Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “alertness”

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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

UAS Integration in the NAS Project: Flight Test 3 Data Analysis of JADEM-Autoresolver Detect and Avoid System

The Unmanned Aircraft Systems Integration in the National Airspace System project, or UAS Integration in the NAS, aims to reduce technical barriers related to safety and operational challenges associated with enabling routine UAS access to the NAS. The UAS Integration in the NAS Project conducted a flight test activity, referred to as Flight Test 3 (FT3), involving several Detect-and-Avoid (DAA) research prototype systems between June 15, 2015 and August 12, 2015 at the Armstrong Flight Research Center (AFRC). This report documents the flight testing and analysis results for the NASA Ames-developed JADEM-Autoresolver DAA system, referred to as 'Autoresolver' herein. Four flight test days (June 17, 18, 22, and July 22) were dedicated to Autoresolver testing. The objectives of this test were as follows: 1. Validate CPA prediction accuracy and detect-and-avoid (DAA, formerly known as self-separation) alerting logic in realistic flight conditions. 2. Validate DAA trajectory model including maneuvers. 3. Evaluate TCAS/DAA interoperability. 4. Inform final Minimum Operating Performance Standards (MOPS). Flight test scenarios were designed to collect data to directly address the objectives 1-3. Objective 4, inform final MOPS, was a general objective applicable to the UAS in the NAS project as a whole, of which flight test is a subset. This report presents analysis results completed in support of the UAS in the NAS project FT3 data review conducted on October 20, 2015. Due to time constraints and, to a lesser extent, TCAS data collection issues, objective 3 was not evaluated in this analysis.

flight test↗
NASA NTRS2017

Real-Time Radar-Based Tracking and State Estimation of Multiple Non-Conformant Aircraft

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.

RADAR tracking↗
NASA NTRS1972

Apollo 16 Press Kit

The Apollo 16 spacecraft is scheduled for launch on Apr. 16, 1972 from Complex 39A at the Kennedy Space Center, Florida by the Saturn V launch vehicle. Crewmen are mission commander John W. Young, command module pilot Thomas K. Mattingly II and lunar module pilot Charles M. Duke Jr. Objectives of the mission, to last up to 12 days, as outlined by NASA: to perform selenological inspection, survey and sampling of materials in a preselected region of Descartes using a lunar roving' vehicle; deploy and activate Apollo surface experiments; develop man's capability to work in the lunar environment; obtain photographs of candidate exploration sites; and toconduct inflight experiments and photographic tasks in lunar orbit. Following launch, the spacecraft will reach Earth Parking Orbit and remain in orbit for about two and one-half revolutions prior to Translunar Injection. Next, the Command and Service Module docks with the Lunar Module and the spacecraft "coasts" to the moon. In orbit around the moon, the Command and Service Module/Lunar Module combination will descend to within 50,000 feet of the lunar surface before undocking. The Lunar Module will continue to descend while the Command and Service Module returns to an orbit approximately 60 miles high. Stay time on the lunar surface is scheduled for approximately 73 hours. The ascent stage of the Lunar Module then lifts the astronauts back into lunar orbit where they will dock with the Command/Service Module. The Lunar Module is jettisoned and Transearth Injection follows. Just prior to reentry into the earth's atmosphere, the Service Module is jettisoned, and the astronauts in the Command Module splashdown in the Pacific Ocean. The target point for end-of-mission splashdown is at 05 degrees 0 minutes north latitude and 158 degrees 40 minutes west longitude or approximately 985 nautical miles south of Honolulu, Hawaii. Splashdown is scheduled for Apr. 28, 1972 at 10:30 a.m. Hawaiian Standard Time (2:30 p.m. CST). Recovery forces for Apollo 16, stationed in both the Atlantic and Pacific Oceans, will consist of three ships, nine aircraft and nearly 1,700 personnel. CTF-130 (Manned Spacecraft Recovery Force, Pacific) forces will be stationed south of Hawaii. Three ships, eight helicopters and three Air Force HC-130H aircraft, and nearly 1,100 personnel, will take part. Task Force 140 (Manned Spacecraft Recovery Force, Atlantic), comprising one ship, six HC-130H aircraft, three helicopters and approximately 300 personnel, will be positioned for possible launch abort operations. Two ships in the Atlantic will also be used for acoustical testing. Other forces, primarily aircraft and personnel of the Air Force Aerospace Rescue and Recovery Service will be on alert around the world for contingency recovery support.

Source record↗
NASA NTRS2016

Modeling the Swift Bat Trigger Algorithm with Machine Learning

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.

gamma-ray burst: general – gamma-rays: general â↗
NASA NTRS2017

FIM Avionics Operations Manual

This document describes the operation and use of the Flight Interval Management (FIM) Application installed on an electronic flight bag (EFB). Specifically, this document includes: 1) screen layouts for each page of the interface; 2) step-by-step instructions for data entry, data verification, and input error correction; 3) algorithm state messages and error condition alerting messages; 4) aircraft speed guidance and deviation indications; and 5) graphical display of the spatial relationships between the Ownship aircraft and the Target aircraft.

Alves, Erin E.↗
NASA NTRS2017

Operational Overview for UAS Integration in the NAS Project Flight Test Series 3

The National Aeronautics and Space Administration Unmanned Aircraft Systems Integration in the National Airspace System Project has conducted a series of flight tests intended to support the reduction of barriers that prevent unmanned aircraft from flying without the required waivers from the Federal Aviation Administration. The 2015 Flight Test Series 3, supported two separate test configurations. The first configuration investigated the timing of Detect and Avoid alerting thresholds using a radar equipped unmanned vehicle and multiple live intruders flown at varying encounter geometries. The second configuration included a surrogate unmanned vehicle (flown from a ground control station, with a safety pilot on board) flying a mission in a virtual air traffic control airspace sector using research pilot displays and Detect and Avoid advisories to maintain separation from live and virtual aircraft. The test was conducted over an eight-week span within the R-2508 Special Use Airspace. Over 200 encounters were flown for the first configuration, and although the second configuration was cancelled after three data collection flights, Flight Test 3 proved to be invaluable for the purposes of planning, managing, and execution of this type of integrated flight test.

unmanned aircraft systems↗
NASA NTRS2016

A Very Bright, Very Hot, and Very Long Flaring Event from the M Dwarf Binary System DG CVn

On 2014 April 23, the Swift satellite responded to a hard X-ray transient detected by its Burst Alert Telescope, which turned out to be a stellar flare from a nearby, young M dwarf binary DG CVn. We utilize observations at X-ray, UV, optical, and radio wavelengths to infer the properties of two large flares. The X-ray spectrum of the primary outburst can be described over the 0.3100 kiloelectron volts bandpass by either a single very high-temperature plasma or a nonthermal thick-target bremsstrahlung model, and we rule out the nonthermal model based on energetic grounds. The temperatures were the highest seen spectroscopically in a stellar flare, at T(sub x) of 290 megakelvin. The first event was followed by a comparably energetic event almost a day later. We constrain the photospheric area involved in each of the two flares to be greater than 10(exp 20) sq cm, and find evidence from flux ratios in the second event of contributions to the white light flare emission in addition to the usual hot, T approximately 10(exp 4) K blackbody emission seen in the impulsive phase of flares. The radiated energy in X-rays and white light reveal these events to be the two most energetic X-ray flares observed from an M dwarf, with X-ray radiated energies in the 0.3-10 kiloelectron volts bandpass of 4 x 10(exp 35) and 9 x 10(exp 35) erg, and optical flare energies at E(sub V) of 2.8 x 10(exp 34) and 5.2 x 10(exp 34) erg, respectively. The results presented here should be integrated into updated modeling of the astrophysical impact of large stellar flares on close-in exoplanetary atmospheres.

stars: coronae – stars: flare – stars: individ↗
NASA NTRS2014

Human-Automation Cooperation for Separation Assurance in Future NextGen Environments

A 2012 Human-In-The-Loop air traffic control simulation investigated a gradual paradigm-shift in the allocation of functions between operators and automation. Air traffic controllers staffed five adjacent high-altitude en route sectors, and during the course of a two-week experiment, worked traffic under different function-allocation approaches aligned with four increasingly mature NextGen operational environments. These NextGen time-frames ranged from near current-day operations to nearly fully-automated control, in which the ground systems automation was responsible for detecting conflicts, issuing strategic and tactical resolutions, and alerting the controller to exceptional circumstances. Results indicate that overall performance was best in the most automated NextGen environment. Safe operations were achieved in this environment for twice todays peak airspace capacity, while being rated by the controllers as highly acceptable. However, results show that sector operations were not always safe; separation violations did in fact occur. This paper will describe in detail the simulation conducted, as well discuss important results and their implications.

air traffic control↗
NASA NTRS2017

GPM V05 Gridded Text Products

This presentation will summarize the changes in the products for the GPM V05 reprocessing cycle. It will concentrate on discussing the gridded text product from the core satellite retrievals. However, all aspects of the GPROF GMI changes in this product are equally appropriate to the other two gridded text products. The GPM mission reprocessed its products in May of 2017 as part of a continuing improvement of precipitation retrievals. This lead to important improvement in the retrievals and therefore also necessitated reprocessing the gridded test products. The V05 GPROF changes not only improved the retrievals but substantially alerted the format and this compelled changes to the gridded text products. Especially important in this regard is the GPROF2017 (used in V05) change from reporting the fraction of the total precipitation rate that occurring as convection or in liquid phase. Instead, GPROF2017, and therefore V05 gridded text products, report the rate of convective precipitation in mm/hr. The GPROF2017 algorithm now reports the frozen precipitation rate in mm/hr rather than the fraction of total precipitation that is liquid. Because of the aim of the gridded text product is to remain simple the radar and combined results will also change in V05 to reflect this change in the GMI retrieval. The presentation provides an analysis of these changes as well as presenting a comparison with the swath products from which the hourly text grids were derived.

PPS↗
NASA NTRS2017

Validation of Fatigue Modeling Predictions in Aviation Operations

Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.

operational safety↗
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 NTRS2017

Validation of Minimum Display Requirements for a UAS Detect and Avoid System

The full integration of Unmanned Aircraft Systems (UAS) into the National Airspace System (NAS), a prerequisite for enabling a broad range of public and commercial UAS operations, presents several technical challenges to UAS developers, operators and regulators. A primary barrier is the inability for UAS pilots (situated at a ground control station, or GCS) to comply with Title 14 Code of Federal Regulations sections 91.111 and 91.113, which require pilots to see and avoid other aircraft in order to maintain well clear. The present study is the final in a series of human-in-the-loop experiments designed to explore and test the various display and alerting requirements being incorporated into the minimum operational performance standards (MOPS) for a UAS-specific detect and avoid system that would replace the see and avoid function required of manned aircraft. Two display configurations were tested - an integrated display and a standalone display - and their impact on pilot response times and ability to maintain DAA well clear were compared. Results indicated that the current draft of the MOPS result in high-level performance and did not meaningfully differ by display configuration.

unmanned aircraft systems↗
NASA NTRS2017

Evolution of a Simulation Testbed into an Operational Tool

This paper describes the evolution over a 20-year period of the Future ATM (Air Traffic Management) Concepts Evaluation Tool (FACET) from a National Airspace System (NAS) based simulation testbed into an operational tool. FACET was developed as a testbed for assessing futuristic ATM concepts, e.g., automated conflict detection and resolution. NAS Constraint Evaluation and Notification Tool (NASCENT) is an application, within FACET, for alerting airspace users of inefficiencies in flight operations and advising time- and fuel-saving reroutes.It is currently in use at American Airlines Integrated Operations Center in Fort Worth, TX. The concepts assessed,research conducted, and the operational capability developed, along with the NASA support and achievements are presented in this paper.

Sheth, Kapil↗
NASA NTRS2017

Evolution of A Distributed Live, Virtual, Constructive Environment for Human in the Loop Unmanned Aircraft Testing

NASA's Unmanned Aircraft Systems Integration in the National Airspace System Project is conducting human in the loop simulations and flight testing intended to reduce barriers associated with enabling routine airspace access for unmanned aircraft. The primary focus of these tests is interaction of the unmanned aircraft pilot with the display of detect and avoid alerting and guidance information. The projects integrated test and evaluation team was charged with developing the test infrastructure. As with any development effort, compromises in the underlying system architecture and design were made to allow for the rapid prototyping and open-ended nature of the research. In order to accommodate these design choices, a distributed test environment was developed incorporating Live, Virtual, Constructive, (LVC) concepts. The LVC components form the core infrastructure support simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing assets distributed across multiple NASA Centers. Using standard LVC concepts enable future integration with existing simulation infrastructure.

UAS in the NA↗
NASA NTRS2017

Evolution of A Distributed Live, Virtual, Constructive Environment for Human in the Loop Unmanned Aircraft Testing

NASA's Unmanned Aircraft Systems Integration in the National Airspace System Project is conducting human in the loop simulations and flight testing intended to reduce barriers associated with enabling routine airspace access for unmanned aircraft. The primary focus of these tests is interaction of the unmanned aircraft pilot with the display of detect and avoid alerting and guidance information. The project's integrated test and evaluation team was charged with developing the test infrastructure. As with any development effort, compromises in the underlying system architecture and design were made to allow for the rapid prototyping and open-ended nature of the research. In order to accommodate these design choices, a distributed test environment was developed incorporating Live, Virtual, Constructive, (LVC) concepts. The LVC components form the core infrastructure support simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This LVC infrastructure enables efficient testing by leveraging the use of existing assets distributed across multiple NASA Centers. Using standard LVC concepts enable future integration with existing simulation infrastructure.

LVC↗
NASA NTRS2017

Ensuring Interoperability between UAS Detect-and-Avoid and Manned Aircraft Collision Avoidance

The UAS community in the United States has identified the need for a collision avoidance region in which UAS Detect-and-Avoid (DAA) vertical guidance is restricted to preclude interoperability issues with manned aircraft collision avoidance system vertical resolution advisories (RAs). This paper documents the process by which the collision avoidance region was defined. Three candidate definitions were evaluated on 1.3 million simulated pairwise encounters between UAS and manned aircraft covering a wide range of horizontal and vertical closure rates, angles, and miss distances. They were evaluated with regard to UAS DAA interoperability with manned aircraft collision avoidance systems in terms of: 1) the primary objective of restricting DAA vertical guidance before RAs when the aircraft are close, and 2) the secondary objective of avoiding unnecessary restrictions of DAA vertical guidance at a DAA alert when the aircraft are further apart. The collision avoidance region definition that fully achieves the primary objective and best achieves the secondary objective was recommended to and accepted by the UAS community in the United States. By this definition, UAS and manned aircraft are in the collision avoidance region--during which DAA vertical guidance is restricted--when the time to closest point of approach is less than 50 seconds and either the time to co-altitude is less than 50 seconds or the current vertical separation is less than 800 feet.

collision avoidance↗
NASA NTRS2017

Oculomotor Assessment of Diurnal Arousal Variations

Saccadic and pupillary responses are reliable indices of arousal decrement (e.g. fatigue), that might be exploited to improve work schedule guidelines. In this study, we tested the sensitivity of a short 30-s oculomotor test to detect diurnal arousal variations. Twelve participants (5 females, 7 males, 37.7+-11.9 years) volunteered to be assessed every hour (66+-20 min) for three consecutive working days, during their regular office-hours. We used a fully automated testing system, the FIT 2000 Fitness Impairment Tester (Pulse Medical Instruments Inc., Rockville, MD, USA), to measure and record saccadic peak velocity, pupil diameter, and latency and amplitude of the pupillary light reflex. In addition, we collected subjective levels of arousal using the Stanford Sleepiness Scale, and body core temperature. We analyzed the data using a linear mixed model approach for longitudinal data. Both saccadic velocity and subjective alertness decreased over the course of a day, while body core temperature increased (all p-values.05). The data also weakly suggested an increase of the pupil diameter (p 07). The findings support the use of oculomotor indices in the assessment of arousal and fatigue in applied settings.

saccadic velocity↗
NASA NTRS2017

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Unmanned Aircraft Detect-And-Avoid Systems

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is generic since it was designed without any assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Therefore, GRACE was adopted as a core component of the Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling, developed by NASA as a research and modeling tool for Unmanned Aerial Systems Integration in the National Airspace System (NAS). GRACE has been used in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Unmanned Aircraft↗