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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗
NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗
NASA NTRSDate not supplied

Demand Capacity Balancing at Vertiports for Urban Air Mobility

Demand Capacity Balancing (DCB) can be applied in strategic conflict management for safe Urban Air Mobility (UAM) operations. Even when the operational tempo is low, traffic demand can locally exceed the capacity at airspace resources like vertiports. This paper proposes a DCB algorithm to manage the UAM traffic demand strategically, given the capacity at vertiports. The DCB algorithm is evaluated with traffic scenarios at Dallas/Fort Worth urban area in terms of various metrics such as demand distribution changes, pre-departure delay, and the number of simultaneous operations in the air. With the same experiment setup, more extended studies are also conducted to investigate how the UAM flight scheduling based on the DCB algorithm is affected by various conditions that can occur in a practical UAM environment, including vertiport capacity changes, a slot size parameter in capacity constraint, differences in operational policy between operators like lead time for flight plan submission and cruise flight speed, and uncertainties in actual departure and arrival times.

Urban Air Mobility↗
NASA NTRSDate not supplied

Dynamic Vehicle Assessment for Intelligent Contingency Management for Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope prediction↗
NASA NTRSDate not supplied

Prediction-Based Auralization of a Multirotor Urban Air Mobility Vehicle

Recent advances in auralization methods applicable to rotary wing vehicles have made it possible to undertake a prediction-based auralization of a representative multirotor urban air mobility vehicle. These advances include a new capability for synthesizing loading and thickness noise directly from the prediction method and a new capability for predicting and synthesizing modulating broadband self noise within a unified system noise prediction-auralization framework. These capabilities are demonstrated for a six passenger quadrotor reference vehicle design using collective-pitch control. Propagation of the source noise to a ground observer completes the auralization process. The demonstrated capability serves as the basis for future work directed at perception-influenced design of low noise urban air mobility vehicles.

Auralization↗
NASA NTRSDate not supplied

Design of a Six-Tiltrotor Concept Vehicle for Urban Air Mobility

NASA has developed a series of aircraft concepts known as reference vehicles for urban air mobility operations to facilitate research and development, enabling system-level comparisons between design candidates. In this study, new six-tiltrotor reference vehicles with full-electric and turboelectric propulsion systems were developed using the NASA Design and Analysis of Rotorcraft (NDARC) tool. The sizing results and performance metrics of these new vehicles are presented, along with comparisons to existing reference vehicles. The turboelectric six-tiltrotor is shown to be the fastest among the turboelectric reference vehicles. Although the full-electric six-tiltrotor is not as fast as the turboelectric version, it still offers considerable speed along with increased safety compared to the tiltrotor reference vehicle.

Six-Tiltrotor↗
NASA NTRSDate not supplied

Conceptual Design of Tiltrotor Aircraft for Urban Air Mobility

There has been an abundance of new and novel aircraft designs created for Urban Air Mobility (UAM) in recent years. The National Aeronautics and Space Administration (NASA) contributes to the research and development of this industry in part by applying its aircraft design tools to create conceptual designs of UAM reference vehicles. The vehicles are intended to quantify the tradeoffs and performance capabilities necessary for VTOL (vertical takeoff and landing) aircraft in the UAM design space. The reference vehicles represent a variety of configurations that seek to encompass many of the design characteristics suitable for UAM. This work focuses on the conceptual design process of two new NASA reference vehicles. Both aircraft are configured as conventional tiltrotors, but one is powered by turboshaft engines, and one is fully electric. The sizing and performance of the two aircraft are discussed, as well as how the performance and characteristics compare to a selection of other NASA reference vehicles. It is found that the tiltrotor configuration is capable of reaching speeds 43% to 51% faster than the other turboshaft designs, and 54% to 93% faster than the other electric designs. The increased speed leads to a 24% to 42% decrease in overall mission time.

Conceptual Design↗
NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
NASA NTRSDate not supplied

An Overview of NASA Research into Urban Air Mobility Noise

NASA is conducting research into the noise of future Urban Air Mobility (UAM) concepts. This work is wide ranging and incorporates efforts to measure, characterize, and predict the possible noise impacts on people and communities. A part of this effort is focused on laboratory psychoacoustic testing. The sorts of tests generate data that can be used to make predictive models that may be used to guide future design, operation and regulation practices. Another component of this work, and one that people outside NASA can engage in, is the NASA-led UAM Noise Working Group (UnWG).

UAM↗
NASA NTRSDate not supplied

Demand Capacity Balancing at Vertiports for Initial Strategic Conflict Management of Urban Air Mobility Operations

Demand Capacity Balancing (DCB) can be applied in strategic conflict management for safe Urban Air Mobility (UAM) operations. Even when the operational tempo is low, traffic demand can locally exceed the capacity at airspace resources like vertiports. This paper proposes a DCB algorithm to manage the UAM traffic demand strategically, given the capacity at vertiports. The DCB algorithm is evaluated with traffic scenarios at Dallas/Fort Worth urban area in terms of various metrics such as demand distribution changes, pre-departure delay, and the number of simultaneous operations in the air. With the same experiment setup, more extended studies are also conducted to investigate how the UAM flight scheduling based on the DCB algorithm is affected by various conditions that can occur in a practical UAM environment, including vertiport capacity changes, a slot size parameter in capacity constraint, differences in operational policy between operators like lead time for flight plan submission and cruise flight speed, and uncertainties in actual departure and arrival times.

Urban Air Mobility↗
NASA NTRS2024

Distributed Sensor Fusion of Ground and Air Nodes Using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds

High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. This work collected data using a distributed radar and camera sensor framework during the NASA Advanced Air Mobility High Density Vertiplex project SUAS flight operation. Node locations include on an onboard SUAS, affixed to the Landing and Impact Research Facility with approximate 200ft elevation, on a tripod on first-floor roof with an approximate elevation of 20 ft, and on a tripod on a concrete pad that is approximately 4 ft above sea level. Each node includes camera, radar, and GPS.

Beyond Visual Line of Sight↗
NASA NTRS2025

Distributed Sensing Node Configuration

A vision and radar sensor integration strategy for surveillance of Advance Air Mobility Mission concept of airspace is contained in the CAD drawing in this publication developed as part of NASA's Transformational Tools and Technology project. High-density airspace operations with multiple aircraft type and potentially noncooperative aircraft require distributed sensor detection and tracking systems to monitor airspace for safe, autonomous flight operations for advanced air mobility, urban air mobility, and high density small uncrewed air systems (SUAS) flight concepts. Contained herein are drawings that are available in the stp and creo file formats of the distributed sensing nodes. Additional dovetail integration is included to enable rapid changing of embedded system. A day in the life node deployment is included in the 2023_last_flight_day.mp4. Please cite this publication if you use these drawings. This sensor configuration methodology is presented in detail in the publication reference below. Chester Dolph, Thomas Lombaerts, Corey A. Ippolito, Vahram Stepanyan, Evan Kawamura, Keerthana Kannan, George Szatkowski, Todd Ferante, Christopher Morris, Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano. "Distributed Sensor Fusion of Ground and Air Nodes using Vision and Radar Modalities for Tracking Multirotor Small Uncrewed Air Systems and Birds," AIAA 2024-1781. AIAA SCITECH 2024 Forum. January 2024.

Chester V Dolph↗
NASA NTRS2018

Observations from Exploration of VTOL Urban Air Mobility Designs

NASA is exploring rotorcraft designs for VTOL air taxi operations, also known as urban air mobility (UAM) or on-demand mobility (ODM) applications. Several concept vehicles have been developed, intended to focus and guide NASA research activities in support of aircraft development for this emerging market. This paper summarizes the work conducted to date. To initially explore the broad design trade-space, three concept vehicles were designed: a quadrotor with electric propulsion; a side-by-side helicopter with hybrid propulsion; and a tiltwing with turbo-electric propulsion. Next a specific UAM mission was developed, accounting for the existing geography, population patterns, infrastructure, and weather in twenty-eight markets across the United States of America. Then in order to quantify the tradeoffs and performance targets necessary for practical implementation of the UAM vision, aircraft were designed to perform this mission, considering a range of aircraft types and propulsion system architectures: quadrotor aircraft, with turboshaft and all-electric propulsion; side-by-side aircraft, with turboshaft and all-electric propulsion; and lift+cruise aircraft with all-electric and turbo-electric propulsion. In examining these vehicles, performance targets and recurring technology themes emerged, which can guide investments in research and development within NASA, other government agencies, academia, and industry. In addition, results from the designs support observations about the trade-offs and key design decisions.

Observations from Exploration↗
NASA NTRS2021

Description of the NASA Urban Air Mobility Maturity Level (UML) Scale

As part of its assessment of the nascent passenger-carrying urban air mobility (UAM) ecosystem, NASA’s UAM Coordination and Assessment Team developed a framework known as the UAM Maturity Level (UML) scale. This framework is intended to have multiple applications including: 1) insight into the likely operational capabilities as a UAM air transportation system develops over time; 2) analysis of technology and regulatory requirements associated with the UAM maturation process; 3) assessment of the current maturity of various segments of the UAM ecosystem; 4) coordination of UAM ecosystem priorities and areas of emphasis; and 5) increasing community and public awareness of UAM and how it may affect mobility in the future. This paper describes the structure of the UML scale and its levels. The paper also describes candidate strategies for advancing between levels, along with associated regulatory gaps and considerations.

Urban Air Mobility↗
NASA NTRS2020

Public Trust and Acceptance for Concepts of Remotely Operated Urban Air Mobility Transportation

There is building interest within industry and government to enable Urban Air Mobility (i.e., air-taxies). One concept envisions remotely piloted aircraft, yet it is unclear how this will impact public trust and acceptance. Method: Two hundred participants read vignettes describing remotely-piloted UAM operations and then responded to a series of questionnaires. The study employed a one-way between-subjects design manipulating five levels of Pilot-in-Command Distance: Onboard Pilot; Remote Control Pilot; Dedicated Remote Operator; Remote Operator; System Manager. Results: The Remote Control Pilot group indicated they would be less likely than the Onboard Pilot Group to use UAM, based on the mediating effect of trust in the automation. The Remote Control Pilot and Remote Operator groups indicated they would be less likely to use UAM than the Onboard Pilot group, based on the mediating effect of trust in the remote pilot/operator. Conclusion: Trust in UAM automation and remote pilots/operators will likely affect public acceptance of UAM.

MTurk↗