Search NASA⌕ Search

SEARCH · Search NASA

Results for “Federated Learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Propulsion Flight Research at NASA Dryden From 1967 to 1997

From 1967 to 1997, pioneering propulsion flight research activities have been conceived and conducted at the NASA Dryden Flight Research Center. Many of these programs have been flown jointly with the United States Department of Defense, industry, or the Federal Aviation Administration. Propulsion research has been conducted on the XB-70, F-111 A, F-111E, YF-12, JetStar, B-720, MD-11, F-15, F- 104, Highly Maneuverable Aircraft Technology, F-14, F/A-18, SR-71, and the hypersonic X-15 airplanes. Research studies have included inlet dynamics and control, in-flight thrust computation, integrated propulsion controls, inlet and boattail drag, wind tunnel-to-flight comparisons, digital engine controls, advanced engine control optimization algorithms, acoustics, antimisting kerosene, in-flight lift and drag, throttle response criteria, and thrust-vectoring vanes. A computer-controlled thrust system has been developed to land the F-15 and MD-11 airplanes without using any of the normal flight controls. An F-15 airplane has flown tests of axisymmetric thrust-vectoring nozzles. A linear aerospike rocket experiment has been developed and tested on the SR-71 airplane. This paper discusses some of the more unique flight programs, the results, lessons learned, and their impact on current technology.

Burcham, Frank W., Jr.↗
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 OSTI2026

Integrated System Planning: Emerging Software Requirements in the Power Industry

Power system planning software remains fragmented across organizational boundaries, with specialized tools for capacity expansion, production cost modeling, power flow, and dynamic analysis operating on incompatible data models and assumptions. This article argues that the fragmentation is not merely a technical problem but a predictable consequence of Conway's law: software architectures mirror the departmental structures within which they are developed. Regulatory milestones like Federal Energy Regulatory Commission (FERC) Order 888 formalized these divisions, but the roots trace back to the distinct engineering disciplines-mechanical, chemical, and electrical-that staffed generation and transmission planning departments in vertically integrated utilities. As the industry moves toward integrated system planning (ISP) that coordinates generation, transmission, and distribution investment decisions, the software ecosystem must evolve accordingly. We identify five categories of software requirements to enable this transition: coherent data inputs decoupled from individual applications, unified and extensible data schemas, modular component representations that support multiple abstraction levels, lifecycle management of planning datasets, and well-defined application programming interface (API) contracts that separate data exchange from algorithmic control. We examine how these requirements interact with three common workflow patterns-serial gate clearing, sequential multiapplication, and convergence oriented-and discuss the interface design principles each demands. We then outline a vision for platform-based planning architectures where specialized analytical services compose through standardized interfaces and where artificial intelligence (AI)/machine learning (ML) tools augment decision support within a disciplined software infrastructure. The practices proposed here offer a path from today's siloed tool collections toward collaborative planning ecosystems capable of handling the complexity of modern power system transformation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Artificial Intelligence in Nuclear Safeguards; Evaluating Safeguards and Security Risks and Benefits for Advanced and Small Modular Reactor Deployments

Rapidly growing interest in advanced and small modular reactor (A/SMR) technologies presents challenges as well as opportunities for implementing international safeguards and security. A/SMR deployments are expected to be more numerous, more geographically dispersed, and more varied in their designs, placing new demands on the data systems and analytical tools used to support oversight (Alberti et al., 2023; Canadian Nuclear Safety Commission et al., 2024). Because of this variability, the importance and reliance on data systems for A/SMR deployments is expected to be higher than for previous reactor generations. Artificial Intelligence and Machine Learning (AI/ML) offer potential capabilities to address the high variability inherent in A/SMR technology. The beneficiaries of AI-assisted tools include facility operators, government regulators, IAEA inspectors, and A/SMR vendors. This report analyzes how AI/ML-assisted technologies can strengthen the implementation of IAEA safeguards and security measures. It also identifies AI-assisted tools to strengthen operator, facility, and regulator knowledge management practices and examines the potential risks AI/ML-based tools may introduce to IAEA safeguards and security efforts. It concludes with a set of hypothetical, standards-style requirements for AI/ML systems used in safeguards contexts, grounded in an inspector-centric view of system verification. Despite the potential benefits of AI/ML systems, understanding potential intentional and unintentional failure modes is critical for ensuring adequate protection of nuclear materials and facilities. Unique features of A/SMRs including sealed cores, remote and novel paradigms of operation, off-site reactor fabrication, novel fuel forms, and varied refueling requirements, introduce challenges for traditional safeguards technological approaches (Pensado et al., 2024; Federation of American Scientists, 2025). AI/ML systems deployed to address these challenges may introduce new risks requiring systematic evaluation rooted in both AI-specific risk frameworks, such as the NIST AI Risk Management Framework (NIST AI RMF), and established cyber risk management standards such as NIST SP 800-30 (National Institute of Standards and Technology [NIST], 2023; NIST, 2012).

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

NASA’s Secured Airspace for Urban Air Mobility (UAM)

The Urban Air Mobility (UAM) architecture is leveraged from the Unmanned Traffic Management (UTM) concept of operations. Within the UAM environment, UAM operators work collaboratively to manage aerial vehicles in the urban environment. Providers of Services for UAM (PSU), UAM operators, and Supplemental Data Service Providers (SDSP) provide services to support flight operations within that environment. As a recognized need, various views of UAM flight information are provided to the public and public safety entities. To accomplish this, among other goals, the Federal Aviation Administration (FAA) can coordinate flight information between the FAA controlled National Airspace System (NAS) and the UAM environments through the FAA-Industry Data Exchange Protocol (FIDXP). This concept of UAM proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical take-off and landing (VTOL) or short take-off and landing (STOL) aircraft to overcome increasing surface congestion. To garner the support of UAM and to realize its potential, an assurance of cybersecurity is critical for public acceptance. Understanding the various components communicating with one-another cybersecurity, like in other industries, has come to the forefront highlighting the need to protect these networks and systems from cyberattacks. With the planned growth and reach of UAM systems, it’s clear that the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. Consequently, as these threats evolve, the UAM cybersecurity capabilities must adapt to these changes as well. While learning is always the goal, the overall intent of this workshop is to make recommendations on the following: (1) how future UAM environments can be protected against cyber-attacks, and (2) what mechanisms should be put in place to detect attacks against UAM environments.

UAM↗
DOE OSTI2025

Atomate2: modular workflows for materials science

High-throughput density functional theory (DFT) calculations have become a vital element of computational materials science, enabling materials screening, property database generation, and training of “universal” machine learning models. While several software frameworks have emerged to support these computational efforts, new developments such as machine learned force fields have increased demands for more flexible and programmable workflow solutions. This manuscript introduces atomate2, a comprehensive evolution of our original atomate framework, designed to address existing limitations in computational materials research infrastructure. Key features include the support for multiple electronic structure packages and interoperability between them, along with generalizable workflows that can be written in an abstract form irrespective of the DFT package or machine learning force field used within them. Our hope is that atomate2's improved usability and extensibility can reduce technical barriers for high-throughput research workflows and facilitate the rapid adoption of emerging methods in computational material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The United States' (DOE) Action Plan for Power Sector Decarbonization

A collaborative report from the Clean Energy Ministerial (CEM) on Lessons Learned for Rapid Decarbonization of Power Sectors was delivered to energy ministers and presented at the 13th CEM (CEM13) in the United States in September 2022. In light of these lessons learned and discussed at CEM13, several jurisdictions signaled intent to develop Action Plans for power sector decarbonization. The first cohort of Action Plans was released at CEM14 in India in July 2023. The U.S. Department of Energy (DOE) is pleased to release this Action Plan as a contribution to the second cohort of Action Plans released at CEM15 in Brazil in October 2024. The Action Plans, supported by the 21st Century Power Partnership, and other CEM workstreams via direct technical assistance and capacity building, are intended to focus on select implementation actions, given each country's existing power sector goals and activities, and are an opportunity for countries to display leadership in power sector decarbonization. The Action Plans are organized in a framework for Planning, Building and Operating, as well as Stakeholder Engagement where appropriate based on country priorities. These Action Plans are voluntary, developed by each country individually, not comprehensive of all activities within the jurisdiction, and are living documents that are subject to change. This Action Plan, developed by DOE, is focused on federal actions for power sector decarbonization primarily led by DOE. There are many other actions being led by other U.S. government agencies, or at the state and local level, that are not featured in this Action Plan.

21st Century Power Partnership↗
NASA NTRS1998

Improving Student Achievement in Math and Science

As the new millennium approaches, a long anticipated reckoning for the education system of the United States is forthcoming, Years of school reform initiatives have not yielded the anticipated results. A particularly perplexing problem involves the lack of significant improvement of student achievement in math and science. Three "Partnership" projects represent collaborative efforts between Xavier University (XU) of Louisiana, Southern University of New Orleans (SUNO), Mississippi Valley State University (MVSU), and the National Aeronautics and Space Administration (NASA), Stennis Space Center (SSC), to enhance student achievement in math and science. These "Partnerships" are focused on students and teachers in federally designated rural and urban empowerment zones and enterprise communities. The major goals of the "Partnerships" include: (1) The identification and dissemination of key indices of success that account for high performance in math and science; (2) The education of pre-service and in-service secondary teachers in knowledge, skills, and competencies that enhance the instruction of high school math and science; (3) The development of faculty to enhance the quality of math and science courses in institutions of higher education; and (4) The incorporation of technology-based instruction in institutions of higher education. These goals will be achieved by the accomplishment of the following objectives: (1) Delineate significant ?best practices? that are responsible for enhancing student outcomes in math and science; (2) Recruit and retain pre-service teachers with undergraduate degrees in Biology, Math, Chemistry, or Physics in a graduate program, culminating with a Master of Arts in Curriculum and Instruction; (3) Provide faculty workshops and opportunities for travel to professional meetings for dissemination of NASA resources information; (4) Implement methodologies and assessment procedures utilizing performance-based applications of higher order thinking via the incorporation of Global Learning Observations To Benefit the Environment (GLOBE), Mission to Planet Earth and the use of Geographic Imaging Systems into the K-12th grade curriculum.

Sullivan, Nancy G.↗
NASA NTRS2024

Operational Integration Assessment (OIA) of Midterm UAM Operations: Class C Airspace Tabletop Exercise and Integration Checkpoint

The National Aeronautics and Space Administration (NASA), in collaboration with the Federal Aviation Administration (FAA), is conducting research into evolving today’s air traffic management system towards a more automated and operationally flexible airspace to accommodate Urban Air Mobility (UAM) operations at scale. UAM operations, enabled by electric Vertical Takeoff and Landing (eVTOL) aircraft, may change the role of aviation in the movement of people and goods and provide practical, cost-effective air transport in metropolitan areas. FAA UAM Concept of Operations v2.0 describes three evolutionary stages of UAM operations: Initial, Midterm, and Mature State operations. Midterm operations are comprised of many complex changes to the national airspace system (NAS). The Operational Integration Assessment (OIA) was created as a capability to address the need to study the progression and identify interdependencies of those changes that may occur during the midterm UAM operations timeframe. The OIA includes a series of tabletop exercises and integration checkpoints planned to explore various use cases from end-to-end, evaluated by NASA’s Air Traffic Management eXploration (ATM-X) project in partnership with the FAA’s William J. Hughes Technical Center (WJHTC) and industry partners. The use cases were exercised in an immersive, integrated live-virtual-constructive (LVC) airspace simulation environment, called the NASA/FAA Laboratory Integrated Test Environment (NFLITE), as part of an effort to learn how UAM operations can scale beyond the as-is NAS and through the transition to higher-tempo and highly automated operations of the future. This document describes the events of the tabletop exercise held from January 24-26, 2023, at the National Airspace Research & Technology Park (NARTP) in Egg Harbor Township, New Jersey, adjacent to the WJHTC and the subsequent integration checkpoint performed on March 28, 2023,at NASA Langley Research Center (LaRC) in Hampton, Virginia.

UAM↗
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 NTRS2011

Climate Change Impacts and Responses: Societal Indicators for the National Climate Assessment

The Climate Change Impacts and Responses: Societal Indicators for the National Climate Assessment workshop, sponsored by the National Aeronautics and Space Administration (NASA) for the National Climate Assessment (NCA), was held on April 28-29, 2011 at The Madison Hotel in Washington, DC. A group of 56 experts (see list in Appendix B) convened to share their experiences. Participants brought to bear a wide range of disciplinary expertise in the social and natural sciences, sector experience, and knowledge about developing and implementing indicators for a range of purposes. Participants included representatives from federal and state government, non-governmental organizations, tribes, universities, and communities. The purpose of the workshop was to assist the NCA in developing a strategic framework for climate-related physical, ecological, and socioeconomic indicators that can be easily communicated with the U.S. population and that will support monitoring, assessment, prediction, evaluation, and decision-making. The NCA indicators are envisioned as a relatively small number of policy-relevant integrated indicators designed to provide a consistent, objective, and transparent overview of major variations in climate impacts, vulnerabilities, adaptation, and mitigation activities across sectors, regions, and timeframes. The workshop participants were asked to provide input on a number of topics, including: (1) categories of societal indicators for the NCA; (2) alternative approaches to constructing indicators and the better approaches for NCA to consider; (3) specific requirements and criteria for implementing the indicators; and (4) sources of data for and creators of such indicators. Socioeconomic indicators could include demographic, cultural, behavioral, economic, public health, and policy components relevant to impacts, vulnerabilities, and adaptation to climate change as well as both proactive and reactive responses to climate change. Participants provided inputs through in-depth discussion in breakout sessions, plenary sessions on break-out results, and several panels that provided key insights about indicators, lessons learned through experience with developing and implementing indicators, and thoughts on how the NCA could proceed to develop indicators for the NCA.

Kenney, Melissa A.↗
NASA NTRS2012

The International Space Station (ISS) Education Accomplishments and Opportunities

The International Space Station (ISS) has the unique ability to capture the imaginations of both students and teachers worldwide and thus stands as an invaluable learning platform for the advancement of proficiency in research and development and education. The presence of humans on board ISS for the past ten years has provided a foundation for numerous educational activities aimed at capturing that interest and motivating study in the sciences, technology, engineering and mathematics (STEM) disciplines which will lead to an increase in quality of teachers, advancements in research and development, an increase in the global reputation for intellectual achievement, and an expanded ability to pursue unchartered avenues towards a brighter future. Over 41 million students around the world have participated in ISS-related activities since the year 2000. Projects such as the Amateur Radio on International Space Station (ARISS) and Earth Knowledge Acquired by Middle School Students (EarthKAM), among others, have allowed for global student, teacher, and public access to space through radio contacts with crewmembers and student image acquisition respectively. . With planned ISS operations at least until 2020, projects like the aforementioned and their accompanying educational materials will be available to enable increased STEM literacy around the world. Since the launch of the first ISS element, a wide range of student experiments and educational activities have been performed by each of the international partner agencies: National Aeronautics and Space Administration (NASA), Canadian Space Agency (CSA), European Space Agency (ESA), Japan Aerospace Exploration Agency (JAXA) and Russian Federal Space Agency (Roscosmos). Additionally, a number of non-participating countries, some under commercial agreements, have also participated in Station-related activities. Many of these programs still continue while others are being developed and added to the station crewmembers tasks on a regular basis. These diverse student experiments and programs fall into one of the following categories: student-developed experiments; students performing classroom versions of ISS experiments; students participating in ISS investigator experiments; students participating in ISS engineering education; education demonstrations and cultural activities. This paper summarizes some of the main student experiments and educational activities that have been conducted on the ISS. It also highlights some upcoming projects.

Alleyne, Camille W.↗
NASA NTRS1995

The Oak Ridge Refrigerant Management Program

For many years, chlorofluorocarbons (CFC's) have been used by the Department of Energy's (DOE) Oak Ridge Y-12 Plant in air conditioning and process refrigeration systems. However, Title 6 of the Clean Air Act Amendments (CAAA) and Executive Order 12843 (Procurement Requirements and Policies for Federal Agencies for Ozone Depleting Substances) signed by President Clinton require, as policy, that all federal agencies maximize their use of safe, alternate refrigerants and minimize, where economically practical, the use of Class 1 refrigerants. Unfortunately, many government facilities and industrial plants have no plan or strategy in place to make this changeover, even though their air conditioning and process refrigeration equipment may not be sustainable after CFC production ends December 31, 1995. The Y-12 Plant in Oak Ridge, Tennessee, has taken an aggressive approach to complying with the CAAA and is working with private industry and other government agencies to solve tough manufacturing and application problems associated with CFC and hydrochlorofluorocarbon (HCFC) alternatives. Y-12 was the first DOE Defense Program (DP) facility to develop a long-range Stratospheric Ozone Protection Plan for refrigerant management for compliance with the CAAA. It was also the first DOE DP facility to complete detailed engineering studies on retrofitting and replacing all air conditioning and process refrigeration equipment to enable operation with alternate refrigerants. The management plan and engineering studies are models for use by other government agencies, manufacturing plants, and private industry. This presentation identifies some of the hidden pitfalls to be encountered in the accelerated phaseout schedule of CFC's and explains how to overcome and prevent these problems. In addition, it outlines the general issues that must be considered when addressing the phase-out of ozone depleting substances and gives some 'lessons learned' by Y-12 from its Refrigerant Management Program. Discussion topics include requirements for developing a refrigerant management plan and establishing priorities for cost-effective compliance with the CAAA, as well as ways in which employees can be empowered to develop a comprehensive refrigerant management plan. The result of this employee empowerment was a cooperative labor-management effort that is beneficial for Y-12, DOE, and the environment.

Kevil, Thomas H.↗
NASA NTRSDate not supplied

Helicopter Pilot Evaluations of the Airborne Collision Avoidance System Xr in a High-Fidelity Motion Simulation

New aircraft and aerial Urban Air Mobility operations require updated technologies to maintain vehicle separation during flight. Specifically, higher-density airspace will need more than traditional air traffic control to keep vehicles well clear of each other and avoid Near Midair Collisions. In response, the Federal Aviation Administration is currently developing the Airborne Collision Avoidance System X (ACAS X) for next-generation air traffic deconfliction, which provides caution-level and warning-level alerts in response to multiple aircraft types. This study recruited six helicopter pilots to fly an electric vertical takeoff and landing vehicle model in simulated operations under Visual Flight Rules (VFR). Flights were accomplished using the high-fidelity Vertical Motion Simulator at the NASA Ames Research Center. Participants controlled the vehicle using two side inceptors and foot rudders. The rotorcraft variant of ACAS X (ACAS Xr) was provided for alerting and guidance during traffic conflicts. Pilots used this system while giving feedback to the researchers through questionnaires, debriefs, and other discussions. Variables of interest to the study were phases of flight (i.e., Cruise, Hover, and Approach) and ACAS Xr configurations: The Collision Avoidance System configuration behaves similarly to current commercial traffic systems used for tactical deconfliction in crewed vehicles, and the Detect and Avoid configuration was developed to provide extra, corrective-level guidance for unmanned aircraft systems. Results showed that pilots found the alerting and guidance from ACAS Xr useful, effective, and acceptable for VFR operations. Certain elements, like speed guidance and text banners, were found to be of no use to the pilots. Hover and Approach scenarios were considered the most difficult for ACAS Xr alerting. Reasons for this difficulty were partially due to learning interference (i.e., overcoming previously learned behavior) and partially due to the vehicle model (i.e., NASA’s Lift Plus Cruise design). Still, alerting-based confounds reveal the need for more development for ACAS Xr during these Hover and Approach flight phases. Study caveats and future projects are discussed.

air taxis↗
NASA NTRSDate not supplied

Helicopter Pilot Evaluations of the Airborne Collision Avoidance System Xr in a High-Fidelity Motion Simulation

New aircraft and aerial Urban Air Mobility operations require updated technologies to maintain vehicle separation during flight. Specifically, higher-density airspace will need more than traditional air traffic control to keep vehicles well clear of each other and avoid Near Midair Collisions. In response, the Federal Aviation Administration is currently developing the Airborne Collision Avoidance System X (ACAS X) for next-generation air traffic deconfliction, which provides caution-level and warning-level alerts in response to multiple aircraft types. This study recruited six helicopter pilots to fly an electric vertical takeoff and landing vehicle model in simulated operations under Visual Flight Rules (VFR). Flights were accomplished using the high-fidelity Vertical Motion Simulator at the NASA Ames Research Center. Participants controlled the vehicle using two side inceptors and foot rudders. The rotorcraft variant of ACAS X (ACAS Xr) was provided for alerting and guidance during traffic conflicts. Pilots used this system while giving feedback to the researchers through questionnaires, debriefs, and other discussions. Variables of interest to the study were phases of flight (i.e., Cruise, Hover, and Approach) and ACAS Xr configurations: The Collision Avoidance System configuration behaves similarly to current commercial traffic systems used for tactical deconfliction in crewed vehicles, and the Detect and Avoid configuration was developed to provide extra, corrective-level guidance for unmanned aircraft systems. Results showed that pilots found the alerting and guidance from ACAS Xr useful, effective, and acceptable for VFR operations. Certain elements, like speed guidance and text banners, were found to be of no use to the pilots. Hover and Approach scenarios were considered the most difficult for ACAS Xr alerting. Reasons for this difficulty were partially due to learning interference (i.e., overcoming previously learned behavior) and partially due to the vehicle model (i.e., NASA’s Lift Plus Cruise design). Still, alerting-based confounds reveal the need for more development for ACAS Xr during these Hover and Approach flight phases. Study caveats and future projects are discussed.

air taxis↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
NASA NTRS2014

A Flight Research Overview of WSPR, a Pilot Project for Sonic Boom Community Response

In support of NASAs ongoing effort to bring supersonic commercial travel to the public, NASA Dryden Flight Research Center and NASA Langley Research Center, in cooperation with other industry organizations, conducted a flight research experiment to identify the methods, tools, and best practices for a large-scale quiet (or low) sonic boom community human response test. The name of the effort was Waveforms and Sonic boom Perception and Response. Such tests will go towards building a dataset that governing agencies like the Federal Aviation Administration and International Civil Aviation Organization will use to establish regulations for acceptable sound levels of overland sonic booms. Until WSPR, there had never been an effort that studied the response of people in their own homes and performing daily activities to non-traditional, low sonic booms.WSPR was a NASA collaborative effort with several industry partners, in response to a NASA Aeronautics Research Mission Directorate Research Opportunities in Aeronautics. The primary contractor was Wyle. Other partners included Gulfstream Aerospace Corporation, Pennsylvania State University, Tetra Tech, and Fidell Associates, Inc.A major objective of the effort included exposing a community with the sonic boom magnitudes and occurrences expected in high-air traffic regions with a network of supersonic commercial aircraft in place. Low-level sonic booms designed to simulate those produced by the next generation of commercial supersonic aircraft were generated over a small residential community. The sonic boom footprint was recorded with an autonomous wireless microphone array that spanned the entire community. Human response data was collected using multiple survey methods. The research focused on essential elements of community response testing including subject recruitment, survey methods, instrumentation systems, flight planning and operations, and data analysis methods.This paper focuses on NASAs role in the efforts logistics and operations including human response subject recruitment, the operational processes involved in implementing the surveys throughout the community, instrumentation systems, logistics, flight planning, and flight operations. Findings discussed in this paper include critical lessons learned in all of those areas. The paper also discusses flight operations results. Analysis of the accuracy and repeatability of planning and executing the unique aircraft maneuver used to generate low sonic booms concluded that the sonic booms had overpressures within 0.15 pounds-per-square-feet of the planned values for 76 of t he attempts. Similarly, 90 of the attempts to generate low sonic booms within the community were successful.

sonic booms↗