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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

What's the Cube Quest Challenge?

Cube Quest Challenge, sponsored by Space Technology Mission Directorates Centennial Challenges program, is NASAs first in-space prize competition. Cube Quest is open to any U.S.-based, non-government CubeSat developer. Entrants will compete for one of three available 6U CubeSat dispenser slots on the EM-1 mission the first un-crewed lunar flyby of the Orion spacecraft launched by the Space Launch System in early 2018. The Cube Quest Challenge will award up to $5M in prizes. The advanced CubeSat technologies demonstrated by Cube Quest winners will enable NASA, universities, and industry to more quickly and affordably accomplish science and exploration objectives. This paper describes the teams, their novel CubeSat designs, and the emerging technologies for CubeSat operations in deep space environment.

Cube Quest Challenge↗
NASA NTRS2017

Physics-of-Failure Approach to Prognostics

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of the electrical components present in the system. In case of electric vehicles, computing remaining battery charge is safety-critical. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. In this presentation our approach to develop a system level health monitoring safety indicator for different electronic components is presented which runs estimation and prediction algorithms to determine state-of-charge and estimate remaining useful life of respective components. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Kulkarni, Chetan S.↗
NASA NTRS2017

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more autonomous electric vehicles emerge in our daily operation progressively, a very critical challenge lies in accurate prediction of remaining useful life of the systemssubsystems, specifically the electrical powertrain. In case of electric aircrafts, computing remaining flying time is safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.Our research approach is to develop a system level health monitoring safety indicator either to the pilotautopilot for the electric vehicles which runs estimation and prediction algorithms to estimate remaining useful life of the vehicle e.g. determine state-of-charge in batteries. Given models of the current and future system behavior, a general approach of model-based prognostics can be employed as a solution to the prediction problem and further for decision making.

Prognostics↗
NASA NTRS2018

Health Monitoring and Prognostics for More Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining flying time/distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of battery charge while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.

Goebel, Kai↗
NASA NTRS2019

Prognostics and Health Monitoring: Application to Electric Vehicles

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving-time distance (for cars) or flying time-distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle.

Electric Vehicles↗
NASA NTRS2015

Safety Related Considerations in Autonomy

In this talk I will describe NASA strategy and research efforts to provide safety assurance for increasingly autonomous systems used in aviation. In the near future, autonomy will play an important role in civil aviation, and its applications will range from vehicles and platforms (UAVs, transport-class, including supersonic to hypersonic, aircraft) to airspace operations, or health management systems. This infusion of autonomy is driven by a need for optimizing airspace operations to accommodate increasing traffic density (e.g., adaptive trajectory-based operations, autonomous tugs, close parallel runways, and dynamic separation assurance), reducing operation costs to ensure that US operators can compete with emergent countries, and enabling new business models (e.g., fire fighting, UAS-based package delivery and precise aerial photography). In essence virtually every component of the National Airspace System will become increasingly autonomous. Yet we need to do so in a safe manner and have techniques and processes in place to ensure the safety of the public. This talk describes NASA plans to address this problem

Air Traffic Management↗
NASA NTRSDate not supplied

Systems Health Management and Prognostics Approaches for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously published information.

Systems Health Managent↗
NASA NTRSDate not supplied

Health Monitoring and Prognostics for Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining driving flying time/distance for the flying vehicles. This information is important, particularly in the case of auto vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing the remaining flying time is also safety-critical, since an aircraft that runs out of power (battery charge) while in the air will eventually lose control leading to catastrophe. To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. A systematic prediction framework is implemented to identify all possible sources of uncertainty, quantify each of them individually, and mathematically estimate their combined effect on the system-level quantity of interest, in this case, the remaining flying time/distance of the unmanned aircraft. Note - This presentation contains all previously approved and published information.

Systems Health Managent↗
NASA NTRSDate not supplied

Gateway Integrated ECLSS Model Analysis of Intermodule Ventilation Failures

The Gateway Integrated ECLSS Model (GIEM) is a set of two independent models, made in Aspen Custom Modeler (ACM) and Thermal Desktop (TD), of the Gateway stack and each modules' associated environment control and life support system (ECLSS). The GIEM utilizes the 41-Node Metabolic Man (METMAN) to simulate crew inside the Gateway consuming O2 and producing CO2, H2O, and heat for the ECLSS to remove. The modules and visiting vehicles all have different levels of ECLSS capabilities and may rely on other modules and Intermodular Ventilation (IMV), which is used to exchange gas between the modules, to ensure the entire Gateway environment is controlled. If IMV were to fail or be turned off, some vehicles may have limited access to necessary ECLSS functions. This analysis focuses on evaluating O2, CO2, and H2O levels when crew are in vehicles without their own life support and IMV has failed to evaluate how long before the atmosphere is unsafe for the crew. This analysis uses the integrated nature of the GIEM to look at time to effect for O2 depletion and CO2 and H2O buildup when crew are working in vehicles that have been cut off from the ECLSS-active portion of the stack. This information is used to inform for how long crew can safely continue working in the ECLSS-inactive vehicles before experiencing a requirement violation or physical limitation and can be referenced when developing maintenance and emergency response concept of operations (ConOps).

Lawrence Barrett↗
NASA NTRSDate not supplied

Air Traffic Management Response to Vertiport Closure

Increasing quantities of urban air mobility operations requires consideration of various contingencies associated with novel operational tempos and airspace management practices. This paper explores a contingency scenario in which a single vertiport closes unexpectedly during high tempo operations. Dynamic re-planning is explored as a function performed both by operators using common operating procedures and through emergency assistance from air traffic control. Implications for airspace capacity, infrastructure requirements, and vehicle energy reserves are discussed.

UAM↗
NASA NTRSDate not supplied

Air Traffic Management Response to Vertiport Closure

Increasing quantities of urban air mobility operations requires consideration of various contingencies associated with novel operational tempos and airspace management practices. This paper explores a contingency scenario in which a single vertiport closes unexpectedly during high tempo operations. Dynamic re-planning is explored as a function performed both by operators using common operating procedures and through emergency assistance from air traffic control. Implications for airspace capacity, infrastructure requirements, and vehicle energy reserves are discussed.

UAM↗
NASA NTRS2019

SAFE50 Reference Design Study for Large-Scale High-Density Low-Altitude UAS Operations in Urban Areas

Enabling safe, routine, and high-density flight operations of small UAS at low-altitude over heavily populated urban centers presents a difficult challenge for emerging UAS Traffic Management (UTM) system concepts. Urban operations by definition involve flight over people, property, and infrastructure. Low-altitude urban environments - such as urban canyons – are one of the most difficult areas for UTM to consider. Mission concepts require routine operations in a cluttered radio-frequency (RF) environment with degraded or denied Global Positioning System (GPS) reception. Flights with any appreciable distance will be beyond visual and communications line-of-sight from ground operators. Timely detection and response to emergencies and onboard failures, which is critical for safe aircraft operation, will be difficult. This work seeks to establish a feasible reference autonomy architecture for autonomous vehicles in an urban UTM system, then verifying and validating this architecture within a complete UTM concept point-design and systems analysis study. In this paper, we present the results from the NASA SAFE50 conceptual design and systems study that investigates the trade-space of urban UTM operations. This advanced conceptual design study develops a feasible, verified, validated point-design solution. The SAFE50 point-design concept places emphasis on advanced, highly-autonomous, and highly-capable vehicles that favors intelligent onboard autonomy over direct human control with today's technologies and operating in today's urban environments. This paper focuses on an general overview of the design study, highlighting decisions made in the architectural solution. This paper will presents a summary of the study, architectures, and requirements. We present an overview of the architecture designs as derived from the top-level UTM system. The point-design has been implemented in both simulation and through flight testing of hardware design prototypes. The results from simulation and flight testing as part of the verification and validation process of the reference design study.

Ippolito, Corey A.↗
NASA NTRS2005

Overview of the Small Aircraft Transportation System Project Four Enabling Operating Capabilities

It has become evident that our commercial air transportation system is reaching its peak in terms of capacity, with numerous delays in the system and the demand still steadily increasing. NASA, FAA, and the National Consortium for Aviation Mobility (NCAM) have partnered to aid in increasing the mobility throughout the United States through the Small Aircraft Transportation System (SATS) project. The SATS project has been a five-year effort to provide the technical and economic basis for further national investment and policy decisions to support a small aircraft transportation system. The SATS vision is to enable people and goods to have the convenience of on-demand point-to-point travel, anywhere, anytime for both personal and business travel. This vision can be obtained by expanding near all-weather access to more than 3,400 small community airports that are currently under-utilized throughout the United States. SATS has focused its efforts on four key operating capabilities that have addressed new emerging technologies, procedures, and concepts to pave the way for small aircraft to operate in nearly all weather conditions at virtually any runway in the United States. These four key operating capabilities are: Higher Volume Operations at Non-Towered/Non-Radar Airports, En Route Procedures and Systems for Integrated Fleet Operations, Lower Landing Minimums at Minimally Equipped Landing Facilities, and Increased Single Pilot Performance. The SATS project culminated with the 2005 SATS Public Demonstration in Danville, Virginia on June 5th-7th, by showcasing the accomplishments achieved throughout the project and demonstrating that a small aircraft transportation system could be viable. The technologies, procedures, and concepts were successfully demonstrated to show that they were safe, effective, and affordable for small aircraft in near all weather conditions. The focus of this paper is to provide an overview of the technical and operational feasibility of the four operating capabilities, and explain how they can enable a small aircraft transportation system.

Viken, Sally A.↗
NASA NTRS2005

Overview of the Small Aircraft Transportation System Project Four Enabling Operating Capabilities

It has become evident that our commercial air transportation system is reaching its peak in terms of capacity, with numerous delays in the system and the demand still steadily increasing. NASA, FAA, and the National Consortium for Aviation Mobility (NCAM) have partnered to aid in increasing the mobility throughout the United States through the Small Aircraft Transportation System (SATS) project. The SATS project has been a five-year effort to provide the technical and economic basis for further national investment and policy decisions to support a small aircraft transportation system. The SATS vision is to enable people and goods to have the convenience of on-demand point-to-point travel, anywhere, anytime for both personal and business travel. This vision can be obtained by expanding near all-weather access to more than 3,400 small community airports that are currently under-utilized throughout the United States. SATS has focused its efforts on four key operating capabilities that have addressed new emerging technologies, procedures, and concepts to pave the way for small aircraft to operate in nearly all weather conditions at virtually any runway in the United States. These four key operating capabilities are: Higher Volume Operations at Non-Towered/Non-Radar Airports, En Route Procedures and Systems for Integrated Fleet Operations, Lower Landing Minimums at Minimally Equipped Landing Facilities, and Increased Single Pilot Performance. The SATS project culminated with the 2005 SATS Public Demonstration in Danville, Virginia on June 5th-7th, by showcasing the accomplishments achieved throughout the project and demonstrating that a small aircraft transportation system could be viable. The technologies, procedures, and concepts were successfully demonstrated to show that they were safe, effective, and affordable for small aircraft in near all weather conditions. The focus of this paper is to provide an overview of the technical and operational feasibility of the four operating capabilities, and explain how they can enable a small aircraft transportation system.

Viken, Sally A.↗
NASA NTRS1999

NASA Aircraft Vortex Spacing System Development Status

The National Aeronautics and Space Administration (NASA) is addressing airport capacity enhancements during instrument meteorological conditions through the Terminal Area Productivity (TAP) program. Within TAP, the Reduced Spacing Operations (RSO) subelement at the NASA Langley Research Center is developing an Aircraft VOrtex Spacing System (AVOSS). AVOSS will integrate the output of several systems to produce weather dependent, dynamic wake vortex spacing criteria. These systems provide current and predicted weather conditions, models of wake vortex transport and decay in these weather conditions, and real-time feedback of wake vortex behavior from sensors. The goal of the NASA program is to provide the research and development to demonstrate an engineering model AVOSS in real-time operation at a major airport. The demonstration is only of concept feasibility, and additional effort is required to deploy an operational system for actual aircraft spacing reduction. This paper describes the AVOSS system architecture, a wake vortex facility established at the Dallas-Fort Worth International Airport (DFW), initial operational experience with the AVOSS system, and emerging considerations for subsystem requirements. Results of the initial system operation suggest a significant potential for reduced spacing.

Hinton, David A.↗
NASA NTRS2012

Best Practices for the Application of Functional Near Infrared Spectroscopy to Operator State Sensing

Functional Near Infrared Spectroscopy (fNIRS) is an emerging neuronal measurement technique with many advantages for application in operational and training contexts. Instrumentation and protocol improvements, however, are required to obtain useful signals and produce expeditiously self-applicable, comfortable and unobtrusive headgear. Approaches for improving the validity and reliability of fNIRS data for the purpose of sensing the mental state of commercial aircraft operators are identified, and an exemplary system design for attentional state monitoring is outlined. Intelligent flight decks of the future can be responsive to state changes to optimally support human performance. Thus, the identification of cognitive performance decrement, such as lapses in operator attention, may be used to predict and avoid error-prone states. We propose that attentional performance may be monitored with fNIRS through the quantification of hemodynamic activations in cortical regions which are part of functionally-connected attention and resting state networks. Activations in these regions have been shown to correlate with behavioral performance and task engagement. These regions lie beneath superficial tissue in head regions beyond the forehead. Headgear development is key to reliably and robustly accessing locations beyond the hair line to measure functionally-connected networks across the whole head. Human subject trials using both fNIRS and functional Magnetic Resonance Imaging (fMRI) will be used to test this system. Data processing employs Support Vector Machines for state classification based on the fNIRS signals. If accurate state classification is achieved based on sensed activation patterns, fNIRS will be shown to be useful for monitoring attentional performance.

Harrivel, Angela R.↗
NASA NTRS2006

Human Factors Considerations for Performance-Based Navigation

A transition toward a performance-based navigation system is currently underway in both the United States and around the world. Performance-based navigation incorporates Area Navigation (RNAV) and Required Navigation Performance (RNP) procedures that do not rely on the location of ground-based navigation aids. These procedures offer significant benefits to both operators and air traffic managers. Under sponsorship from the Federal Aviation Administration (FAA), the National Aeronautics and Space Administration (NASA) has undertaken a project to document human factors issues that have emerged during RNAV and RNP operations and propose areas for further consideration. Issues were found to include aspects of air traffic control and airline procedures, aircraft systems, and procedure design. Major findings suggest the need for human factors-specific instrument procedure design guidelines. Ongoing industry and government activities to address air-ground communication terminology, procedure design improvements, and chart-database commonality are strongly encouraged.

Barhydt, Richard↗
NASA NTRS2018

A First Look at the Evolution of Flight Crew Requirements for Emerging Market Aircraft

This is an exciting time for aviation. New vehicle and airspace technologies promise large increases in the number of aircraft in operation. One critical technology for these emerging markets is the increased use of automated systems to reduce pilot skill, training, and proficiency requirements. While the use of these systems promises to reduce or eliminate pilot functions in the long-term, the technology development for the required functions will necessitate a phased transition. The transition to, and adoption of automated systems will generate new safety challenges. This paper is a first look at a model to help frame flight crew functions for evaluation of future operational requirements. The model is intended to provide required flight crew functions regardless of whether the functions are performed by human or artificial agent. It is hoped that the model will be useful in identifying safety challenges and enabling a safe transition for the new aviation markets. The paper presents some background for a model for framing the flight crew function model and some thoughts about next steps.

safety-critical automation↗