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At least 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Preliminary Analysis of Separation Standards for Urban Air Mobility using Unmitigated Fast-Time Simulation

This research provides a preliminary analysis of various separation standards for Urban Air Mobility (UAM) operations. This study focuses on understanding and analyzing potential conflicts between UAM flights and conflicts between UAM and conventional aircraft. Fast-time simulations are conducted with a projected high-density of UAM traffic scenario and historical conventional non-UAM traffic flying under Instrument Flight Rules (IFR) or Visual Flight Rules (VFR) in Dallas Fort Worth (DFW) airspace. The conflicts are analyzed by examining losses of separation and near mid-air collisions of UAM flights and unmitigated risk of collision. A set of varied separation standards are used to investigate the impact of the new UAM operations on safety. The preliminary conflict analysis results will help inform separation standards development and in airspace and route design activities for future UAM operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

A Data Analysis and Simulation Study of Urban Air Mobility

For the Urban Air Mobility (UAM) industry, NASA has defined a series of UAM Maturity Levels (UML) corresponding to increasingly more complex and operationally dense UAM operations. In support of the gradual progression towards higher UML levels, NASA is currently conducting a set of UAM air traffic simulations—collectively referred to as X4. This paper describes a set of system effectiveness measures, and their associated metrics, for data analysis of X4 simulations. The descriptions, rationales, and calculation procedures for two metrics to be used in data analysis of simulation results, the number of predicted demand-capacity imbalances and the pre-departure delays, are described. Results from data analysis of one set of simulation runs are presented to demonstrate how these metrics support the assessment of performance of the system architecture for X4 simulations and the verification of experiment requirements.

Urban Air Mobility↗
NASA NTRSDate not supplied

Characterization of Urban Air Mobility Vehicle Operational Noise and Community Noise Impact

This presentation focuses on the process for predicting urban air mobility (UAM) vehicle noise at the source and how that can be used to estimate the impact on the community. Following conceptual design, in which the vehicle is appropriately sized for its intended mission, a comprehensive analysis must be performed for a range of operating conditions spanning the flight envelope to determine the corresponding configurations of the vehicle, that is, the trimmed states. Many UAM vehicles have redundant controls, so the trimmed state for any particular operating condition may not be unique, with some states producing more noise than others. For each trimmed state, the noise produced by each source, for example, steady and unsteady rotor noise, inclusive of propulsion airframe aeroacoustic effects, may be computed and so-called source noise (hemi)spheres generated. Land use planning tools, including those using simulation and integrated modeling approaches, use these (or derived) data to generate noise exposure maps on the ground. Alternatively, these data may serve as input to auralization, turning the numerical data into an audible sound that can subsequently be used as part of a perception-influenced acoustic design process that takes into account human response.

urban air mobiliy↗
NASA NTRS2022

Scheduling For Urban Air Mobility Using Safe Learning

This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over interarrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.

Urban Air Mobility↗
NASA NTRSDate not supplied

Scheduling for Urban Air Mobility using Safe Learning

This work considers the scheduling problem for Urban Air Mobility (UAM) vehicles travelling between origin-destination pairs with both hard and soft trip deadlines. Each route is described by a discrete probability distribution over trip completion times (or delay) and over interarrival times of requests (or demand) for the route along with a fixed hard or soft deadline. Soft deadlines carry a cost that is incurred when the deadline is missed. An online, safe scheduler is developed that ensures that hard deadlines are never missed and that average cost of missing soft deadlines is minimized. The system is modelled as a Markov Decision Process (MDP) and safe model based learning is used to find the probabilistic distributions over route delays and demand. Monte Carlo Tree Search (MCTS) Earliest Deadline First (EDF) is used to safely explore the learned models in an online fashion and develop a near-optimal non-preemptive scheduling policy. These results are compared with Value Iteration (VI) and MCTS (Random) scheduling solutions.

Urban Air Mobility↗
NASA NTRSDate not supplied

Characterization of Urban Air Mobility Vehicle Operational Noise and Community Noise Impact

This presentation focuses on the process for predicting urban air mobility (UAM) vehicle noise at the source and how that can be used to estimate and mitigate the impact on the community. Following conceptual design, in which the vehicle is appropriately sized for its intended mission, a comprehensive analysis must be performed for a range of operating conditions spanning the flight envelope to determine the corresponding configurations of the vehicle, that is, the trimmed states. Many UAM vehicles have redundant controls, so the trimmed state for any particular operating condition may not be unique, with some states producing more noise than others. For each trimmed state, the noise produced by each source, for example, steady and unsteady rotor noise, inclusive of propulsion airframe aeroacoustic effects, may be computed and so-called source noise (hemi)spheres generated. The data may be used for analyses supporting noise certification or serve as input to auralizations (turning the numerical data into an audible sound) that can subsequently be used as part of a perception-influenced acoustic design process. Land use planning tools, including those using simulation and integrated modeling approaches, also use these (or derived) data to generate noise exposure maps on the ground. Finally, the data may be used to support development of low noise flight operations using an acoustic flight simulator or acoustically aware flight control.

urban air mobility↗
NASA NTRS2023

Performance Modeling of Urban Air Mobility Vehicles to Support Air Traffic Management Research

The recent emergence of Urban Air Mobility (UAM) vehicles has resulted in a need for flight performance models that enable comprehensive simulation-based research on air traffic management topics such as route structure, scheduling, and separation standards. Successful performance modeling methods exist for a wide range of traditional aircraft designs. However, comparable modeling methods appropriate for UAM vehicles that combine fixed-wing and rotorcraft performance have not yet been established. One challenge to progress has been the lack of available data capturing the performance characteristics and unique flight profiles of these aircraft. This paper describes methods used to generate the required performance data and the development of performance models for UAM vehicles. Included is a review of the energy and power equations often used in developing performance models for traditional aircraft as well as a discussion of their applicability to UAM vehicles. The challenge of generating realistic performance data in over-actuated vehicles transitioning from hover to cruise flight is also addressed through an approach based on objective function optimization. A table-based performance model format adapted to UAM configurations is described, as well as parametric models intended to accompany the performance table to allow detailed modeling of power and fuel consumption during accelerated flight, turning flight, or flight at an arbitrary climb or descent rate. A discussion of future work is also provided, including the need for refinement of UAM performance modeling methods and formats, especially in conjunction with improvements to aerodynamic modeling of vehicles with complex designs where strong interaction effects may dominate important regions of the flight envelope.

Performance Modeling↗
NASA NTRS2023

Performance Modeling of Urban Air Mobility Vehicles to Support Air Traffic Management Research

The recent emergence of Urban Air Mobility (UAM) vehicles has resulted in a need for flight performance models that enable comprehensive simulation-based research on air traffic management topics such as route structure, scheduling, and separation standards. Successful performance modeling methods exist for a wide range of traditional aircraft designs. However, comparable modeling methods appropriate for UAM vehicles that combine fixed-wing and rotorcraft performance have not yet been established. One challenge to progress has been the lack of available data capturing the performance characteristics and unique flight profiles of these aircraft. This paper describes methods used to generate the required performance data and the development of performance models for UAM vehicles. Included is a review of the energy and power equations often used in developing performance models for traditional aircraft as well as a discussion of their applicability to UAM vehicles. The challenge of generating realistic performance data in over-actuated vehicles transitioning from hover to cruise flight is also addressed through an approach based on objective function optimization. A table-based performance model format adapted to UAM configurations is described, as well as parametric models intended to accompany the performance table to allow detailed modeling of power and fuel consumption during accelerated flight, turning flight, or flight at an arbitrary climb or descent rate. A discussion of future work is also provided, including the need for refinement of UAM performance modeling methods and formats, especially in conjunction with improvements to aerodynamic modeling of vehicles with complex designs where strong interaction effects may dominate important regions of the flight envelope.

Performance Modeling↗
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRS2023

Prediction of Noise-Power-Distance Data for Urban Air Mobility Vehicles

In contrast to most commercial air traffic today, vehicles serving the urban air mobility (UAM) market are anticipated to operate within communities and be close to the public at large. The approved model for assessing environmental impact of air traffic actions in the United States, the Federal Aviation Administration (FAA) Aviation Environmental Design Tool (AEDT), does not directly support analysis of such operations due to a combined lack of UAM aircraft flight performance model data and aircraft noise data. This paper addresses the latter by offering two prediction-based approaches for generation of noise-power-distance (NPD) data for use within AEDT. One utilizes the AEDT fixed-wing aircraft modeling approach and the other utilizes the AEDT rotary-wing aircraft modeling approach.

noise-power-distance↗
NASA NTRSDate not supplied

Designing A Coaxial Quadrotor for Urban Air Mobility

The NASA Revolutionary Vertical Lift Technology Project has created several Urban Air Mobility reference design vehicles to aid the burgeoning UAM industry. With over 700 conceptual designs already proposed, much interest has been generated across many unique configurations to bring vertical flight to the masses. The quadrotor configuration is one of the NASA reference vehicles, and does have some conceptual designs proposed in the industry. This study looks at the conceptual design of a coaxial quadrotor vehicle as an extension of the NASA RVLT quadrotor concept vehicle. The coaxial quadrotor has a similar layout to the quadrotor, but with a coaxial rotor at each of the four corners of the vehicle. Trade studies carried out using the NASA Design and Analysis of Rotorcraft (NDARC) tool analyzed the vehicle sizing, design gross weight, and performance for four design variants. The variants all use the baseline coaxial quadrotor layout, but analyze the effects of two major design choices: 1) variable pitch vs variable speed rotor control, and 2) motor to rotor power transmission via a gearbox vs direct-drive. The results indicate one design variant may have more benefits on some of the design objectives while another configuration may better satisfy the others.

UAM↗
NASA NTRSDate not supplied

High-Fidelity Analysis of Lift+Cruise VTOL Urban Air Mobility Concept Aircraft

This paper presents a high-fidelity multidisciplinary analysis for the NASA lift+cruise vertical takeoff and landing urban air mobility concept aircraft. The reported simulations couple comprehensive rotorcraft aeromechanics and high-fidelity computational fluid dynamics (CFD). Aerodynamic solutions are computed on dynamic, deforming, unstructured, overset grid systems by an unsteady CFD solver, FUN3D, developed at the NASA Langley Research Center. An integrated overset-grid assembler, Yoga, is used for communications between component grids. Two turbulence models are compared for loose-coupling simulations in hover and low-speed forward flight conditions. One model is the negative variant of the one-equation Spalart-Allmaras model (SA-neg), and the other model is the SA-neg-R model adding a simple rotation correction term to the SA-neg model. It demonstrated that the rotation correction in the SA-neg-R turbulence model significantly improves resolution of secondary vortices and wake interactions for rotorcraft simulations.

CFD↗
NASA NTRSDate not supplied

Development of a Weather Capability for the Urban Air Mobility Airspace Research Roadmap

Traditionally, the transportation system’s resiliency to the impacts of weather is an area where neglected or incorrect assumptions can lead to difficulties later in the research and development lifecycle. To mitigate this, NASA has ongoing efforts to develop a set of research roadmaps for organizing, integrating, and communicating research into new aviation infrastructure and transportation modalities, within which weather is being addressed early on. An effort has been undertaken to add weather assumptions and requirements to an already-existing roadmap for the Urban Air Mobility (UAM) airspace, seeking to integrate weather requirements early in the system design. This effort addresses the way in which state-of-the art and evolving weather science and technology can enable safe and efficient travel with increasing tempo of UAM operations over time. This paper describes the addition of weather as one of 10 capabilities into the UAM Airspace research roadmap, laying out the anticipated weather technology and information requirements needed to facilitate operations at various UAM Maturity Levels. The process developed and exercised by MIT Lincoln Laboratory researchers produced 41 unique requirements to be satisfied by a Weather capability for the UAM ecosystem, with more than 300 dependencies identified across the system. These requirements cover measurement, analysis, modeling, forecasting, decision support, dissemination, and overarching policy, and are provided with an overview of weather challenges for UAM. The requirements were mainly defined based on subject matter expert review of existing UAM Airspace system requirements, and refined based on iterative feedback with various stakeholders including regulators, academia, and industry. Going forward, this roadmap will help researchers and developers align to a common vision in ensuring that weather is appropriately considered in the UAM ecosystem.

research roadmap↗
NASA NTRS2023

High-Fidelity Analysis of Lift+Cruise VTOL Urban Air Mobility Concept Aircraft

This paper presents a high-fidelity multidisciplinary analysis for the NASA Lift+Cruise vertical takeoff and landing urban air mobility concept aircraft. The reported simulations couple comprehensive rotorcraft aeromechanics and high-fidelity computational fluid dynamics (CFD). Aerodynamic solutions are computed on dynamic, deforming, unstructured, overset grid systems by an unsteady CFD solver, FUN3D, developed at the NASA Langley Research Center. An integrated overset-grid assembler, Yoga, is used for communications between component grids. Two turbulence models are compared for loose-coupling simulations in hover and low-speed forward flight conditions. One model is the negative variant of the one-equation Spalart-Allmaras model (SA-neg), and the other model is the SA-neg-R model adding a simple rotation correction term to the SA-neg model. It demonstrated that the rotation correction in the SA-neg-R turbulence model significantly improves resolution of secondary vortices and wake interactions for rotorcraft simulations.

CFD↗
NASA NTRS2023

A Blockchain Case Study for Urban Air Mobility Operational Intent

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case. In this case study, two vehicle operators are operating in the same airspace. Their intent is to fly vehicles that land at a shared vertiport, securely.

Urban Air Mobility↗
NASA NTRSDate not supplied

A Blockchain Case Study for Urban Air Mobility Operational Intent

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case.

UAM↗
NASA NTRSDate not supplied

The Viability of See and Avoid for Urban Air Mobility Operations

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗