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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Nasa Scaled Power Electrified Drivetrain

A new transportation system is upon us, and it aims to satisfy the increasing need for air transportation. Advanced Air Mobility (AAM) has the potential to connect cities and increase air transportation capabilities and services. NASA recognizes that there is a need for standards and technology development to ensure the safety and reliability of future AAM aircraft. The NASA Revolutionary Vertical Lift Technology (RVLT) Project is using testbed data to satisfy these needs. One of these testbeds is the Scaled Power ElEctrified Drivetrain (SPEED). SPEED is a 400 VDC, 6 kW continuous, electrified aircraft propulsion system which is used to calibrate equipment, develop procedures, and perform tests at a reduced power level. This paper describes the testbed and the work it has supported at NASA.

Patrick A Hanlon↗
NASA NTRS2023

NASA Scaled Power ElEctrified Drivetrain

A new transportation system is upon us, and it aims to satisfy the increasing need for air transportation. Advanced Air Mobility (AAM) has the potential to connect cities and increase air transportation capabilities and services. NASA recognizes that there is a need for standards and technology development to ensure the safety and reliability of future AAM aircraft. The NASA Revolutionary Vertical Lift Technology (RVLT) Project is using testbed data to satisfy these needs. One of these testbeds is the Scaled Power ElEctrified Drivetrain (SPEED). SPEED is a 400 VDC, 6 kW continuous, electrified aircraft propulsion system which is used to calibrate equipment, develop procedures, and perform tests at a reduced power level. This paper describes the testbed and the work it has supported at NASA.

Patrick Hanlon↗
NASA NTRSDate not supplied

Initial Development and Integration of a Vertiport Automation System for Advanced Air Mobility Operations

The High Density Vertiplex Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference Vertiport Automation System (VAS) to support the management of arrival and departure operations in the terminal area of a vertiport or network of inter-connected vertiports known as a vertiplex. The VAS is potentially a key element of an integrated AAM architecture that will enable greater levels of coordination and predictability for vertiport users through its integration and sharing of information, and greater control of vertiport resources for managing operators. This paper provides an overview of the VAS and a description of an initial implementation that has been integrated into a broader reference architecture as part of ongoing research. Examples from recent simulations are presented with an outline for next steps and considerations.

vertiport↗
NASA NTRS2023

Distributed Sensing and Reasoning for Advanced Air Mobility Health Management and Mission Assurance

As envisioned, Advanced Air Mobility (AAM) and Urban Air Mobility (UAM) will introduce new vehicles and operations within the national airspace, moving people and cargo safely and efficiently at a much larger scale than today. Driven by transformative technology and revolutionary aircraft, this movement must still manage technical, regulatory, operational, and policy challenges. NASA’s work in support of AAM and UAM includes, but is not limited to tools, technologies, and architectures for distributed sensing of aircraft, data & reasoning services exchange, Human-Autonomy Teaming (HAT), contingency management, and vehicle health management. This paper builds upon these concepts and evaluates the use of distributed sensing and infrastructure assistance towards health management and mission assurance of UAM vehicles in specific operational scenarios. Through analysis of these example missions, aided by the data produced by the conceptual distributed sensing and reasoning infrastructure, we define opportunities for state estimation, diagnosis, and key decision points affecting the health state of the vehicle and the airspace volume. As a result, we define a number of measurable health state parameters providing relevant information to drive decision-making in contingency situations or feed automation tools in support of operators and managers.

Safety↗
NASA NTRSDate not supplied

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class-E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution. Overall, the ETM tabletop provided insights into how the community wanted to instantiate the generation and sharing of operational intent, detect strategic conflicts and resolve those conflicts using a preliminary set of procedural community-agreed upon COPs.

Upper Class-E Traffic Management (ETM)↗
NASA NTRSDate not supplied

Assessing Helicopter Pilots’ Detect and Avoid and Collision Avoidance Performance With ACAS Xr

The latest variant of the Federal Aviation Administration’s Airborne Collision Avoidance System (ACAS X) is being designed for both crewed and uncrewed rotorcraft. Referred to as ACAS Xr, the system joins a suite of other ACAS X variants poised to replace the Traffic Alert and Collision Avoidance System (TCAS II). ACAS Xr is tuned to support current-day helicopter platforms as well as electric Vertical Takeoff and Landing (eVTOL) vehicles that are still under development. Given this flexibility, ACAS Xr may be used by helicopter crews currently in operation or by remotely-operated eVTOL aircraft in the emerging Advanced Air Mobility (AAM) market. To cover the range of potential uses, two distinct configurations are being proposed for ACAS Xr: Collision Avoidance System (CAS) and Detect and Avoid (DAA). Under the CAS configuration, ACAS Xr provides minimal caution-level alerting but issues directive warning-level alerting and guidance. The DAA configuration, by contrast, provides caution-level alerting and guidance, in addition to the warning-level alerting and guidance. The current study was performed as part of the National Aeronautics and Space Administration’s AAM project. Six helicopter pilots were recruited to fly a variety of scripted traffic scenarios in a full-motion, crewed eVTOL simulator. Participants flew 60 encounters over two days, reacting to pre-recorded intruder aircraft that were scripted to fly into the participant’s aircraft from different approach angles, relative altitudes, and during different phases of flight. The pilots flew half of the encounters with the CAS configuration and half with the DAA configuration. Within each block of 30 encounters, pilots experienced 10 conflicts while in cruise, 10 in hover, and 10 while on approach to a heliport. Results showed that pilot response times were consistently under 5 seconds for RAs and under 10 seconds for DAA alerts, when present, during all three phases of flight. Unsurprisingly, the DAA configuration was associated with lower rates of en-route and high-severity losses of DAA well clear compared to the CAS configuration in all phases of flight except for the terminal area. Rates of losses of DAA well clear were found to be substantially higher in the Hover scenario, compared to Cruise. Pilots failed to fully comply with RAs at a rate of 0.10-0.18 in all conditions except for the DAA configuration in the Hover scenario, which was associated with a higher non-compliance rate of 0.4 due to Descend RAs issued at low altitudes. The implications of these results with regards to the ongoing development of ACAS Xr is discussed.

air taxis↗
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

Integration of Automated Systems (IAS) Flight Test Overview

The Integration of Automated Systems (IAS) Project is conducting a series of 2023 flight tests supporting NASA's Advanced Air Mobility (AAM) and National Campaign efforts. These flights include crewed, test (i.e., ownship) and traffic (i.e., intruder) aircraft that will fly with unique technologies onboard. The presentation will include overviews of the Hazard Perception and Avoidance (HPA) and Flight Path Management (FPM) technical areas but will focus primarily on HPA. HPA will test the FAA's Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant, ACAS Xr, is designed to accommodate existing helicopter platforms and in-development, vertical takeoff and landing (VTOL) concepts, which are critical to the emerging AAM concept of operations. Two configurations of ACAS Xr will be examined: Collision Avoidance System (CAS, similar to the Traffic Collision Avoidance System [TCAS] II) and Detect and Avoid (DAA, previously developed to provide added situational awareness for uncrewed aircraft). Additionally, this system will be explored during cruise and low-speed flight as well as flights within en-route, structured (i.e., dense/urban), and terminal airspaces. Scripted flight conflicts will be conducted, and these conflicts will be mitigated through maneuvers that are manual (i.e., performed by the pilots) or automated (i.e., achieved by the cooperation of the program middleware and onboard ownship systems). Objective data will be collected involving system and pilot performance as well as pilot decisions; subjective data will include pilot opinions of ACAS Xr's alerting and guidance as well as the automated maneuvers.

detect and avoid↗
NASA NTRSDate not supplied

Roadmap to Cooperative Operating Practices for Strategic Conflict Detection and Resolution in the Upper Class E Traffic Management Concept

As the governing body of flight operations in the highly anticipated emergent area of Upper Class E airspace (60,000 ft and above), the Federal Aviation Administration (FAA) has recognized the potential for a possible extensible traffic management system for new entrants into this domain. Following the successes with Unmanned Aircraft Systems (UAS) Traffic Management (UTM) and Advanced / Urban Air Mobility (AAM / UAM) Traffic Management programs, FAA put forth an initial concept of operations for supporting the start of the Upper Class E Traffic Management (ETM) concept. Like UTM and AAM / UAM, ETM is envisioned to also be a community-based, industry driven cooperative management concept. However, tailoring it to be adaptable to the atmospheric communication, navigation, and surveillance deficits, as well as the diverse vehicle and mission profiles operating in the ETM environment will be the challenge. As such, the National Aeronautics and Space Administration (NASA) Ames Research Center has been investigating several technologies that will help enable industry in the development of this new type of cooperative operating environment. These technologies are being prototyped and will be tested in a collaborative evaluation of an initial ETM system in late 2023. The evaluation will concentrate on building out ETM system technologies that will inform the participants regarding operational intent sharing, strategic conflict detection, and the resultant deconfliction process. In addition to the technical aspects, key roles and responsibilities will need to be defined. This will be done through exploring community-agreed upon Cooperative Operating Practices (COPs) that include procedures and capabilities to aid in timely, strategic conflict identification and resolution to be developed during the evaluation. As an initial step to the evaluation, the ETM research team at NASA Ames solicited industry feedback on various aspects of ETM operations from subject matter experts. A virtual tabletop walkthrough session was held over a two-day period to follow a roadmap through the functional steps needed to build COPs, focusing on strategic conflict detection and resolution.

Upper Class E Traffic Management (ETM)↗
NASA NTRSDate not supplied

Assessing Helicopter Pilots’ Detect and Avoid and Collision Avoidance Performance with ACAS Xr

The latest variant of the Federal Aviation Administration’s Airborne Collision Avoidance System (ACAS X) is being designed for both crewed and uncrewed rotorcraft. Referred to as ACAS Xr, the system joins a suite of other ACAS X variants poised to replace the Traffic Alert and Collision Avoidance System (TCAS II). ACAS Xr is tuned to support current-day helicopter platforms as well as electric Vertical Takeoff and Landing (eVTOL) vehicles that are still under development. Given this flexibility, ACAS Xr may be used by helicopter crews currently in operation or by remotely-operated eVTOL aircraft in the emerging Advanced Air Mobility (AAM) market. To cover the range of potential uses, two distinct configurations are being proposed for ACAS Xr: Collision Avoidance System (CAS) and Detect and Avoid (DAA). Under the CAS configuration, ACAS Xr provides minimal caution-level alerting but issues directive warning-level alerting and guidance. The DAA configuration, by contrast, provides caution-level alerting and guidance, in addition to the warning-level alerting and guidance. The current study was performed as part of the National Aeronautics and Space Administration’s AAM project. Six helicopter pilots were recruited to fly a variety of scripted traffic scenarios in a full-motion, crewed eVTOL simulator. Participants flew 60 encounters over two days, reacting to pre-recorded intruder aircraft that were scripted to fly into the participant’s aircraft from different approach angles, relative altitudes, and during different phases of flight. The pilots flew half of the encounters with the CAS configuration and half with the DAA configuration. Within each block of 30 encounters, pilots experienced 10 conflicts while in cruise, 10 in hover, and 10 while on approach to a heliport. Results showed that pilot response times were consistently under 5 seconds for RAs and under 10 seconds for DAA alerts, when present, during all three phases of flight. Unsurprisingly, the DAA configuration was associated with lower rates of en-route and high-severity losses of DAA well clear compared to the CAS configuration in all phases of flight except for the terminal area. Rates of losses of DAA well clear were found to be substantially higher in the Hover scenario, compared to Cruise. Pilots failed to fully comply with RAs at a rate of 0.10-0.18 in all conditions except for the DAA configuration in the Hover scenario, which was associated with a higher non-compliance rate of 0.4 due to Descend RAs issued at low altitudes. The implications of these results with regards to the ongoing development of ACAS Xr is discussed.

air taxis↗
NASA NTRS2023

National Campaign Development Test Executive Summary

NASA’s vision for Advanced Air Mobility (AAM) is to provide safe, sustainable, accessible, and affordable aviation for transformational local and intraregional missions and includes the transportation of passengers and cargo as well as aerial work missions, such as infrastructure inspection or search and rescue operations. NASA’s technical expertise, intergovernmental relationships, and high level of public trust will help this technology come to market concurrent with infrastructure readiness, public acceptance, and constructive regulation. By advising and integrating disparate AAM efforts across the country and working collaboratively with the FAA, the National Campaign (NC) objective is to motivate industry progress and support the development of policy, regulatory, and technical standards in a manner that best ensures public safety and benefit to the American people. NC began with the Dry Run and Developmental Test (NC-DT), which served as a pathfinder for collaboration and direct involvement with industry partners in integrated simulation exercises and flight tests. NC-DT culminated with an acoustics-gathering flight test in September 2021 with industry partner Joby’s prototype S4 2.0 air vehicle, which delivered the first foundational baseline of noise levels present in Electric Vertical Takeoff and Landing (eVTOL) vehicles. Through the DT phase, the NC team built and tested the airspace and range infrastructure while assessing the readiness level of industry partners leading up to future NC events. The purpose of this paper is to provide an overview of NC-DT.

National Campaign↗
NASA NTRS2024

The Knowledge-based Digital Platform Concept for Advanced Air Mobility Research and Development

National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers (SE) together across organizational boundaries. The overarching vision for the KbDP Concept for AAM R&D is a substantial undertaking. The initial concept and implementation will focus on UAM operations to tractably learn and adjust the concept with a manageable database. Lessons learned and best practices with a smaller scope will enable successful scalability to AAM R&D or even to the entire modes of transportation and logistics. 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. Initial implementation of three KbDP concepts of use shows promising benefits to NASA’s Air Traffic Management-Exploration (ATM-X) UAM Airspace Subproject. It is envisioned that the KbDP will manage an information database defined by mathematical, data science, and system engineering principles. AIML algorithms play a vital role in this KbDP concept by extracting meaningful knowledge from the information database, which the human user leverages to improve the efficiency and effectiveness of their research greatly.

ATM↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
NASA NTRSDate not supplied

MBSE Execution of Scalable Autonomous Operations for a High Density Vertiplex

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE) and NASA SE processes were executed via MBSE using MagicDraw. Since the adoption of MBSE and utilization of MagicDraw, the systems engineering team has made tremendous strides in each pillar of MBSE including, requirements, behavior, and structure. Scalable Autonomous Operations (SAO) was a stage in the development of the High Density Vertiplex focusing on the autonomous terminal operations of a vertiport with sUAS aircraft. MBSE served the systems engineering team to document and verify the physical architecture and capture a logical architecture of SAO for distribution to the AAM community. This paper will detail methodologies that were created to successfully execute NASA SE processes via MBSE in the SAO stage as well as highlight challenges and lessons learned.

Demetrios Katsaduros↗
NASA NTRSDate not supplied

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

high density vertiport↗
NASA NTRSDate not supplied

Integration of Automated Systems (IAS) Flight Test - Hazard Perception & Avoidance (HPA) Results

The Integration of Automated Systems (IAS) flight test series concluded in October 2023 in support of NASA's Advanced Air Mobility (AAM) project. These flights include crewed, test (i.e., ownship) and traffic (i.e., intruder) aircraft that flew with unique technologies onboard. The presentation includes overviews of the flight tet itself and the specific results that pertain to the Hazard Perception and Avoidance (HPA) technical areas. HPA tested the FAA's Airborne Collision Avoidance System X (ACAS X), a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant, ACAS Xr, is designed to accommodate existing helicopter platforms and in-development, vertical takeoff and landing (VTOL) concepts, which are critical to the emerging AAM concept of operations. Two configurations of ACAS Xr were examined: Collision Avoidance System (CAS, similar to the Traffic Collision Avoidance System [TCAS] II) and Detect and Avoid (DAA, previously developed to provide added situational awareness for remote pilots). Additionally, this system was flown in cruise and low-speed flight regimes as well as within en-route and (emulated), structured (i.e., dense/urban), and terminal airspaces. Results include the types of alerts generated by ACAS Xr across the different configurations, the distances at which the alerts were generated, response times, manuever sizes, miss distances, and general comments from pilots. Key takeaways and next steps are also provided.

detect and avoid↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph With ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) con-cept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze require-ments within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗
NASA NTRSDate not supplied

Requirement Discovery Using Embedded Knowledge Graph with ChatGPT

The field of Advanced Air Mobility (AAM) is witnessing a transformation with innovations such as electric aircraft and increasingly automated airspace operations. Within AAM, the Urban Air Mobility (UAM) concept focuses on providing air-taxi services in densely populated urban areas. This research introduces the utilization of Large Language Models (LLMs), such as OpenAI's GPT-4, to enhance the UAM Requirement discovery process. This study explores two distinct approaches to leverage LLMs in the context of UAM Requirement discovery. The first approach evaluates the LLM's ability to provide responses without relying on additional outside systems, such as a relational or graph database. Instead, a vector store provides relevant information to the LLM based on the user’s question, a process known as Retrieval Augmented Generation (RAG). The second approach integrates the LLM with a graph database. The LLM acts as an intermediary between the user and the graph database, translating user questions into cypher queries for the database and database responses into human-readable answers for the user. Our team implemented and tested both solutions to analyze requirements within a UAM dataset. This paper will talk about our approaches, implementations, and findings related to both approaches.

systems engineering↗