An Overview of Advanced Air Mobility (AAM) and NASA’s AAM Mission
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Advanced Air Mobility↗
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Advanced Air Mobility (AAM) Mission: AAM Maturity Levels (AML)
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Joshua Kyle Jenkins↗
High Level Architecture (HLA) as a Research Tool for Advanced Air Mobility (AAM)
In this document, a NASA investigation into the High-Level Architecture (HLA) standard for distributed simulation as a means of furthering Advanced Air Mobility (AAM) research is illustrated along with a description of the evolution of distributed simulation leading to the HLA standard. HLA enables efficient definition and insertion of components and participants in simulation sessions. Novel concepts can be added or removed as the work evolves. A general history of the HLA standard is described to show past needs for improved methods of distributed simulation, particularly among participating laboratories in separate locations, as well as previous applications of HLA simulation to airspace research. These examples from the past illustrate how new and emerging concepts can be simulated by layering notional ideas over simulated current-day systems and capabilities in a controlled environment for examination. In the 1990s, the Federal Aviation Administration (FAA) and the MITRE Corporation performed joint research into then-emergent capabilities in the National Airspace System (NAS). In the early 2000s, the MITRE Corporation developed an open, extendable set of objects and protocols designed for global and collaborative aviation research called AviationSimNet, which has HLA as its foundation. This led to NASA and the FAA using AviationSimNet to connect laboratories for AAM research, resulting in the NASA/FAA Laboratory Integrated Test Environment (NFLITE). The underlying capabilities of HLA enabled testing a notional interaction between AAM and legacy ATC systems that resulted in AAM vehicles receiving beacon codes for identification on ATC screens in Class C airspace. The potential next steps in this work include defining and examining notional interactions between AAM and legacy ATC systems within Class B airspace.
UAM↗
A Concept of Operations (ConOps) of an In-time Aviation Safety Management System (IASMS) for Advanced Air Mobility (AAM)
The growth of new emerging operations involving Advanced Air Mobility (AAM) necessitates developing a perspective for an In-time Aviation Safety Management System (IASMS). This perspective advances from the National Academies report on IASMS and its recommendation for developing a Concept of Operations (ConOps) for IASMS. A ConOps has been developed for In-time System-Wide Safety Assurance (ISSA) from which the IASMS ConOps pivots to provide a robust scope commensurate with the broad vision defined by the National Academies. The IASMS ConOps focuses on emerging operations and spans innovations in Unmanned Aircraft System (UAS) and an increasingly complex ecosystem comprised of a widening mix of vehicles and technologies, Urban Air Mobility (UAM) with industry-federated services, traditional operations, as well as new supersonic aircraft and space launch systems.The challenge for the IASMS ConOps is to be broad to encompass innovations in the coming years and decades while agile to ensure levels of safety compatible with operational and certification requirements of the National Airspace System (NAS). The IASMS ConOps interweaves increasing complexity of operational safety capabilities and unlocking UAS Maturity Levels (UMLs). The relationships between increased complexity of automation and automated systems, fewer operators who are not as traditionally higher skilled, more complex operational environments, and aviation operations management with mixed aircraft and equipage pose a multi-dimensional space for IASMS capabilities essential for safety assurance and risk management. Instantiating IASMS capabilities and how they would be integral to AAM operations and increasing maturity of UAM could be accomplished through a series of Safety Demonstrators. These Safety Demonstrators could provide increased understanding and insight into use of controls for risk mitigation, means of compliance for certification, and operational experience with safety services such as in relation to contingency management. The IASMS capabilities can be viewed as initially residing with the vehicle, airspace, and Supplemental Data Service Provider (SDSP). For example, vehicle capabilities include communications including the command and control link, Remote Identification (ID), conflict advisory/alerting, and UAS system monitoring. These capabilities monitor and assess data such as battery health, aircraft state, and human performance. Complexity of ISAMS capabilities depends on a number of factors. These factors are intendedonly as a notional categorization with the purpose being to reflect the complexity of the AAM ecosystem that would drive up the complexity of ISSA capabilities including systems, sensors, models, standards, and controls. Factors could include the Vehicle Flight Management, Environment, Airspace, and Contingency Management. Each of these factors can be comprised of multiple sub-factors that contribute to increasing complexity. For example, Airspace at a lower level of complexity could be dedicated to UTM operations that are unmonitored, and at a higher level of complexity could involve mixed UTM and ATM operations. The IASMS concept includes safety services that provide data and information to different participants in AAM. The roles and responsibilities of participants can be defined using the Responsible-Accountable-Consulted-Informed (RACI) analysis. For example, for the safety service involving the Remote ID, the Operator would be accountable for providing the data, the Vehicle would be responsible for transmitting it, and the USS, SDSP, Vertiports, FIMS (FAA), and Public Entities such as safety services would be informed by receiving the data. The IASMS ConOps identifies the capabilities needed for risk mitigation and safety assurance in the increasingly complex national airspace. The ConOps serves as a pathway for engaging with industry to gain operational experience including through the Safety Demonstrator series, the RACI analysis, and operational complexity factors. The ConOps serves to integrate these different perspectives to build a cohesive and cogent approach to an AAM safety management system.
In-Time Aviation Safety Management System↗
Defining Services, Functions, and Capabilities for an Advanced Air Mobility (AAM) In-time Aviation Safety Management System (IASMS)
NASA’s vision for Advanced Air Mobility (AAM) Mission is to help emerging aviation markets to safely develop an air transportation system that moves people and cargo between places previously not served or underserved by aviation. The integration of new operational paradigms and vehicle classes in this system requires a transformation of the National Airspace System (NAS) that includes substantive changes critical for assuring safety. These changes are compelled by unique challenges posed by AAM to the safety management system (SMS). These challenges were assessed by committees of the National Academies in their reports on a vision for an In-time Aviation Safety Management System (IASMS) and a blueprint for AAM [1,2]. In their description of an IASMS, the top recommendation was development of a concept of operations (ConOps) for IASMS. This paper describes the high-priority recommendations from the National Academies for its IASMS vision and how they are addressed through a distributed system-of-systems architecture. The IASMS architecture is structured on the services, functions, and capabilities (SFCs) necessary for In-time System-wide Safety Assurance (ISSA)initially developed for urban air mobility (UAM). The paper then posits where these SFCs would reside across vehicles, airspace, or service suppliers such as Supplemental Data Service Providers (SDSPs), and how SFCs scale with increasing complexity in design and operations of AAM. SFCs are foundational building blocks for a system that targets an individual or family of risks using a Monitor-Assess-Mitigate risk paradigm for anomalies, precursors and trends. An IASMS could be conceived that uses a portfolio of SFCs for AAM in general or prioritizes SFCs for a specific domain or operation.
K Ellis↗
The Impact on EOP Predictions of AAM Forecasts from the ECMWF and NCEP
Predictions of UT1 are improved when dynamical model-based forecasts of the axial component of atmospheric angular momentum (AAM) are used as proxy length-of-day (LOD) forecasts (Freedman et al. 1994; Johnson et al. 2005). For example, the accuracy of JPL's predictions of UT1 are improved by nearly a factor of 2 when AAM forecast data from the National Centers for Environmental Prediction (NCEP) are used. Given the importance of AAM forecasts on the accuracy of UT1 predictions, other sources of AAM forecasts should be sought. Here, the angular momentum of the forecasted wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) are computed and used to predict UT1. The results are compared to those obtained using NCEP forecasts.
atmospheric angular momentum (AAM)↗
Advanced Air Mobility (AAM) Vertiport Automation Trade Study
The NASA Advanced Air Mobility (AAM) Vertiport Automation Trade Study seeks to understand the barriers to scaling AAM takeoff and landing facilities, described in this study as vertiplaces. This trade study provides an overview of AAM, vertiplaces, and insight into the gaps in capability, regulatory certainty, and knowledge needed to sufficiently manage the volume of expected traffic anticipated for vertiplaces. The study also outlines potential mitigations to those gaps based on research conducted by the project team and interviews with 23 individuals in government and industry. In this report, the elements that make up a vertiport are discussed. The study then defines a vertiplace and introduces a concept for vertiplace categorization based on capability. Capability gaps and mitigations to increasing scale and automation of vertiplaces are then discussed based around four cross-cutting themes the research of our project team found in the interviews and research: Technology, Physical Infrastructure, Policy, and Community Acceptance. The study then concludes by discussing topics requiring further research that were identified by interviewees.
Vertiport↗
A HIRF-Map Certification Approach for UAM, AAM, and UAS Vehicles
Advanced Air Mobility (AAM), Urban Air Mobility (UAM), and Unmanned Aerial Systems (UAS) vehicles will fly in airspace similar to Transport Category Rotorcraft, requiring them to meet stringent requirements for High-Intensity Radiated Fields (HIRF) certification. The environment is notably severe, particularly compared to fixed-wing aircraft, due to operations at lower altitudes. This potentially exposes the vehicles to high-power transmitters on the ground, leading to significant challenges. High-level HIRF exposure can result in avionic system upsets, interference, and undesirable effects. Shielding and circuit protection against HIRF pose significant barriers concerning size, weight, and cost, especially for emerging electric vertical take-off and landing (eVTOL) and electric short take-off and landing (eSTOL) vehicles. This paper proposes a novel HIRF protection approach, aiming to reduce costs by certifying vehicles to a "vehicle tolerance level" lower than that required for Transport Category Rotorcraft. The remaining protection is achieved by maintaining a safe distance from known HIRF sources. This distance is calculated based on the vehicle's tolerance level and transmitter characteristics, such as transmit power, antenna beamwidth, and direction. Tailored flight maps are developed to identify transmitters and avoidance zones within an operating area or along a flight path, enabling restricted vehicle operations. Vehicles with higher tolerance levels could have smaller HIRF avoidance zones, allowing them to operate closer to transmitters. Transmitter data are sourced from government databases, such as those of the Federal Communications Commission (FCC) and the National Oceanic and Atmospheric Administration (NOAA). A map tool is developed using Matlab to calculate and visualize HIRF avoidance zones based on available databases. The tool determines stand-off distances based on input vehicle tolerance levels, displaying results on various base maps depicting HIRF-restricted areas. Illustrations cover various FCC transmitters, including AM/FM/TV transmitters, satellite earth stations, and NOAA weather radars. The HIRF zones of smaller transmitters, such as land-mobile radios, pagers, microwave links, and cellular towers, are also illustrated. An example of flight planning around transmitters is provided. For now, airports and government-owned lands are excluded due to limited access to sensitive transmitter data. Despite this approach, a minimum HIRF tolerance level for vehicles may still be necessary to cover mobile devices, cellular base stations, transmitters on other AAM vehicles, and other small power devices not included in the FCC databases. The paper discusses findings and areas for improving existing databases and suggests an approach for better access to sanitized data in more restricted government databases. This method significantly deviates from the standard approach and introduces slightly higher flight-planning complexity. However, the potential cost savings are considerable. Future AAM/UAM/UAS aeronautical charts could potentially incorporate these new HIRF avoidance zones. Keywords—HIRF; Map; AAM; UAM; UAS; Advanced Air Mobility; Urban Air Mobility; Unmanned Aerial Systems; Certification.
HIRF↗
National League of Cities AAM Presentation
Advanced Air Mobility (AAM) Mission Overview given to the National League of Cities on May 4, 2021. Presentation included an overview of AAM highlights in the AAM Ecosystem and community integration status.
Nancy L. Mendonca↗
AAM NC ATI TechTalk - Aerograph Architecture v1
Aerograph is NASA’s data management system for Advanced Air Mobility. Its mission is to support AAM research by providing a reliable and secure system that collects, stores, protects, and shares AAM data. Its vision is to provide a system that AAM research scientists, aerospace engineers, data scientists, and analysts trust for obtaining NC data and performing key analyses. The types of data Aerograph manages involves data related to flight test events, including: Aircraft Performance and Characterization (e.g., position reports) Airspace (e.g., operation intent, waypoints, and constraints) Environment (e.g., surface and wind weather) Infrastructure (e.g., surveillance coverage) Derivative Analytical Artifacts (e.g., glide path performance chart, 3D position chart, Integrated Data Product)
Aerograph↗
Demonstration of Data Processing and Fusion from Distributed Radars for AAM Surveillance
Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles (UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with three independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10% increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by two of the radars.
Federica Vitiello↗
NASA Advanced Air Mobility (AAM)
This presentation will provide a UTM update, AAM overview (including details on the AAM Ecosystem Working Groups), and Regional Modeling and Simulations tool description.
Advance Air Mobility↗
Identifying Human Factors Research for Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM)
This paper identifies some of the key human factors (HF) challenges when integrating Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) into the civil airspace. Unique HF considerations—those which are derived from the key differentiating aspects of UAS/AAM compared to conventional aviation—are the primary basis for identifying HF research opportunities. By identifying what makes UAS and AAM fundamentally different from conventional aviation, from a human integration perspective, HF research can be targeted to effectively inform best practices, standards, policy, guidance, and regulations associated with aircraft and air traffic systems and operations. HF research areas are discussed within the following topic areas: Sustained low-altitude operations; loss of natural sensing; novel aircraft; novel operations; link management and lost link; link performance; distributed pilot teams; and increased automation. The identified research descriptions are intended to serve as illustrative examples of what research is fundamental, and why. They are not intended to prescribe, prioritize or exclude research.
AAM↗
Resilience Engineering's Potential for Advanced Air Mobility (AAM)
The national airspace (NAS) will rapidly evolve in the next ten to twenty years. Plans for Advanced Air Mobility (AAM) during that period envision highly automated airspace management systems and electrically powered vehicles. AAM concepts also anticipate limited human roles. The goal of limiting the human role is to minimize the potential for misadventures, yet how the human role is limited needs to be carefully considered in order to also preserve the potential for human successes. The field of resilience engineering (RE) focuses on how systems can change in order to seize an opportunity or withstand an unforeseen challenge. RE methods rely on the use of empirical data to optimize the ability of any system to adapt. RE studies have shown how individual and team initiatives ensure resilient system performance by creating safety through flexibility. Benefits of the RE approach include improved awareness of operational circumstances and how system elements depend on each other, and the ability to allocate limited resources and prepare for surprise. RE offers the ability to account for and incorporate the human role as an essential element in order to ensure NAS systems’ resilient performance. Data on the human contribution to safe and resilient system performance, which is termed “work as done,” are available but are not being considered as the NAS evolves. We present an approach that describes how use of RE can enable the evolving NAS to adapt, and perform, in a resilient manner.
Aviation Safety↗
An Overview of Nasa Research on Aam Noise and A Recent Noise and Number Study
NASA conducts psychoacoustic research on the human response to Urban Air Mobility (UAM) vehicles as part of its Advanced Air Mobility (AAM) mission. This presentation provides an overview of NASA’s role in AAM research with an emphasis on recent work investigating people’s response to frequent UAM vehicle flyover sounds.
UAM↗
UTM to AAM
This short presentation is intended to provide a a brief overview of the background of the UAS Traffic Management (UTM) concept and the legacy of work performed in the area of human factors as part of the project's research. The introduction of UTM provides the foundation for presenting the progression of the concept from low altitude airspace with small UAS to an environment with larger passenger and cargo carrying vehicles integrating into more complex airspace as part of the Advanced Air Mobility (AAM) concept. Related research and testing plans are presented to illustrate the direction of related project efforts as well as the facilities that are available to support the necessary research ahead.
UTM↗
AAM Supply Chain Working Group: Aerospace Supply Chain and Manufacturing
This presentation provides an overview of the aerospace supply chain and how the AAM Supply Chain Working Group will help to make it more sustainable and resilient. The urgent needs are building a tiered system, a modeling and simulation platform, and an electronic exchange platform.
AAM↗
Breaking Down Barriers to Operationalization: NASA’s AAM Ecosystem Approach
NASA's approach to developing partnerships, sharing and communicating with stakeholders, and disseminating results of the AAM research to our ecosystem partners.
Advanced Air Mobility↗