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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.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

NASA Sustainable Flight National Partnership Panel

The National Aeronautics and Space Administration (NASA) Aeronautics Mission Directorate (ARMD) Hybrid Thermally Efficient Core (HyTEC) Project within the Advanced Air Vehicles Program (AAVP) is focused on accelerating the development of small-core turbofan engine technologies to advance the next entry into service (EIS) single-aisle aircraft having 25,000-35,000 lb. thrust class engines. The goal is to accelerate the development of key engine technologies with improvements in efficiency, durability, performance, and hybridization in order to meet the next EIS single-aisle aircraft expected in the 2030s. The HyTEC Project technology portfolio includes High Pressure Compressor, High Pressure Turbine, Advanced Materials, Hybrid Electric and Compact Combustor (includes operation with Sustainable Aviation Fuels (SAF)). The individual technologies were industry partner proposed and defined where HyTEC selected technologies to cost share with the industry partner. The first phase of the project has matured some and continues to mature numerous technologies to a Technology Readiness Level (TRL) 4-5, which will then be integrated into an advanced small core demonstration. The results of completed efforts have been successful, and projections toward project performance metrics of all Phase 1 technologies indicates significant progress toward meeting the requirements. The core demonstration will integrate many of these technologies into a large-scale ground demonstration that will take them to TRL 6 and enable industry to transition the technologies into the next single-aisle engine architecture. The demonstration goal is to meet the project performance metrics that signify a compact engine core with substantial efficiency and durability improvements over a year 2020 baseline. The core demonstration has been awarded with a cost-share partnership and will take place by the end of 2028.

Tony Nerone↗
NASA NTRSDate not supplied

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗
NASA NTRS1980

Technical approaches for measurement of human errors

Human error is a significant contributing factor in a very high proportion of civil transport, general aviation, and rotorcraft accidents. The technical details of a variety of proven approaches for the measurement of human errors in the context of the national airspace system are presented. Unobtrusive measurements suitable for cockpit operations and procedures in part of full mission simulation are emphasized. Procedure, system performance, and human operator centered measurements are discussed as they apply to the manual control, communication, supervisory, and monitoring tasks which are relevant to aviation operations.

Clement, W. F.↗
NASA NTRS2000

Internet over the VDL-2 Subnetwork: the VDL-2/IP Aviation Datalink System

This report describes the design to operate the standard Internet communications protocols (IP) over the VHF aviation Data Link Mode 2 (VDL-2) subnetwork. The VDL-2/IP system specified in this report can operate transparently with the current aviation users of VDL-2 (Airline Communications and Reporting System, ACARS and Aeronautical Telecommunications Network, ATN) and proposed users (Flight Information Service via Broadcast, FIS-B). The VDL-2/IP system provides a straightforward mechanisms to utilize inexpensive, commercial off-the-shelf (COTS) communications packages developed for the Internet as part of the aviation datalink system.

Grappel, R. D.↗
NASA NTRS1980

Analysis of general aviation single-pilot IFR incident data obtained from the NASA aviation safety reporting system

Data obtained from the NASA Aviation Safety Reporting System (ASRS) data base were used to determine problems in general aviation single pilot IFR operations. The data examined consisted of incident reports involving flight safety in the National Aviation System. Only those incidents involving general aviation fixed wing aircraft flying under IFR in instrument meteorological conditions were analyzed. The data were cataloged into one of five major problem areas: (1) controller judgement and response problems; (2) pilot judgement and response problems; (3) air traffic control intrafacility and interfacility conflicts; (4) ATC and pilot communications problems; and (5) IFR-VFR conflicts. The significance of the related problems, and the various underlying elements associated with each are discussed. Previous ASRS reports covering several areas of analysis are reviewed.

Bergeron, H. P.↗
NASA NTRS1996

Operation Heli-STAR - Atlanta Communications Experiment (ACE)

Operation Heli-STAR (Helicopter Short-Haul Transportation and Aviation Research) was established and operated in Atlanta, Georgia, during the period of the 1996 Centennial Olympic Games. Heli-STAR had three major thrusts: (1) the establishment and operation of a helicopter-based cargo transportation system, (2) the management of low-altitude air traffic in the airspace of an urban area, and (3) the collection and analysis of research and development data associated with items 1 and 2. Heli-STAR was a cooperative industry/government program that included parcel package shippers and couriers in the Atlanta area, the helicopter industry, aviation electronics manufacturers, the Federal Aviation Administration (FAA), the National Aeronautics and Space Administration (NASA), and support contractors. Several detailed reports have been produced as a result of Operation Heli-STAR. These include four reports on acoustic measurements and associated analyses, and reports on the Heli-STAR tracking data including the data processing and retrieval system, the Heli-STAR cargo simulation, and the community response system. In addition, NASA's Advanced General Aviation Transport Experiments (AGATE) program has produced a report describing the Atlanta Communications Experiment (ACE) which produced the avionics and ground equipment using automatic dependent surveillance-broadcast (ADS-B) technology. This latter report is restricted to organizations belonging to NASA's AGATE industry consortium. A complete list of these reports is shown on the following page.

Source record↗
NASA NTRS2012

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in realtime. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors

Harrivel, Angela↗
NASA NTRS2012

Functional Near Infrared Spectroscopy: Watching the Brain in Flight

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neurological sensing technique applicable to optimizing human performance in transportation operations, such as commercial aviation. Cognitive state can be determined via pattern classification of functional activations measured with fNIRS. Operational application calls for further development of algorithms and filters for dynamic artifact removal. The concept of using the frequency domain phase shift signal to tune a Kalman filter is introduced to improve the quality of fNIRS signals in real-time. Hemoglobin concentration and phase shift traces were simulated for four different types of motion artifact to demonstrate the filter. Unwanted signal was reduced by at least 43%, and the contrast of the filtered oxygenated hemoglobin signal was increased by more than 100% overall. This filtering method is a good candidate for qualifying fNIRS signals in real time without auxiliary sensors.

Harrivel, Angela↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety producing behaviors in addition to safety reducing behaviors. The panel will discuss opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors.

aviation safety↗
NASA NTRS2013

Preliminary Recommendations for the Collection, Storage, and Analysis of UAS Safety Data

Although the use of UASs in military and public service operations is proliferating, civilian use of UASs remains limited in the United States today. With efforts underway to accommodate and integrate UASs into the NAS, a proactive understanding of safety issues, i.e., the unique hazards and the corresponding risks that UASs pose not only through their operations for commercial purposes, but also to existing operations in the NAS, is especially important so as to (a) support the development of a sound regulatory basis, (b) regulate, design and properly equip UASs, and (c) effectively mitigate the risks posed. Data, especially about system and component failures, incidents, and accidents, provides valuable insight into how performance and operational capabilities/limitations contribute to hazards. Since the majority of UAS operations today take place in a context that is significantly different from the norm in civil aviation, i.e., with different operational goals and standards, identifying that which constitutes useful and sufficient data on UASs and their operations is a substantial research challenge.

Safety Data↗
NASA NTRS1972

Design study of general aviation collision avoidance system

The selection and design of a time/frequency collision avoidance system for use in general aviation aircraft is discussed. The modifications to airline transport collision avoidance equipment which were made to produce the simpler general aviation system are described. The threat determination capabilities and operating principles of the general aviation system are illustrated.

Bates, M. R.↗
NASA NTRSDate not supplied

Exploring Methods to Collect and Analyze Data on Human Contributions to Aviation Safety: A Panel Discussion

Focusing on undesired operator behaviors is pervasive in system design and safety management cultures in aviation. This focus limits the data that are collected, the questions that are asked during data analysis, and therefore our understanding of what operators do in everyday work. Human performance represents a significant source of aviation safety data that includes both desired and undesired actions. When safety is characterized only in terms of errors and failures, the vast majority of human impacts on system safety and performance are ignored. The outcomes of safety data analyses dictate what is learned from those data, which in turn informs safety policies and safety-related decision making. When learning opportunities are systematically restricted by focusing only on rare failure events, not only do we learn less (and less often), but we can draw misleading conclusions by relying on a non-representative sample of human performance data. Changes in how we define and think about safety can highlight new opportunities for collection and analysis of safety-relevant data. Developing an integrated safety picture to better inform safety-related decision making and policies depends upon identifying, collecting, and interpreting safety-producing behaviors in addition to safety-reducing behaviors. Opportunities and challenges in collecting and analyzing the largely unexploited data on desired, safety-producing operator behaviors are discussed.

Aviation Safety↗
NASA NTRS2012

Creating a Realistic Weather Environment for Motion-Based Piloted Flight Simulation

A flight simulation environment is being enhanced to facilitate experiments that evaluate research prototypes of advanced onboard weather radar, hazard/integrity monitoring (HIM), and integrated alerting and notification (IAN) concepts in adverse weather conditions. The simulation environment uses weather data based on real weather events to support operational scenarios in a terminal area. A simulated atmospheric environment was realized by using numerical weather data sets. These were produced from the High-Resolution Rapid Refresh (HRRR) model hosted and run by the National Oceanic and Atmospheric Administration (NOAA). To align with the planned flight simulation experiment requirements, several HRRR data sets were acquired courtesy of NOAA. These data sets coincided with severe weather events at the Memphis International Airport (MEM) in Memphis, TN. In addition, representative flight tracks for approaches and departures at MEM were generated and used to develop and test simulations of (1) what onboard sensors such as the weather radar would observe; (2) what datalinks of weather information would provide; and (3) what atmospheric conditions the aircraft would experience (e.g. turbulence, winds, and icing). The simulation includes a weather radar display that provides weather and turbulence modes, derived from the modeled weather along the flight track. The radar capabilities and the pilots controls simulate current-generation commercial weather radar systems. Appropriate data-linked weather advisories (e.g., SIGMET) were derived from the HRRR weather models and provided to the pilot consistent with NextGen concepts of use for Aeronautical Information Service (AIS) and Meteorological (MET) data link products. The net result of this simulation development was the creation of an environment that supports investigations of new flight deck information systems, methods for incorporation of better weather information, and pilot interface and operational improvements for better aviation safety. This research is part of a larger effort at NASA to study the impact of the growing complexity of operations, information, and systems on crew decision-making and response effectiveness; and then to recommend methods for improving future designs.

Daniels, Taumi S.↗
NASA NTRS1994

Alertness Management In Flight Operations: A NASA Education and Training Module

Since 1980, the NASA Ames Fatigue Countermeasures Program has been conducting research on sleep, circadian rhythms, and fatigue in a variety of flight operations 1. An original goal of the program was to return the scientific and operational knowledge to the aviation industry. To meet this goal, the NASA Ames Fatigue Countermeasures Program has created an Education and Training Module entitled, "Strategies for Alertness Management in Flight Operations." The Module was designed to meet three objectives: 1) explain the current state of knowledge about the physiological mechanisms underlying fatigue, 2) demonstrate how this knowledge can be applied to improve flight crew sleep, performance, and alertness, and 3) offer countermeasure recommendations. The Module is composed of two components: 1) a 60-minute live presentation provided by a knowledgeable individual and 2) a NASA/FAA Technical Memorandum (TM) that contains the presentation materials and appendices with complementary information. The TM is provided to all individuals attending the live presentation. The Module content is divided into three parts: 1) basic information on sleep, sleepiness, circadian rhythms, fatigue, and how flight operations affect these physiological factors, 2) common misconceptions about sleep, sleepiness, and fatigue, and 3) alertness management strategies. The Module is intended for pilots, management personnel, schedulers, flight attendants, and the many other individuals involved in the aviation system.

Rosekind, Mark R.↗