Results for “urban mobility” — Page 15

NASA NTRS2019

A Research Platform for Urban Air Mobility (UAM) and UAS Traffic Management (UTM) Concepts and Application

The purpose of this paper is to describe a research platform for the Urban Air Mobility (UAM) concept. UAM was conceived to enable short-range, low-altitiude service to commuters in metropolitan areas. At the NASA Ames Airspace Operations Laboratory (AOL) user interfaces have been developed to support small UAS operations for the UAS Traffic Management (UTM) project. Due to operational similarities between UAM and UTM, the UTM architecture offers a convenient starting point for research and development of UAM operations. Herein, we begin with an overview of the primary need and expectations characterizing concepts for modern air transportation systems, followed by a brief description of the UTM and UAM concepts. We then describe the UTM architecture, as well as the AOL's research platform and capabilities. The potential applications for UAM research and development are then discussed, providing some direction for future research efforts.

UAM↗

NASA NTRSDate not supplied

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding, which used a blended coding approach to generate actionable heuristics that stakeholders (e.g., researchers, UI designers, software developers) could use to answer specific research questions and make future design decisions. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

high density vertiplex↗

NASA NTRSDate not supplied

Qualitative Data Coding of User Experience with an Urban Air Mobility Fleet Manager Interface

The NASA Aeronautics Research Mission Directorate created the High Density Vertiplex (HDV) project to integrate and evaluate a prototype Urban Air Mobility (UAM) ecosystem. Part of the HDV testing environment included a prototype operator user interface called the Fleet Manager Interface (FMI). In 2023, HDV conducted flight testing with the FMI during which a user experience (UX) study was performed to assess the quality of UX and elicit design recommendations. As a result, a large database of open-ended, qualitative responses was generated and then coded using a new qualitative data coding technique called Directive String Coding, which used a blended coding approach to generate actionable heuristics that stakeholders (e.g., researchers, UI designers, software developers) could use to answer specific research questions and make future design decisions. The key themes that arose from the coded responses showed that the UX was pleasant, system notifications should be more salient, and information across multiple screens should be integrated into a central display. As the Fleet Manger operational role is still being defined, it is vital to increase our understanding of the tools and capabilities needed for such a role. The results from HDV work will eventually feed into standards for vertiport operations that will enable safe and scalable UAM operations in the United States.

high density veriplex↗

NASA NTRSDate not supplied

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗

NASA NTRS2020

Data Science Challenges for Urban Air Mobility

Aviation is a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Data Science↗

NASA NTRSDate not supplied

Community Noise Impact of Urban Air Mobility Vehicle Operations

Advanced air mobility (AAM) missions, carried out by electrically driven air vehicles, are characterized by ranges of less than about 300-500 nm (about 500-900 km) and include both rural and urban operations. The missions may include public transportation, cargo delivery, air taxi, and emergency response. While the urban air mobility (UAM) subset of AAM is projected to have high economic benefit, it is also the most difficult to develop because it must overcome many barriers, including those associated with the airspace system, safety, and community noise. This presentation focuses on the utilization of UAM source noise data for assessment of community noise impact. Although a limited number of acoustic flight measurement campaigns have been made to characterize the source noise of prototype and preproduction UAM aircraft, prediction-based approaches are primarily considered herein. 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. For each trimmed state, the noise produced by each source, for example, steady and unsteady rotor noise, may be computed and so-called source noise (hemi)spheres generated. These source data may subsequently be used in various community noise impact analyses. Several use cases are presented including those supporting noise certification and those for auralizations that may, in turn, be used as part of a perception-influenced design process. Land use planning tools for generating noise exposure maps, including those using simulation and integrated modeling approaches, are also presented. Finally, use cases supporting development of low noise flight operations, including an acoustic flight simulator and acoustically aware flight control, are considered.

aircraft community noise↗

NASA NTRSDate not supplied

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗

NASA NTRS2020

Data Science and Urban Air Mobility: Challenges and Opportunities

Aviation is broadly a combination of aircraft, airspace and airports. The data science life cycle comprises of five steps - capture, maintain, process, analyze and communicate. The presentation introduces the legacy of conventional aviation research in the context of the data science life cycle to motivate the challenges with Urban Air Mobility, a field that is quite nascent. A summary of recent research will be presented to highlight the innovative ways to address the challenges. Examples provided will include the generation of synthetic data, encounter models from simulations, and leveraging novel and diverse data sets from traditional transportation and non-aviation sources, to analyze problems of operation in urban airspace. Finally, opportunities will be identified for further exploration, niche development and filling the gaps in the field of data science for UAM.

Urban Air Mobility↗

NASA NTRS2020

Urban Air Mobility (UAM) Working Groups

The NASA Aeronautics Research Mission Directorate (ARMD) is bringing together public, private, and academic organizations to collaborate with NASA in Urban Air Mobility (UAM) working groups. The primary purpose of the working groups is to accelerate the development of safe, high-volume UAM flight operations in the existing and anticipated future national airspace system. In coordination with the Grand Challenge series, industry developments and other ARMD efforts, these working groups will contribute to the enablement of UAM markets. Working group activities will seek to align the community on concepts to enable UAM, including communicating industry demonstrations and R&D priorities. The collaborative efforts will facilitate the identification of critical parameters and standards necessary to develop the framework and approach for safety, certification, airspace, aircraft concepts, technologies, and architectures. The realization of the UAM vision will be possible only though the efforts and contributions of multiple stakeholders, each possessing the necessary authority, expertise, and/or resources to fulfill a critical role in UAM's development, approval, and implementation. The applicable participants will become integral to the UAM working groups and will provide a platform for those stakeholders to collaborate and impact the overall ecosystem.

Kopardekar, Parimal H.↗

NASA NTRSDate not supplied

Feasibility Study for Remote Psychoacoustic Testing of Human Response to Urban Air Mobility Vehicle Noise

NASA will remotely administer a psychoacoustic test in late summer of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. This study relies on the cooperation of multiple government agencies, academia, and industry to assemble a wide range of UAM vehicle sounds. This database of sounds will be used to create a rich database of human response to UAM noise that would be challenging for a single organization to acquire. The development of the remote test method to study human response to aviation noise was prompted by the novel coronavirus pandemic. The feasibility portion of the study described in this work will demonstrate and refine the remote test method for use in the implementation phase. This paper details the method for remotely administering the psychoacoustic test and the sound stimuli to be used in the Feasibility Test. Comparisons of annoyance response data from previous in-person tests will be used to demonstrate the viability of the remote test method. The paper also describes an effort to determine if providing a contextual cue to test subjects influences the annoyance response.

UAM Vehicle Noise Human Response Study↗

NASA NTRSDate not supplied

Feasibility study for remote psychoacoustic testing of human response to urban air mobility vehicle noise

NASA will remotely administer a psychoacoustic test in late summer of 2022 as the first of two phases of a cooperative Urban Air Mobility (UAM) vehicle noise human response study. This study relies on the cooperation of multiple government agencies, academia, and industry to assemble a wide range of UAM vehicle sounds. This database of sounds will be used to create a rich database of human response to UAM noise that would be challenging for a single organization to acquire. The development of the remote test method to study human response to aviation noise was prompted by the novel coronavirus pandemic. The feasibility portion of the study described in this work will demonstrate and refine the remote test method for use in the implementation phase. This paper details the method for remotely administering the psychoacoustic test and the sound stimuli to be used in the Feasibility Test. Comparisons of annoyance response data from previous in-person tests will be used to demonstrate the viability of the remote test method. The paper also describes an effort to determine if providing a contextual cue to test subjects influences the annoyance response.

Remote psychoacoustic test↗

NASA NTRS2024

Extended Investigation into Fault-Tolerant Integrated Motor Drive for a Quadrotor Urban Air Mobility (UAM) Aircraft

Electric propulsor machines and their associated power electronics have been identified in past studies as low-reliability components in emerging Urban Air Mobility (UAM) Vertical Takeoff and Landing (VTOL) vehicles that raise their predicted catastrophic failure rates. This research effort extends previously presented work investigating the use of fault-tolerant (FT) motor drives in quadrotor aircraft as a promising approach to significantly increase their mean-time-to-failure (MTTF). In particular, fault-tolerant modular motor drives (FT-MMDs) are identified as strong candidates for closing the electric propulsor reliability gap. These FT-MMDs divide a machine drive into multiple redundant modules, each consisting of three phase stator windings and power electronics units, enabling continued machine operation after a module failure. Achieving the highest possible isolation between modules (physical, magnetic, thermal, electrical) is critically important in FT-MMDs to prevent the propagation of any failure between modules. Key additional requirements for achieving the highest possible reliability characteristics with FT-MMDs are high repair rates and aggressive suppression of all single-point failures.

quadrotor↗

NASA NTRSDate not supplied

Capacity and Throughput of Urban Air Mobility Vertiports with a First-Come, First-Served Vertiport Scheduling Algorithm

In this paper, a first-come, first-served vertiport scheduling algorithm for Urban Air Mobility (UAM) was exercised to assess and compare the capacity and throughput of various vertiport configurations. The scheduler models each vertiport by the number of vertipads and parking spaces, and manages reservations on timelines for those vertiport resources, at a level of fidelity suitable for fast-time and system-level analyses of UAM concepts and other airspace studies. The paper defines the theoretical model that can be used to estimate the capacity of various vertiport configurations. The theoretical model provides an understanding of the conditions that can lead to either a parking space-limited or a vertipad-limited vertiport. Examples of potential throughput for some vertiport configurations are provided using both a queueing approach as well as a simulated UAM demand scenario. The study demonstrated that a first-come, first-served scheduling approach can have inefficiencies in the use of the vertiport resources. The inefficiencies can increase as the number of resources increases. Nonetheless, 80% or better peak throughput to capacity ratio was observed for most vertiport configurations.

UAM↗

NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗

NASA NTRSDate not supplied

Airspace Research and Development Portfolio Assessment of Urban Air Mobility using Knowledge Graph Data Science

National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of Urban Air Mobility (UAM) operations. The UAM vision is one in which advanced technologies and new operational procedures enable practical and cost-effective air transport as an integrated mode of movement of people and goods throughout metropolitan areas. To safely support UAM operations at scale in the National Airspace System (NAS), NASA’s Air Traffic Management-Exploration (ATM-X) project has been conducting research that evolves the UAM air traffic management system towards a highly automated and operationally flexible system of the future. The complexity of UAM airspace evolution to accommodate the increasing tempo of UAM operations over time is managed through the UAM airspace research roadmap, which is a system engineering approach to the R&D of complex system-of-systems, where system’s interdependencies make it nearly impossible to define requirements for individual elements of the system in isolation. These interdependencies form a knowledge graph (node-link network) with a highly complex structure far beyond the human user’s ability to extract insights for project management’s research portfolio assessment. This study applies advanced data analytics in knowledge graph to the UAM knowledge graph to facilitate the portfolio assessment.

ATM↗

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 NTRS2020

Reliable, Secure, and Scalable Communications, Navigation, and Surveillance (CNS) Options for Urban Air Mobility (UAM)

The Aeronautics Research Mission Directorate directed a study to identify, evaluate, and recommend viable communications, navigation, and surveillance systems and technologies to enable safe, secure, and efficient Urban Air Mobility operations in US cities. The focus is on UML-4, when commercial air taxi operations take place in all weather, are widespread, and include autonomous systems. This report recommends technologies for independent navigation, collaborative communication, and surveillance, based on capacity, availability, precision, and update rate, size, weight, power, cost and other values. Recommendations for maturing and implementing the technologies are included.

Virginia L. Stouffer↗

NASA NTRSDate not supplied

Considerations for Optimal Sensor Placement for Higher Accuracy Object Localization for Urban Air Mobility

Previous research into object localization has shown that sensor placement and alignment plays an important role in achieving higher accuracy levels of the estimated location of a tracked Urban Air Mobility Vehicle. In general, a near-orthogonal intersection between the ground node observation vectors results in the highest accuracy due to a smaller overlapping uncertainty region between both. This applies to triangulation by means of ground node camera angle observations as well as trilateration by means of ground node distance measurements. However, this simple concept is not easily fulfilled with a network of a limited number of static ground nodes and a moving object to be localized. This case study performs sensitivity analyses and explores practical ways on how to achieve higher estimate accuracy levels in this context.

sensor placement↗