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At least 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Developing Urban Air Mobility Vehicle Models to Support Air Traffic Management Concept Development

To support Urban Air Mobility (UAM) research efforts at NASA, the Airspace Target Generator (ATG) software used in the FutureFlight Central (FFC) air traffic control tower simulator is undergoing updates to support physics-based UAM vehicle models. A process was developed to integrate UAM vertical takeoff and landing (VTOL) aircraft into the fixed-wing ATG modeling environment without significant change to the underlying equations of motion and vehicle model database. The VTOL aircraft models were converted from a six degrees-of-freedom (6-DOF) representation into a four degrees-of-freedom (4-DOF) representation for integration within ATG. Three vehicle designs from the NASA Revolutionary Vertical-Lift Technologies (RVLT) project were selected: a lift-plus-cruise (LPC) aircraft model and quadrotor, electric-powered (QEP) 1-seater and 6-seater models. With the LPC model comprised of a nonlinear force and moment build-up, and the QEP models comprised of linearized stability derivatives, two separate processes were developed to convert the lift, drag, and propulsion characteristics of each model into the ATG model database. Key aircraft performance characteristics including climb, cruise, and descent performance were preserved during the conversion process. Because ATG simulates fixed-wing aircraft through ground taxi and takeoff to approach and landing, acceleration command algorithms were developed to model the vertical takeoff and vertical landing phase of UAM operations. A strategy was then developed to transition the aircraft model to- and from- the new control mode.

urban air mobility↗
NASA NTRS2020

Urban Air Mobility Generation 1 Acoustic Database

An acoustic database has been generated in support of the NASA Revolutionary Vertical Lift Technology (RVLT) project, Technical Challenge (TC) TC.UAM.Noise.1, entitled “Urban Air Mobility Operational Fleet Noise Assessment.” As part of that TC, a periodic (approximately annual) development of an acoustic database will be performed based on the latest available information. The release of the first such database, namely, the “Gen 1 database for fleet noise assessments,” developed in support of RVLT milestone RVLT.23.02.L350, is the subject of this document.

urban air mobility↗
NASA NTRSDate not supplied

Multirotor Configuration Trades Informed by Handling Qualities for Urban Air Mobility Application

Many contemporary Advanced Air Mobility (AAM), and more specifically, urban air mobility (UAM) vehicle designers are attracted to variable rotor speed-controlled designs with multiple rotors because of the great potential for mass savings compared to more traditional, variable blade pitch-controlled vehicles. These designs are based on the assumption that the stability and control of recreation or basic utility-sized drones can be scaled to larger passenger-sized vehicles. Previous work had shown the challenges in stabilizing passenger-sized quadcopters. In this study, power constraints were made less restrictive and varied, allowing more control power. Motor parameters such as efficiency, nominal voltage and current operating point, and rise time of the rotor speed controller step response were studied. By fixing the efficiency of the motor to 95% and assuming a motor voltage to current ratio of 2.0 (previously, assumed to be 1.0), the authors were able to stabilize the quadcopter in the roll axis because this allowed the vehicle to achieve adequate rise times between 0.4 and 0.8 s. This motor optimization was extended to a hexacopter and octocopter designed to the same payload size and mission as the quadcopter. The three vehicle configurations and their motor speed controllers were compared. It was found that while hexacopter and octocopter required more mass and overall power; all three configurations had similar margins required for control. However, the hexacopter and octocopter were able to use this power margin to achieve lower rise times (i.e. the vehicle responded more quickly to pilot inputs) than the quadcopter, with the octocopter having the lowest rotor response rise time of the three vehicle configurations studied.

Multirotor↗
NASA NTRS2020

Lessons Learned: Using UTM Paradigm for Urban Air Mobility

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA’s UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

Urban Air Mobility↗
NASA NTRSDate not supplied

Autonomous Contingency Management In Urban Air Mobility: The Communication Network Awareness Machine System

Next Generation Air Transportation System (NextGen) has begun the modernization of the nation’s air transportation system (NAS), with goals to improve system safety, increase operation efficiency and capacity, provide enhanced predictability, resilience and robustness [1]. The overall objective of the Air Traffic Management-eXploration (ATM-X) project is to facilitate the goals of NextGen by conducting research to enable the growing demand of new, mission variant, air vehicles with safe access to the NAS. The implementation and utilization of new and burgeoning technologies that are both flexible, scalable, and systematically user-focused are requisite for ATM-X to achieve its intention of NAS safe entry [2]. Researchers from NASA Langley’s Flight Deck Integration Team have developed a system architecture that would allow ATM-X to leverage the necessary capabilities of an Increasingly Autonomous System (IAS), machine-agent that will promote the safe access and operation of air vehicles within what has become the byproduct of NextGen modernization, a Net-Centric airspace architecture and an Urban Air Mobility (UAM) community. Conducting flight operations within this type of architecture constrains the human-agent’s natural ability by data management. When the massive volume of data, its types, and the acquisition speed at which the data is ingested is observed it becomes evident that the human-agent will be functioning at an operational disadvantage. Therefore, the development and integration of intelligent machine-agents into the flight deck are a necessary implementation to achieve ATM-X overall objective of safe access and operation in the NAS.

Urban Air Mobility↗
NASA NTRS2020

Intelligent Contingency Management for Urban Air Mobility

The third aviation revolution is seeking to enable transportation where users have access to immediate and flexible air travel; the users dictate trip origin, destination and timing. One of the major components of this vision is urban air mobility (UAM) for the masses. UAM means a safe and efficient system for vehicles to move passengers and cargo within a city. In order to reach UAM’s full market potential the vehicle will have to be autonomous. One of the primary challenges of autonomous flight is dealing with off-nominal events, both common and unforeseen; thus, intelligent contingency management (ICM) is one of the enabling technologies. In this context, the vehicle has to be aware of its internal state and external environment at all times, ascertain its capability and make decisions about mission completion or modification. All of these functions require data to model and assess the environment and then take actions based on these models. Necessarily, there is uncertainty associated with the data and the models generated from it. Since we are dealing with safety-critical systems, one of the main challenges of ICM is to generate sufficient data and to minimize its uncertainty to enable practical and safe decision making. We propose an overall architecture that incorporates deterministic and learning algorithms together to assess vehicle capabilities, project these into the future and make decisions on mission management level. A layered approach allows for mature parts and technologies to be integrated into early highly automated vehicles before the final state of autonomy is reached.

data-driven systems↗
NASA NTRSDate not supplied

Lessons Learned: Using UTM paradigm for Urban Air Mobility Operations

Urban Air Mobility (UAM) aims to reduce congestion on the roads and highways by offering air taxi as an alternative to driving on surface roads. Integration of UAM operations in the National Airspace System (NAS) has been the focus of the research conducted at NASA Ames Research Center. A simulation was performed in collaboration with Uber Technologies Inc to investigate if NASA’s UTM architecture and its implementation as demonstrated in the 2019 UTM field tests were extensible for UAM operations, and if the data exchange between multiple operators as planned under UTM were adequate for UAM operations in the shared airspace. In order to explore these research questions, three Use Cases were defined to investigate different airspace management challenges. This paper will describe the lessons learned from exercising the uses cases and the airspace management services including scheduling and separation developed to facilitate initial UAM operations.

Urban Air Mobility↗
NASA NTRS2020

Motion Sickness and Concerns for Urban Air Mobility Vehicles: A Literature Review

Motion sickness is a general term for a constellation of signs and symptoms, generally due to exposure to abrupt, periodic, or unnatural accelerations, especially when traveling in a vehicle. Motion sickness results from a mismatch of the visual and nonvisual (vestibular and kinesthetic) information, the observed scene and the motion felt or lack of it. Motion sickness onset is associated with a pattern of physiological changes in heart rate, peripheral blood flow, respiration, and skin conductance and the pattern is repeatable for a particular subject but variable between subjects. Demographic factors such as gender and age that affect motion sickness are well known with children, women, and older adults more likely to be susceptible. Often motion sickness is assessed and quantified using variations of the motion sickness susceptibility questionnaires including the Pensacola Diagnostic Rating Scale and the Simulator Sickness Questionnaire. Even though symptoms are easily identified by such questionnaires, they commonly are subjective. Tools such as these questionnaires for screening individuals susceptible to motion sickness are useful, however, they are only mildly predictive. Moreover, models for predicting motion sickness, which have largely been developed for sea sickness, do not consider task characteristics. Predictions of motion sickness rates and prevalence for Urban Air Mobility (UAM) vehicles are not possible at present because data from actual flight or full-fidelity simulation are simply not yet available. Extrapolation from other modes of transportation (i.e., automobiles, buses, trains, boats, other types of aircraft) is difficult because of differences in the motion stimulus experienced, trip duration, and other factors. How UAM vehicles will change the social dynamics of passenger interaction and vehicle interior design changes (e.g., seat orientations) is unknown. Should motion sickness prove to be an issue, vehicle design modifications such as having passengers face forward, providing additional seat recline, giving each person their own climate control for airflow, perhaps ensuring the horizon is visible to all passengers (reducing visual occlusion by the headrest) and visually stabilizing displays on carry-on devices (smart phones, tablets, etc.) may benefit passengers. Several commercial companies provide wearable devices for physiological monitoring that have been validated and are suitable for use with passengers in UAM vehicles or high-fidelity simulators. Potential countermeasures for motion sickness include user-worn devices, anti-motion sickness medications, and non-pharmacological approaches such as biofeedback and Autogenic Feedback Training Exercise. Both simulator and in-vehicle UAM research is needed to evaluate the effectiveness of any potential countermeasure.

motion sickness↗
NASA NTRSDate not supplied

An Initial Concept for Intermediate-State, Passenger-Carrying Urban Air Mobility Operations

This paper describes a general “vision” concept of operations (ConOps) for intermediate state, passenger-carrying urban air mobility (UAM) missions that has been developed jointly by NASA and Deloitte with input from stakeholders in the UAM ecosystem. This vision ConOps provides a broad overview of some of the high-level requirements for realizing the simultaneous operation of hundreds of aircraft over a single metropolitan area in a wide range of weather conditions as conceptualized by many visionaries at the time of its publication. The concepts contained in this vision ConOps are intended to provide a starting point for further discussions and investigations into how UAM operations can be best enabled. Consequently, we also describe some of the areas where additional research is required before a detailed baseline ConOps can be finalized.

urban air mobility↗
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 NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Computational Experiments

Rapid Aero Modeling (RAM) applied to computational testing, RAM-C, is an approach to efficiently and automatically obtain aerodynamic models during computational investigations. RAM-C is designed to estimate models appropriate for flight dynamics studies and simulations. The approach responds to a demand for experimental efficiency and model fidelity that has increased with growing aircraft complexity and aerodynamic nonlinearities associated with hybrid and electric vertical takeoff and landing (eVTOL) aircraft. In an Urban Air Mobility (UAM) transportation system, it is expected that aircraft will embrace many features from both airplanes and rotorcraft. These vehicles present many more factors than conventional aircraft which can lead to increased computational costs and missed key factor interactions when applying traditional testing and modeling methods. RAM-C provides feedback loops around computational codes to rapidly guide testing toward aerodynamic models meeting user-defined fidelity goals. It combines and extends concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM-C only collects enough data required to meet the user-specified prediction error requirements thus saving computational time and resources. The overall achievable fidelity of the final model also depends on the accuracy of the test facility, or in this case, the computational modeling approach. Previous studies to support development of the RAM-T process were conducted in wind tunnel tests to assess potential metrics, algorithms, and procedures. This paper presents results from the next steps taken and tests conducted for the development of RAM-C technology and highlights some of the unique features of RAM applied eVTOL configurations in a computational study.

Aerodynamics↗
NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Wind-Tunnel Tests

Rapid Aero Modeling (RAM) applied to wind tunnel testing, RAM-T, is an approach to efficiently and automatically obtain aerodynamic models during testing. The approach saves time and resources by responding to the demand for experimental efficiency and model fidelity. Motivation for this demand is more acute when investigating a class of vehicles categorized as Urban Air Mobility (UAM) aircraft where many features from both aircraft and rotorcraft are present. RAM-T provides a feedback loop around the test facility to guide the test toward high-fidelity, statistically rigorous aircraft models. The general RAM approach is applicable to computational or physical experiments. It combines concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. This paper presents results from tests conducted for development of an automated RAM-T technology. The results highlight some of the unique features of RAM applied to eVTOL configurations.

Aerodynamics↗
NASA NTRSDate not supplied

Cyber Threats and Security Controls Analysis for Urban Air Mobility Environments

Since the cyber threat landscape changes daily, cybersecurity needs to be an ongoing activity for every organization within the UAM environments. This paper will provide information on identified cyber threats and controls associated with an instance of the Urban Air Mobility (UAM) environment. The MITRE ATT&CK model and framework and the selection of cyber threats and the National Institute of Standards and Technology publications for security control identification and cybersecurity risk identification will be leveraged for analysis.

Urban Air Mobility↗
NASA NTRSDate not supplied

Cyber Threats & Security Controls Analysis for Urban Air Mobility Environments

Since the cyber threat landscape changes daily, cybersecurity needs to be an ongoing activity for every organization within the UAM environments. This paper will provide information on identified cyber threats and controls associated with an instance of the Urban Air Mobility (UAM) environment. The MITRE ATT&CK model and framework and the selection of cyber threats and the National Institute of Standards and Technology publications for security control identification and cybersecurity risk identification will be leveraged for analysis.

Cyber Threats↗
NASA NTRSDate not supplied

Mechanical Design of the Urban Air Mobility Side-by-Side Test Stand

The Urban Air Mobility Side-by-Side Test Stand (SBS) is a new capability for the National Aeronautics and Space Administration (NASA) to test the conceptual side-by-side rotorcraft configuration. This test stand enhances the experimental capabilities of the Revolutionary Vertical Lift Technology (RVLT) Project and is primarily designed to be tested in the U.S. Army’s 7-by 10-Foot Wind Tunnel at NASA Ames Research Center. One of the goals for the SBS is to identify the optimal degree of rotor overlap that will yield the best aerodynamic performance. The SBS has two counter-rotating, intermeshing rotors that can vary in lateral separation. The test stand can pitch nose up and nose down, with each rotor having the capability to be trimmed independently through cyclic and collective controls. Six-axis load cells and rotary torque sensors are placed underneath each rotor to measure the thrust, torque, and side force from each rotor system. This paper describes the mechanical design of the SBS and outlines the structural analyses conducted to ensure a safety factor of 4 for ultimate strength and 3 for yield strength during all experimental testing.

Mechanical Design↗
NASA NTRSDate not supplied

Fundamental Aeroelastic Analysis of an Urban Air Mobility Rotor

A computational investigation was conducted of the aeroelastic characteristics of a representative rotor typical of aircraft for Urban Air Mobility. A rotorcraft comprehensive analysis was used to examine the aeroelastic characteristics of the model, both in hover and in forward flight. Throughout the study, parameters examined included rotor rotational speed, forward flight speed, blade torsion stiffness, and chordwise locations of the blade elastic axis and center of gravity. Three sets of airfoil properties were analyzed: NACA 0012, VR8, and VR12. For hover, the effect of dynamic inflow was studied via a comparison of uniform inflow and dynamic inflow models. Thrust coefficient and blade tip twist were analyzed to ascertain performance characteristics, and eigen analysis was employed to determine stability. For the forward flight cases, rpm and forward flight speed were swept separately, and the effect of each on performance and stability was assessed. Thrust coefficient and mean drag coefficient were used to evaluate performance of the rotor, and eigen analysis was invoked to study rotor stability. Forward flight stability results generated via a constant coefficient analysis were compared with results generated via Floquet Theory.

Fundamental Aeroelastic Analysis↗
NASA NTRSDate not supplied

Webinar on Urban Air Mobility Vision: Sky For All

The webinar will highlight the role UAM can play in future mobility improvements to enhance lives of people living in urban environments. In addition, challenges of integrating these vehicles into the current airspace will be discussed. NASA will present how these questions are being addressed from a US perspective.

Urban Air Mobility↗