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At least 217 records · Page 12

All sources NASA NTRS DOE OSTI

NASA NTRS1992

Crew decision making under stress

Flight crews must make decisions and take action when systems fail or emergencies arise during flight. These situations may involve high stress. Full-missiion flight simulation studies have shown that crews differ in how effectively they cope in these circumstances, judged by operational errors and crew coordination. The present study analyzed the problem solving and decision making strategies used by crews led by captains fitting three different personality profiles. Our goal was to identify more and less effective strategies that could serve as the basis for crew selection or training. Methods: Twelve 3-member B-727 crews flew a 5-leg mission simulated flight over 1 1/2 days. Two legs included 4 abnormal events that required decisions during high workload periods. Transcripts of videotapes were analyzed to describe decision making strategies. Crew performance (errors and coordination) was judged on-line and from videotapes by check airmen. Results: Based on a median split of crew performance errors, analyses to date indicate a difference in general strategy between crews who make more or less errors. Higher performance crews showed greater situational awareness - they responded quickly to cues and interpreted them appropriately. They requested more decision relevant information and took into account more constraints. Lower performing crews showed poorer situational awareness, planning, constraint sensitivity, and coordination. The major difference between higher and lower performing crews was that poorer crews made quick decisions and then collected information to confirm their decision. Conclusion: Differences in overall crew performance were associated with differences in situational awareness, information management, and decision strategy. Captain personality profiles were associated with these differences, a finding with implications for crew selection and training.

Orasanu, J.↗

NASA NTRSDate not supplied

Initial testing of the Uncrewed Aerial System Pilot Kit (UASP-kit) in Operational Settings

Pilots for small uncrewed aerial systems (sUAS) are at a disadvantage for building situation awareness of the remote airspace in which they are flying, simply because they are distant from their vehicles. A tool to provide increased air traffic situation awareness for an sUAS pilot is being developed. The UAS pilot kit, “UASP-kit,” is small and self-contained, with its chief capability being to collect and display Automatic Dependent Surveillance-Broadcast reports of local aircraft. UASP-kits were taken into the field, introduced to users during a training course, and then left with them for the summer fire season. sUAS pilots used the prototypes when it was appropriate throughout the summer. The UASP-kits were operational for a total of 79 flight-days. Users reported that the UASP-kits supported their situation awareness but also identified several usability issues. The findings contribute to validation of the UASP-kit, and support continuing the work to improve the tool and develop additional functionality.

STEReO↗

NASA NTRSDate not supplied

Initial testing of the Uncrewed Aerial System Pilot Kit (UASP-kit) in Operational Settings

Remote pilots for small uncrewed aerial systems (sUAS) are at a disadvantage for building situation awareness of the airspace in which they are flying, because of limited sight lines and distance from cell towers. A tool to provide increased air traffic situation awareness for an sUAS pilot is being developed. The “UASP-kit” is small and self-contained, with its chief capability being to collect and display Automatic Dependent Surveillance-Broadcast reports of local aircraft. UASP-kits were taken into the field, introduced to users during a training course, and then left with them for the summer fire season. sUAS pilots used the prototypes when it was appropriate throughout the summer. The UASP-kits were operational for a total of 79 flight-days. Users noted that the UASP-kits supported their situation awareness but also identified several usability issues. The findings contribute to validation of the UASP-kit, and support continuing the work to improve the tool and develop additional functionality.

STEReO↗

NASA NTRS2013

Pilot Subjective Assessments During an Investigation of Separation Function Allocation Using a Human-In-The-Loop Simulation

Two human-in-the-loop simulation experiments were conducted to investigate allocation of separation assurance functions between ground and air and between humans and automation. The experiments modeled a mixed-operations concept in which aircraft receiving ground-based separation services shared the airspace with aircraft providing their own separation service (i.e., self-separation). The two experiments, one pilot-focused and the other controller-focused, addressed selected key issues of mixed operations and modeling an emergence of NextGen technologies and procedures. This paper focuses on the results of the subjective assessments of pilots collected during the pilot-focused human-in-the-loop simulation, specifically workload and situation awareness. Generally the results revealed that across all conditions, pilots' perceived workload was low to medium, with the highest reported levels of workload occurring when the pilots experienced a loss of separation during the scenario. Furthermore, the results from the workload data and situation awareness data were complimentary such that when pilots reported lower levels of workload they also experienced higher levels of situation awareness.

Burke, Kelly A.↗

NASA NTRS1994

Operator modeling in commerical aviation: Cognitive models, intelligent displays, and pilot's assistants

One of the goals of the National Aviation Safety/Automation program is to address the issue of human-centered automation in the cockpit. Human-centered automation is automation that, in the cockpit, enhances or assists the crew rather than replacing them. The Georgia Tech research program focused on this general theme, with emphasis on designing a computer-based pilot's assistant, intelligent (i.e, context-sensitive) displays, and an intelligent tutoring system for understanding and operating the autoflight system. In particular, the aids and displays were designed to enhance the crew's situational awareness of the current state of the automated flight systems and to assist the crew's situational awareness of the current state of the automated flight systems and to assist the crew in coordinating the autoflight system resources. The activities of this grant included: (1) an OFMspert to understand pilot navigation activities in a 727 class aircraft; (2) an extension of OFMspert to understand mode control in a glass cockpit, Georgia Tech Crew Activity Tracking System (GT-CATS); (3) the design of a training system to teach pilots about the vertical navigation portion of the flight management system -VNAV Tutor; and (4) a proof-of-concept display, using existing display technology, to facilitate mode awareness, particularly in situations in which controlled flight into terrain (CFIT) is a potential.

Govindaraj, T.↗

NASA NTRS2018

Design, Development, and Flight Evaluation of Pilot Displays and Long-Track Control for Wake Surfing Applications

From April to May 2017, the National Aeronautics and Space Administration Armstrong Flight Research Center completed a series of flights with a trail C-20A airplane surfing in the wake of a Gulfstream III airplane using a commercially available datalink as the primary communication between the two aircraft. The purpose of this test was to characterize the aerodynamic benefits received by the trail airplane flying in the upwash portion of the wake generated from the lead airplane. Lateral and vertical relative position to the wake were automatically controlled through an experimental programmable autopilot on the C-20A airplane. Long-track, the separation distance between the two aircraft, was maintained by test pilots managing throttle position using customized cockpit displays. These displays provided the pilots with throttle cues for maintaining long-track position and situational awareness of the wake vortex relative to the position of the trail airplane. Flight testing demonstrated the ability of the pilots to use these displays to maintain a safe long-track distance, but found there to be trades between tracking performance and the frequency of throttle motion. The wake awareness display provided the pilots with adequate situational awareness of the wake vortex during the flight experiment. This paper presents a summary of the design, development, and flight evaluation of the pilot displays and long-track control.

aircraft↗

NASA NTRS1999

An Experimental Study of the Effect of Shared Information on Pilot/Controller Re-Route Negotiation

Air-ground data link systems are being developed to enable pilots and air traffic controllers to share information more fully. The sharing of information is generally expected to enhance their shared situation awareness and foster more collaborative decision making. An exploratory, part-task simulator experiment is described which evaluates the extent to which shared information may lead pilots and controllers to cooperate or compete when negotiating route amendments. The results indicate an improvement in situation awareness for pilots and controllers and a willingness to work cooperatively. Independent of data link considerations, the experiment also demonstrates the value of providing controllers with a good-quality weather representation on their plan view displays. Observed improvements in situation awareness and separation assurance are discussed. It is argued that deployment of this relatively simple, low-risk addition to the plan view displays be accelerated.

Farley, Todd C.↗

DOE OSTI2022

Systems and Implementation: Integrating Unmanned Aircraft Systems into Physical Protection Systems at Fixed Sites and During Transportation

Physical protection systems, and response forces in particular, are designed to prevent an adversary from successfully completing a malevolent act against a facility or transport operations. Timely detection and assessment of any potential adversary action against a target is an essential element of materials security. The timely detection and assessment must then be followed-up by a capable and timely response that might be enhanced with the additional situational awareness provided by unmanned aircraft systems (UAS). The United States Department of Energy’s National Nuclear Security Administration Office of International Nuclear Security has been exploring capabilities provided by UAS to support response force operations within the physical protection system. UAS have the potential to provide response force commanders and operators with situational awareness in assessing adversary locations and actions as well as the locations of responders. UAS may be utilized for area searches ahead of responder pathways to identify potential threats and to provide situational awareness of areas not normally covered by cameras (such as areas outside the fence line outside at fixed facilities). In addition, UAS can provide real-time information to transportation convoy teams that pass through constantly changing public access environments. This paper will provide operational recommendations to be addressed when integrating UAS into existing physical protection systems at fixed sites and during transport. Recommendations will include aspects of the following: needs analysis; tactics and techniques to support detection and assessment as well as response force deployment; remote pilot selection, qualifications, training, and currency; UAS selection criteria; UAS laws and regulations; possible cost sharing with other facility operations; and on-scene emergency management.

Stockwell, Brandon↗

NASA NTRS2017

Real Time Metrics and Analysis of Integrated Arrival, Departure, and Surface Operations

To address the Integrated Arrival, Departure, and Surface (IADS) challenge, NASA is developing and demonstrating trajectory-based departure automation under a collaborative effort with the FAA and industry known Airspace Technology Demonstration 2 (ATD-2). ATD-2 builds upon and integrates previous NASA research capabilities that include the Spot and Runway Departure Advisor (SARDA), the Precision Departure Release Capability (PDRC), and the Terminal Sequencing and Spacing (TSAS) capability. As trajectory-based departure scheduling and collaborative decision making tools are introduced in order to reduce delays and uncertainties in taxi and climb operations across the National Airspace System, users of the tools across a number of roles benefit from a real time system that enables common situational awareness. A real time dashboard was developed to inform and present users notifications and integrated information regarding airport surface operations. The dashboard is a supplement to capabilities and tools that incorporate arrival, departure, and surface air-traffic operations concepts in a NextGen environment. In addition to shared situational awareness, the dashboard offers the ability to compute real time metrics and analysis to inform users about capacity, predictability, and efficiency of the system as a whole. This paper describes the architecture of the real time dashboard as well as an initial proposed set of metrics. The potential impact of the real time dashboard is studied at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport (CLT). The architecture of implementing such a tool as well as potential uses are presented for operations at CLT. Metrics computed in real time illustrate the opportunity to provide common situational awareness and inform users of system delay, throughput, taxi time, and airport capacity. In addition, common awareness of delays and the impact of takeoff and departure restrictions stemming from traffic flow management initiatives are explored. The potential of the real time tool to inform users of the predictability and efficiency of using a trajectory-based departure scheduling system is also discussed.

integrated arrival departure surface operations↗

NASA NTRS2018

Operational Impact of the Baseline Integrated Arrival, Departure and Surface System Field Demonstration

To address the Integrated Arrival, Departure, and Surface (IADS) challenge, NASA is developing and demonstrating trajectory-based departure automation under a collaborative effort with the FAA (Federal Aviation Administration) and industry known Airspace Technology Demonstration 2 (ATD-2). ATD-2 builds upon and integrates previous NASA research capabilities that include the Spot and Runway Departure Advisor (SARDA), the Precision Departure Release Capability (PDRC), and the Terminal Sequencing and Spacing (TSAS) capability. As trajectory-based departure scheduling and collaborative decision making tools are introduced in order to reduce delays and uncertainties in taxi and climb operations across the National Airspace System, users of the tools across a number of roles benefit from a real time system that enables common situational awareness. A real time dashboard was developed to inform and present users notifications and integrated information regarding airport surface operations. The dashboard is a supplement to capabilities and tools that incorporate arrival, departure, and surface air-traffic operations concepts in a NextGen environment. In addition to shared situational awareness, the dashboard offers the ability to compute real time metrics and analysis to inform users about capacity, predictability, and efficiency of the system as a whole. This paper describes the architecture of the real time dashboard as well as an initial proposed set of metrics. The potential impact of the real time dashboard is studied at the site identified for initial deployment and demonstration in 2017: Charlotte-Douglas International Airport (CLT). The architecture of implementing such a tool as well as potential uses are presented for operations at CLT. Metrics computed in real time illustrate the opportunity to provide common situational awareness and inform users of system delay, throughput, taxi time, and airport capacity. In addition, common awareness of delays and the impact of takeoff and departure restrictions stemming from traffic flow management initiatives are explored. The potential of the real time tool to inform users of the predictability and efficiency of using a trajectory-based departure scheduling system is also discussed.

air traffic optimization↗

NASA NTRSDate not supplied

Developing an Unmanned Aircraft System Pilot (UASP-kit) for Wildland Fire UAS Operators

The use of unmanned aircraft systems (UAS) to support disaster management is becoming more widespread. Uninhibited by many of the operational constraints imposed on crewed aircraft, UAS have the potential to augment the aerial response in a number of ways, including supporting situation awareness, thereby accelerating disaster management efforts. The intent of the Scalable Traffic management for Emergency Response Operations (STEReO) activity is to build an ecosystem that will allow small UAS (sUAS) operations to fly safely in the airspace during disaster management events. The first step toward this goal is to develop a small, single-user tool (the UASP-kit) that will enhance an sUAS pilot’s situation awareness of the airspace. User needs for the functions and features of this tool were collected during a walkthrough over multiple days with subject matter experts from the US Forest Service. A prototype of the UASP-kit was then taken on an extended demonstration to multiple users, who were able to interact with it. User feedback was largely positive and users discussed the information they needed to support situation awareness in context. The demonstration also highlighted features that were unintuitive to use, and functions that need to be modified to make the kit more user-friendly and robust.

STEReO↗

NASA NTRSDate not supplied

Developing an Unmanned Aircraft System Pilot Kit (UASP-kit) for Wildland Fire UAS Operators

STEReO↗

DOE OSTI2021

Accessibility and Availability of Gas Stations for Liquid Fuel Supply During Severe Weather Events

Energy infrastructure is a pivotal lifeline with wider inter-dependencies. Electric power is essential to the continued functioning of critical assets; liquid fuels and other physical primary energy commodities that are used to supply the local energy needs of these assets during extreme events when electricity infrastructure is compromised. During Superstorm Sandy, the disruption of fuel supplies in the Tri-state area necessitated increasing situational awareness about oil and natural gas (ONG) systems to redirect fuel supplies to first responders and for restoration efforts. During winter storm events over the past several years, the demand for ONG for heating and power generation has skyrocketed in the north-east, which subsequently had caused ONG price to hit their highest level in years. ONG is a major energy source for restoration and recovery activities. However, very little, if any, situational awareness information about ONG supply and demand is available to those coordinating emergency efforts. Often this lack of information is due to the decentralized nature of the ONG supply chain and proprietary data maintained by private entities. To understand the impact of fuel availability on response and recovery efforts, this research focuses on (i) determining the accessibility to gas stations for liquid fuel supply across space and time, and (ii) examining the spatio-temporal variation of fuel availability and demand during extreme weather events. The broader scope of the project focuses on providing spatio-temporal situational awareness information about ONG availability and demand across the fuel supply chain for positioning of resources for response activities.

Kar, Bandana↗

NASA NTRS2004

A Comparison of a Brain-Based Adaptive System and a Manual Adaptable System for Invoking Automation

Two experiments are presented that examine alternative methods for invoking automation. In each experiment, participants were asked to perform simultaneously a monitoring task and a resource management task as well as a tracking task that changed between automatic and manual modes. The monitoring task required participants to detect failures of an automated system to correct aberrant conditions under either high or low system reliability. Performance on each task was assessed as well as situation awareness and subjective workload. In the first experiment, half of the participants worked with a brain-based system that used their EEG signals to switch the tracking task between automatic and manual modes. The remaining participants were yoked to participants from the adaptive condition and received the same schedule of mode switches, but their EEG had no effect on the automation. Within each group, half of the participants were assigned to either the low or high reliability monitoring task. In addition, within each combination of automation invocation and system reliability, participants were separated into high and low complacency potential groups. The results revealed no significant effects of automation invocation on the performance measures; however, the high complacency individuals demonstrated better situation awareness when working with the adaptive automation system. The second experiment was the same as the first with one important exception. Automation was invoked manually. Thus, half of the participants pressed a button to invoke automation for 10 s. The remaining participants were yoked to participants from the adaptable condition and received the same schedule of mode switches, but they had no control over the automation. The results showed that participants who could invoke automation performed more poorly on the resource management task and reported higher levels of subjective workload. Further, those who invoked automation more frequently performed more poorly on the tracking task and reported higher levels of subjective workload. and the adaptable condition in the second experiment revealed only one significant difference: the subjective workload was higher in the adaptable condition. Overall, the results show that a brain-based, adaptive automation system may facilitate situation awareness for those individuals who are more complacent toward automation. By contrast, requiring operators to invoke automation manually may have some detrimental impact on performance but does appear to increases subjective workload relative to an adaptive system.

Bailey, Nathan R.↗

NASA NTRSDate not supplied

A Queuing Theory Approach to Pilot-Controller Coordination for m:N Operations

In recent years, attention and interest by industry and researchers has grown in a control paradigm for remotely piloted aircraft termed “m:N operations.” In an m:N operation, a team of m remote pilots in command (RIPCs) collaboratively manage the flights of N aircraft. A consequence of an m:N concept of operations is that the RPICs will have to switch attention from one aircraft to another and from one task to another. Previous research in m:N operations has focused on the workload experienced by an RPIC and their level of situation awareness on their flights. Researchers have found that RPIC workload and situation awareness are generally sensitive to increasing N, although NASA’s Multi-Vehicle (m:N) Working Group has suggested that the driver of workload/situation awareness is the number of exceptions requiring human intervention as opposed to the value of N itself. In any case, a natural antecedent of workload is task load. In this paper, queueing theory is applied to a 1:N Urban Air Mobility (UAM) air taxi operation in order to estimate pilot task load for managing radio communications with air traffic controllers (ATCs) under increasing N. An M/M/1 queueing system is used to model the RIPC’s servicing of calls and clearance requests (e.g., departure, arrival, or airspace transition) to ATC for the N aircraft. Important parameters for the queueing model are the task arrival rate and the average service time for task completion. Radio communication times from past human-in-the-loop simulation studies are used to measure service times for a 1:4 and 1:12 UAM operation and to interpolate service times for 4 < N < 12. A Monte Carlo method is then employed, using the measured and interpolated service times, to estimate arrival rate and related queueing statistics. The paper concludes by considering the estimated queuing statistics, particularly the RPIC’s utilization (i.e., proportion of time actively servicing tasks), the length of the task queue over time, and the implications for task-balanced system design.

task load↗

DOE OSTI2026

Community Resilience Through Rapid Restoration Leveraging Distributed Energy Resources (DERs) and Low-Cost Sensors

Equitable and automated bottoms-up power restoration following an extreme event will be demonstrated at a site in Puerto Rico. To do so, the team will develop enhanced grid situational awareness techniques integrating behind-the-meter (BTM) distributed energy resources (DER) discovery, impedance sweeping based outage boundary detection, and feasible restoration path identification algorithms. Resilience metric will be developed and incorporated along with situational awareness information in a distributed Model Predictive Control (MPC)-based restoration optimization algorithm to control and mobilize grid assets. These algorithms will be validated through power hardware-in-the-loop experiments and ultimately, a site demonstration to show that outage recovery time and total recovered load could be improved by >20% over the baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Model-Free State Estimation Using Low-Rank Canonical Polyadic Decomposition

As electric grids experience high penetration levels of renewable generation, fundamental changes are required to address real-time situational awareness. Here, we utilize unique traits of tensors to devise a model-free situational awareness and energy forecasting framework for distribution networks. This work formulates the state of the network at multiple time instants as a three-way tensor; hence, recovering full state information of the network is tantamount to estimating all the values of the tensor. Given measurements received from µphasor measurement units and/or smart meters, the recovery of unobserved quantities is carried out using the low-rank canonical polyadic decomposition of the state tensor—that is, the state estimation task is posed as a tensor imputation problem utilizing observed patterns in measured (sampled) quantities. Two structured sampling schemes are considered, namely, asynchronous slab and fiber sampling. For both schemes, we present sufficient conditions on the number of sampled slabs and fibers that guarantee identifiability of the factors of the state tensor. Numerical results demonstrate the ability of the proposed framework to achieve high estimation accuracy in multiple sampling scenarios.

42 ENGINEERING↗

DOE OSTI2020

Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning

Power system frequency disturbances are caused by various generation and transmission events including generator trips, load disconnections, line trips, etc. Accurate detections of the events are crucial to bulk power system situation awareness and event investigation. This paper utilizes the recent advances of deep learning to build a convolutional neural network model to detect events in an accurate yet straightforward manner. Herein, the rate of change of frequency and the relative angle shift are converted to images as the inputs of the proposed model. Finally, this paper uses two convolutional neural networks and classifier fusion to achieve the detection result. Compared with the conventional event detection algorithm and the frequency only deep learning model, the proposed model improves the detection accuracy by over 48%. As a promising tool for bulk power system situation awareness, the proposed model requires a short decision time, which is suitable for practical scenarios.

42 ENGINEERING↗