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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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45 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Fe3: An Evaluation Tool for Low-Altitude Air Traffic Operations

The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) are introducing high-density operations in low altitude airspace in closer proximity to populated areas than conventional high-altitude air traffic. The Flexible engine for Fast-time Evaluation of Flight Environments (Fe (sup 3)) provides the capability of statistically analyzing the high-density, high-fidelity, and low-altitude traffic system under numerous scenarios, such that stake holders can study impacts of factors in the low-altitude high-density traffic system and define requirements, policies, and protocols needed to support a safe yet efficient traffic system, and even assess operational risks and optimize flight schedules without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and sample application in UAM and UTM are also presented.

Trajectory Modeling↗
NASA NTRS2019

Mission Planner Algorithm for Urban Air Mobility – Initial Performance Characterization

In this paper, an initial characterization was performed of the Mission Planner algorithm developed by NASA for Urban Air Mobility (UAM) operations research. The algorithm plans conflict-free trajectories for flights to support a given set of UAM passenger trips. The UAM trips are planned in an on-demand, first-come, first-served manner, such that any given trip is subject to the constraints imposed by previously planned trips. For this analysis, the mission planning algorithm considered only the trajectory constraints from previously-planned trips in one test condition and added vertiport constraints for the second test condition. The conflict and constraint resolution strategies used by the Mission Planner were characterized by their percentage contribution to planning iterations, their percentage effectiveness in those iterations, and their contributions to the departure delay applied to each UAM trip’s flight. With the exception of the climb and descent vertical speed strategies, most strategies showed reasonable or good performance in all test scenarios. In the test condition with vertipad constraints enabled, both the total number of iterations executed, and the number of flights that required planning iterations, was reduced for all scenarios. This was the result of the natural conditioning of the traffic achieved with scheduling and the additional information available to the Mission Planner from the vertiport scheduler. The next steps for this work will include improvements to the mission planning strategies and analyses with additional constraints and under other demand scenarios.

Guerreiro, Nelson M.↗
NASA NTRS2020

Urban Air Mobility: Predictive Modelling on the Behavior of Air-Service Bookings in Metropolitan Areas

The demand for a ride to work without running into the inefficiencies of traffic in a bustling, metropolitan area beckons for a system to tackle on-the ground traffic congestion, thus fueling the need for Urban Air Mobility (UAM). The instant gratification culture, notably its effect on Generation Z, has given way to the nature of impulsive decision making. The 2020 COVID-19 crisis, with its use of tap-to-gratify technology, highlights passengers preferring late booking. This last-minute airport reorganization crisis spearheaded our research goal to create a more user-personalized assessment of cancellation rates. Our research highlights that there will be a market in the 2030s for UAM, short-distance air transportation, and the world must prepare for the challenges that come with this new market. Through a two-part model, we obtained two randomized sets of profiles of user-specific and ridership attributes and their relationship with cancellations. Recommendations for future research include factoring this personalized aspect into cancellation projections to provide reasoned user discounts as well as a remedy for the scheduling chaos that stems from the airport organizational crisis.

Srushti Adesara↗
NASA NTRSDate not supplied

Algorithmic Analysis of the Effect of Human Behavior on Future Urban Air Mobility Operations

The demand for a ride to work without running into the inefficiencies of traffic in a bustling, metropolitan area beckons for a system to tackle on-the ground traffic congestion, thus fueling the need for Urban Air Mobility (UAM). The instant gratification culture, notably its effect on Generation Z, has given way to the nature of impulsive decision making. The 2020 COVID-19 crisis, with its use of tap-to-gratify technology, highlights passengers preferring late booking. This last-minute airport reorganization crisis spearheaded our research goal to create a more user-personalized assessment of cancellation rates. Our research highlights that there will be a market in the 2030s for UAM, short-distance air transportation, and the world must prepare for the challenges that come with this new market. Through a two-part model, we obtained two randomized sets of profiles of user-specific and ridership attributes and their relationship with cancellations. Recommendations for future research include factoring this personalized aspect into cancellation projections to provide reasoned user discounts as well as a remedy for the scheduling chaos that stems from the airport organizational crisis.

Harbani Jaggi↗
NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗
NASA NTRSDate not supplied

Eye Glance Behaviors of Ground Control Station Operators in a Simulated Urban Air Mobility Environment

Research into concepts such as advanced air mobility (AAM) and urban air mobility (UAM) offers an opportunity for successfully and safely adding new classes of vehicles into the National Airspace System. However, a need exists for research into the human factors associated with these concepts. In this paper, we evaluate the gaze behaviors of three remote ground control station operators (GCSOs) conducting simulated UAM operations. The participants monitored and controlled an unmanned aircraft system (UAS) from pre-flight to landing using ground control station (GCS) software across nine scenarios within a remote UAS operations center at NASA Langley Research Center (LaRC). As this work was exploratory, descriptive statistics were calculated to provide some initial insight into GCSO gaze patterns associated with the GCS display. Scenarios that required the operator to directly interact with an airspace scheduling system off-screen resulted in fewer on-screen glances than scenarios that did not include direct interactions with the off-screen system. After investigating several areas of interest (AOIs) within the GCS display, participants primarily viewed three AOIs: the map, vehicle status, and operations checklist. The results yielded several GCS design and operational improvement recommendations to include: (a) adding altitude information to the vehicle icon, (b) adding additional traffic information, and (c) including additional GCS training, to reduce the need to scan an operations checklist, which would allow allocation of visual attention towards other AOIs.

Urban Air Mobility↗
NASA NTRSDate not supplied

Initial Performance Evaluation of Flight Path Management Onboard Automation

Significant developments in automation are necessary to achieve safe and efficient operations in advanced aerial mobility related concepts. Urban Air Mobility (UAM) is rapidly growing, emerging field that poses a challenging use case with a tighter scale of operations compared to the traditional commercial transport paradigm. A large part of the challenge is the uncharted territory; as of this paper, no set of operational standards or guidelines for UAM operations have been established and automated en route operations for UAM level 4 (UML-4) have not been studied. Flight Path Management (FPM) automation provides a set of capabilities that are critical toward enabling airborne vehicles to achieve mission success while maintaining operational safety. An initial performance evaluation of FPM automation was conducted using a UAM-adapted version of the Autonomous Operations Planner (AOP), an onboard trajectory management capability developed over years of research targeting commercial transport operations, as its reference implementation. This paper describes the evaluation, including the approach and methodology for simulating FPM automation in UML-4, key results, future work, and conclusions.

flight path management↗
NASA NTRS2019

Fe(exp3) - A Monte Carlo Simulation Capability for Evaluation of New Air Traffic Management Concepts

The concepts of unmanned aircraft system traffic management (UTM) and urban air mobility (UAM) introduce high-density operations in low-altitude airspace and will change the paradigm of the traditional air traffic system. The Flexible engine for Fast-time evaluation of Flight environments (Fe3) provides the capability of statistically analyzing high-density, high-fidelity, and low-altitude traffic system without conducting infeasible and cost-prohibitive flight tests that involve a large volume of aerial vehicles. With this simulation capability, stakeholders can study the impacts of critical factors, define requirements, policies, and protocols needed to support a safe yet efficient traffic system, assess operational risks, and optimize flight schedules. This work provides an introduction to this simulation tool including its architecture and various models involved. Its performance and applications in high density air traffic operations are also presented.

Paralellization↗
NASA NTRSDate not supplied

Initial Performance Evaluation of Flight Path Management Onboard Automation

Significant developments in automation are necessary to achieve safe and efficient operations in advanced aerial mobility related concepts. Urban Air Mobility (UAM) is rapidly growing, emerging field that poses a challenging use case with a tighter scale of operations compared to the traditional commercial transport paradigm. A large part of the challenge is the uncharted territory; as of this paper, no set of operational standards or guidelines for UAM operations have been established and automated en route operations for UAM level 4 (UML-4) have not been studied. Flight Path Management (FPM) automation provides a set of capabilities that are critical toward enabling airborne vehicles to achieve mission success while maintaining operational safety. An initial performance evaluation of FPM automation was conducted using a UAM-adapted version of the Autonomous Operations Planner (AOP), an onboard trajectory management capability developed over years of research targeting commercial transport operations, as its reference implementation. This paper describes the evaluation, including the approach and methodology for simulating FPM automation in UML-4, key results, future work, and conclusions.

flight path management↗