviation: Review, Aspirations, and Operational Improvements

Air traffic affects the environment locally, regionally and globally. Estimates show that aviation is responsible for 13% of transportation-related fossil fuel consumption and 2% of all anthropogenic CO2 emissions. Although there is considerable decline in air traffic due to COVID pandemic, Federal Aviation Administration (FAA) expects domestic air traffic to grow at an annual rate of 2.0 % over the next 20 years. Global air traffic is expected to grow more rapidly than domestic air traffic at an annual rate of 4.8% from 2011 to 2030. The desire to accommodate growing air traffic needs while limiting the impact of aviation on the environment has led to research in green aviation with the goals of better scientific understanding, utilization of alternative fuels, introduction of new aircraft technology, and rapid operational changes. Greenhouse gases, nitrogen oxides, and contrails generated by air traffic affect the climate in different and uncertain ways. Understanding the changes requires a hierarchy of models to deal with multiple disciplines, time scales ranging from few minutes to few hundred years and uncertainties in modeling parameters affecting both science and policy. This talk reviews earlier work at Ames on the modeling approach and an integrated capability to design aircraft operations based on a trade-off between fuel consumption, environmental goals and stakeholder values.

Green Aviation↗

DOE OSTI2024

Synthesis of aviation biofuel precursors from biomass-derived ketones: Substrate adsorption configuration-catalytic activity

The production of aviation biofuel precursors from biomass-derived ketones by heterogeneous catalysis has been hindered by the low catalytic activity. Herein, a series of Cu-doped metal oxide catalysts were prepared for the conversion of biomass-derived ketones to aviation biofuel precursors. Solvent-free cyclopentanone conversion via aldol condensation reached 91.1 % over Cu/Al 2 O 3 with 100 % selectivity toward dimer and trimer oxygenated species, all of which are aviation biofuel precursors. This catalyst primarily contains Cu 2 O and Cu nanoparticles which are uniformly dispersed across the Al 2 O 3 surface. From in situ DRIFTS and DFT results, the incorporation of Cu species onto Al 2 O 3 not only increased the diversity of Lewis acidic sites, but also changed the adsorption of C = O groups, which lead to the increased aldol condensation activity of Cu/Al 2 O 3 . In conclusion, this study provides insight on the design of heterogeneous catalysts suitable for the solvent-free synthesis of aviation biofuel precursors from biomass-derived ketones.

Adsorption configuration↗

DOE OSTI2024

Lignin Deoxygenation for the Production of Sustainable Aviation Fuel Blendstocks

Lignin is an abundant source of renewable aromatics that has long been targeted for valorization. Traditionally, the inherent heterogeneity and reactivity of lignin has relegated it to direct combustion, but its higher energy density compared with polysaccharides makes it an ideal candidate for biofuel production. This Review critically assesses lignin's potential as a substrate for sustainable aviation fuel blendstocks. Lignin can generate the necessary cyclic compounds for a fully renewable, sustainable aviation fuel when integrated with current paraffinic blends and can meet the current demand 2.5 times over. Using an energy-centric analysis, we show that lignin conversion technologies have the near-term potential to match the enthalpic yields of existing commercial sustainable aviation fuel production processes. Key factors influencing the viability of technologies for converting lignin to sustainable aviation fuel include lignin structure, delignification extent, depolymerization performance, and the development of stable and tunable deoxygenation catalysts.

09 BIOMASS FUELS↗

DOE OSTI2024

Sustainable Aviation Fuel State-of-Industry Report: Hydroprocessed Esters and Fatty Acids Pathway

Climate change is a pressing issue that requires immediate and decisive action to ensure a sustainable future. To reduce CO 2 emissions and speed up the transition to net-zero aviation, the Biden administration has launched the "Sustainable Aviation Fuels (SAF) Grand Challenge" to scale up production of SAF. The challenge aims to achieve 20% reduction in aviation emissions by producing 3 billion gallons per year (BGPY) of SAF by 2030 and to meet 100% of aviation fuel demand by producing 35 BGPY of SAF by 2050. In this report, we provide an overview of the current state of the hydroprocessed esters and fatty acids (HEFA) SAF industry, guided by the perspectives of the interviewed experts. Currently, the HEFA pathway is the only commercially deployed method to produce significant amounts of SAF. As a result, SAF produced via the HEFA pathway is expected to make the largest contribution to achieving the 2030 production target and play a key role in boosting and establishing the SAF market. Announced SAF's total capacity, including alcohol-to-jet, FT, and power-to-liquid facilities, is expected to reach 2 BGPY by 2030 (1), with the expected from HEFA. Total HEFA capacity, including construction and planned projects, is expected to reach about 9 BGPY by 2030; if completely executed, this would contribute to renewable diesel (RD) and SAF. The production ratio of SAF and RD will depend on market conditions, incentives, and the capabilities of facilities. While some stakeholders believe that the 2030 goal may be achieved solely via HEFA, others believe that overly relying on HEFA may be detrimental to the development of other necessary pathways to meet 2050 goals. Our conclusion is that the HEFA pathway alone will not be sufficient to reach the 2030 target. It is crucial to implement additional pathways to reach the goal. This report conducts a comprehensive analysis and evaluation of the HEFA SAF value chain. Our aim is to provide current status of the industry and to identify potential challenges that could hinder the commercial production and use of SAF produced through the HEFA pathway. We have had extensive discussions, consultations, and collaborative sessions with stakeholders in the HEFA SAF value chain, including HEFA feedstocks, potential volume of HEFA SAF, economic and sustainability metrics when compared to petroleum, and assessment of the HEFA SAF industry's ability to grow and contribute to achieving the "SAF Grand Challenge." Since the HEFA pathway produces both SAF and RD, this report compares both pathways: HEFA to SAF and HEFA to RD.

09 BIOMASS FUELS↗

NASA NTRS2017

A Virtual Laboratory for Aviation and Airspace Prognostics Research

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

LVC-DE↗