Results for “AAM” — Page 11

NASA NTRS2011

An Estimate of the Size and Shape of Sunspot Cycle 24 Based on its Early Cycle Behavior using the Hathaway-Wilson-Reichmann Shape-Fitting Function

On the basis of 12-month moving averages (12-mma) of monthly mean sunspot number (R), sunspot cycle 24 had its minimum amplitude (Rm = 1.7) in December 2008. At 12 mo past minimum, R measured 8.3, and at 18 mo past minimum, it measured 16.4. Thus far, the maximum month-to-month rate of rise in 12-mma values of monthly mean sunspot number (AR(t) max) has been 1.7, having occurred at elapsed times past minimum amplitude (t) of 14 and 15 mo. Compared to other sunspot cycles of the modern era, cycle 24?s Rm and AR(t) max (as observed so far) are the smallest on record, suggesting that it likely will be a slow-rising, long-period sunspot cycle of below average maximum amplitude (RM). Supporting this view is the now observed relative strength of cycle 24?s geomagnetic minimum amplitude as measured using the 12-mma value of the aa-geomagnetic index (aam = 8.4), which also is the smallest on record, having occurred at t equals 8 and 9 mo. From the method of Ohl (the inferred preferential association between RM and aam), one predicts RM = 55 +/- 17 (the ?1 se prediction interval) for cycle 24. Furthermore, from the Waldmeier effect (the inferred preferential association between the ascent duration (ASC) and RM) one predicts an ASC longer than 48 mo for cycle 24; hence, maximum amplitude occurrence should be after December 2012. Application of the Hathaway-Wilson-Reichmann shape-fitting function, using an RM = 70 and ASC = 56 mo, is found to adequately fit the early sunspot number growth of cycle 24.

Wilson, Robert M.↗

NASA NTRS2020

Designing and Training for Appropriate Trust in Increasingly Autonomous Advanced Air Mobility Operations: A Mental Model Approach: Version 1

To enable effective human-autonomy teaming (HAT) in Advanced Air Mobility (AAM) operations, the current paper presents a theoretical framework to design and train for appropriate trust in automation. The novel contribution of this work resides in connecting the construct of trust to mental models and showing how this method could be used to enable emerging HAT concepts such as Adaptive Trust Calibration. To contextualize this framework, in section 2 we discuss simplified vehicle operations (SVO) and remote vehicle operations (RVO), which are leading operational concepts within AAM. In section 3 we describe our perspective on automation and increasingly autonomous systems and present a brief discussion on human-automation interaction and human-autonomy teaming. In section 4 we provide a detailed discussion on the construct of trust in automation. In section 5 we present a framework that associates mental models with trust through principles of transparent design. Finally, in section 6 we present three descriptive models for designing and training for appropriate trust in increasingly autonomous systems.

Human-Autonomy Teaming↗

NASA NTRS1993

Earth Rotation and Global Change

Global climate change, whether from natural or man-made causes, can be expected to lead to changes in the Earth's rotation. General Circulation Models (GCMs) of the atmosphere that have been used to study the effects of changes in the amount of atmospheric greenhouse gasses general predict the globally averaged temperature to increase by 1 degree to 4 degrees C during the next century. This temperature increase is not expected to occur uniformly over the globe, but should exhibit regional variations. This changing atmospheric temperature field will cause changes in the atmospheric wind field and hence in the atmospheric angular momentum (AAM). Since the angular momentum of the atmosphere-solid Earth system is conserved, a thermally-induced change in the AAM will cause consequent changes in the rotation of the solid Earth...

Dickey, J. O.↗

NASA NTRS2021

Advanced Air Mobility and Safety Management Systems

Our current air transportation system has underserved markets, including local, regional, intraregional, and urban transportation of both people and cargo. Recent advances in aviation technology such as small highly-automated vehicles, electric aircraft, and automated air traffic are enabling business opportunities in these markets. Advanced Air Mobility, or AAM, refers to a community effort to overcome the gaps in operational rules, safety analysis, and overall acceptance, so that these new operations will be possible. For full details, please see: https://www.nasa.gov/aam As we think about how we can safely introduce these new operations, fundamental questions about how the structure of Safety Management Systems can be applied to AAM, and how that structure can be used to help us overcome the necessary technical and societal obstacles arise. Two of these questions are: • How do we tailor the requirements and desired level of monitoring and assessment to achieve safety given the breadth of possible operations and the associated risk of those operations? • How do we aid innovation by rapidly evaluating the safety of novel operations without losing associated rigor? We assume that a Safety Management System that enables these future systems will incorporate knowledge of the acceptable level of risk, and will utilize data science to automate the core monitor, assess and mitigate functions that will allow us to respond to risks and hazards in time to prevent safety incidents. In 2018, the National Academies proposed an In-Time Aviation Safety Management System (IASMS) that would advance these goals. (https://www.nap.edu/catalog/24962/in-time-aviation-safety-management-challenges-and-research-for-an) The National Academies made a clear distinction between in-time systems, in which hazards could be identified and risks mitigated in time to prevent incidents, and real-time systems, since many hazards and risks do not need real-time data and analysis to detect and mitigate.

In-Time Aviation Safety Management System↗

NASA NTRSDate not supplied

Urban Air Mobility (UAM) Vision Concept of Operations (ConOps) UAM Maturity Level (UML)-4 Overview

The Vision ConOps is intended as a foundation to engage members of the UAM community and provide a consensus on the future vision of UAM operations. It provides a concept for more detailed discussion and a basis for the exploration of ideas using a common framework to inform the continued development and integration of UAM as part of the broader transportation system. Advanced Air Mobility (AAM) encompasses a range of innovative aviation technologies (small drones, electric aircraft, automated air traffic management, etc.) that are transforming aviation’s role in everyday life, including the movement of goods and people. Urban Air Mobility (UAM) represents one of the most exciting and complex AAM concepts with highly automated aircraft, providing commercial services to the public over densely populated cities.

Brian Hill↗

NASA NTRSDate not supplied

Acoustic and Performance Measurements Obtained in the NASA Langley Small Hover Anechoic Chamber

The emergence of small Unmanned Aircraft Systems (UAS) and Advanced Air Mobility (AAM) vehicles has motivated the need to acoustically characterize the rotors found on these vehicle platforms. Hover chamber tests of small rotors are beneficial in assessing the noise impact of UAS, and potentially larger AAM vehicles. Various experiments have been conducted in the NASA Langley Small Hover Anechoic Chamber (SHAC), formerly known as the Small Anechoic Jet Facility (SAJF), in order to investigate the noise and performance of small rotors. One of these tests was that of an “ideally twisted rotor”, which was designed in order to simplify the theoretical rotor inflow for a target thrust condition in hover. This rotor was fabricated using state-of-the-art rapid prototyping and tested in the SHAC. Aerodynamic load and acoustic data were acquired across a range of rotation rates and rotor collective settings. Both tonal and broadband components were identified in the acoustic data, with broadband noise being a prominent contributor to the overall rotor system noise.

Nicole Pettingill↗

NASA NTRS2021

Methods for Recording and Documenting Ambient Environmental Sound for use in Listening Devices

The potential for implementing Advanced Air Mobility (AAM) vehicles as a viable new transportation system into communities will likely be significantly affected by the psychoacoustic impact of these new noise sources into the existing ambient soundscape. This document addresses a need within the research community for a consistent means of documenting recordings of the ambient soundscape via a metadata framework (Rizzi, et al. 2020). Such recordings can provide a cognitive context for AAM vehicle sounds, as well as a fixed condition or independent variable against which experimental manipulations of vehicle sounds can be evaluated in listening tests. This document also provides recommendations for recording and reproduction techniques of ambient sound. A brief review is made of the use of ambient recordings in prior aircraft noise studies, and of psychoacoustic motivations for its implementation is reviewed from the fields of soundscape, auditory scene analysis, and time-varying partial specific loudness research.

urban air mobility↗

NASA NTRSDate not supplied

An Approach for Identifying IASMS Services, Functions, and Capabilities From Data Sources

Assuring safety in the NAS with the inclusion of new entrants that are part of Advanced Air Mobility (AAM) will require overcoming unique safety challenges that result from combining innovative technologies with novel airspace concepts for moving people and cargo using semi-autonomous/autonomous vehicles. Overcoming these AAM safety assurance challenges is the focus of the In-time Aviation Safety Management System (IASMS). The IASMS Concept of Operations (ConOps) describes an interconnected set of services, functions, and capabilities (SFCs)designed to manage operational risks, identify unknown risks, and inform system design to mitigate risk. This paper describes a broad approach for identifying SFCs involving technology trends in research, assessment of known and unknown risks in safety reports, and causal and contributing factors in aviation accidents and incidents. This approach leverages these sources to identify potential SFCs that enable the Monitor, Assess, and Mitigate (M-A-M)functionality that represents the enabling framework of the IASMS.

Kyle Ellis↗

NASA NTRSDate not supplied

NASA’s Simulation Activities for Evaluating UAM Concept of Operations

NASA’s vision for Advanced Air Mobility (AAM) 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 using revolutionary new aircraft types. Urban Air Mobility (UAM), the concept of expanding transportation networks by serving short flights to transport people and goods around metropolitan areas, is part of a larger paradigm shift toward AAM, in which new technologies and business models are enabling transformational applications of aviation. In NASA’s Concept of Operations for UAM, Providers of Services for UAM (PSU) play a key role to enable safe and efficient UAM operations by sharing operational data among UAM operators. PSUs provide a pre-departure strategic conflict management service that establishes operational plans for new flights and coordinates them with all relevant operations in shared airspace. The strategic conflict management aims to minimize the need for tactical separation provision, while considering anticipated traffic demand, vertiport capacity and availability, forecasted weather, and airspace restrictions. NASA is currently preparing a new lab evaluation activity with aviation industry partners, called the X4 simulation, to demonstrate their airspace services and capabilities and test new information exchange requirements for UAM operations. This simulation activity will support NASA’s National Campaign (NC) flight tests which are planned over the next several years to guide the collective community and stakeholders through a series of scenario-based test activities that involve vehicles and airspace management services operating in a live test environment. This presentation will provide an overview of the X4 simulation, including objectives, scenarios, assumptions, system architecture, and test schedule. The X4 simulation will evaluate the interconnectivity and operational intent sharing between PSUs for multiple operators, as well as the performance of strategic conflict management.

Urban Air Mobility, Simulation, Advanced Air Mobil↗

NASA NTRSDate not supplied

Revolutionary Vertical Lift Technology (RVLT) Overview

The Aircraft Working Group enables the Advanced Air Mobility (AAM) ecosystem through vehicle development and production as well as individual vehicle management and operations. Aircraft design and operations management develop AAM-inspired concepts and technologies to help define requirements and standards addressing key challenges such as safety, integration, noise, automation, and scalability. This presentation focuses on RVLT electronic Vertical Takeoff Landing Propulsion overview.

Peggy Cornell↗

NASA NTRSDate not supplied

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya N Das↗

NASA NTRS2022

A 3D Simulation Platform for Decentralized Decision-Making in Advanced Air Mobility

This paper presents a general purpose, plug-and-play simulation platform for the use of future aviation stakeholders, such as urban airspace planners, air vehicle operators, ground operation managers, air traffic controllers and aviation researchers. The presented simulator platform is envisioned to serve as a toolkit to visualize, evaluate, and configure future advanced air mobility (AAM) operations. Highlighting features of this toolkit include a modular architecture that allows multiple smart unmanned aerial systems (UASs) to remotely connect to the simulation server and participate in decentralized decision-making scenario simulations. As an example of the decentralized decision-making scenario, an inter-agent negotiation-based conflict resolution use case is considered in this paper, where the UASs leverage the on-board/on-the-edge artificial intelligence (AI) capability to continually build situational awareness, and use this information to predict future conflicts and resolve them through machine-to-machine negotiation. As such operations are non-existent at scale currently, the presented simulation platform offers a viable and cost-effective alternative for assessing the efficacy of AAM research outcomes and challenges in future shared airspace usage. The simulation platform allows plug-n-play connectivity with AI and non-AI compute modules representing individual UAS’s flight control. Each module can interact with the simulation platform independently to communicate current and desired future states, situational awareness, and conflict resolution utilization costs for inter-agent negotiation. The simulation environment orchestrates realistic operational scenarios with spatiotemporal details, dynamic events, tactical conflict-resolution methods, interfaces for customizing air traffic control parameters, and information exchange uncertainties. In the future, this can serve as a community focused cloud simulation platform, incorporating multi-stakeholder airspace constraints from regulatory, government, city, and local agencies.

Aditya Das↗

NASA NTRSDate not supplied

Acoustic Flight Test of the Joby Aviation Advanced Air Mobility Prototype Vehicle

An extensive acoustic flight test was performed on the Joby Aviation pre-production all-electric vertical takeoff and landing prototype. With the design intent of carrying a pilot and four passengers a maximum range of 150 miles, this vehicle utilizes distributed propulsion and vectored thrust via six tilting propellers. As part of the Advanced Air Mobility (AAM) National Campaign, this is the first full-scale AAM aircraft tested by NASA over representative conditions for all phases of a typical mission profile. A 58- channel distributed microphone array was used to acquire acoustic measurements on more than 100 test points (31 unique conditions). The measurements were postprocessed with synchronously sampled vehicle position and state data to form ground noise contours of departures and approaches at various flight path angles and accelerations. Comparing ground areas of 75 dBA isolines, approaches consistently exhibit higher noise levels rela- tive to departures. Directivity maps were generated for constant airspeed level flyovers. Examples comparing the differences in a semi-thrust borne and full wing-borne mode are given, with full wing-borne mode representing the quietest phase of the flight envelope tested. Measurements for hover in and out of ground effect are given and found to have 2 to 5 dB variation over a single run, and more significant variation between back-to-back runs. Finally, initial analysis of acoustic symmetry about the longitudinal vehicle axis and repeatability over several test days are presented.

Acoustics↗

NASA NTRSDate not supplied

Applications of Advanced Perception and Distributed Sensing Technology towards Autonomous Advanced Air Mobility Applications

Emerging concepts for Advanced Air Mobility (AAM) envisions responsive air transportation capabilities that can safely move people and cargo between places - including local, regional, intraregional, and urban - previously not served or underserved by aviation. Expanding traditional aviation services to these environments, particularly when autonomous operations are involved, face a number of challenges that will require advances beyond the state-of-the-art techniques for airborne sensing and perception, which goes beyond the limits of scalability and applicability of the current air transportation system infrastructure. Additionally, the emerging field of distributed sensing and ‘smart spaces’– where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting and can be exploited by the agents through real-time wireless communication – may provide realistic near-time solutions to limitations imposed by traditional aviation techniques. This paper outlines the needs, challenges, and opportunities for advanced perception and distributed sensing techniques to meet the emerging needs for advanced AAM operations in the national airspace. A general roadmap for research, development, and maturation of perception and distributed sensing (P&DS) technologies is proposed to guide future development through verification, validation, certification into airborne systems. Through this analysis of challenges and the proposed roadmap for technology maturation, we hope to accelerate transition of advanced research techniques from other disciplines into this domain.

Autonomy↗

NASA NTRSDate not supplied

Investigation of Intelligent Resource Management for Aviation Communications

The emergence of new aerial vehicles into the airspace as part of new initiatives, such as Advanced Air Mobility (AAM), will place growing demand for spectrum resources to support airspace operations. The traditional approach of using fixed channel allocations within standard service volumes will not allow for dynamic and efficient distribution of resources based on airspace demand; consequently, a new approach to aviation spectrum management will be required to meet the anticipated needs of airspace users. The National Aeronautics and Space Administration (NASA) is investigating the application of advanced concepts to implement a novel spectrum management approach that allows for the intelligent utilization of aviation spectrum throughout the airspace while maintaining the quality of service prescribed by aeronautical standards. This technical investigation evaluates the dynamic assignment of resources for both air-ground and air-air communication links applicable to both the emerging AAM initiative as well as the existing air traffic management system. The performance of the proposed spectrum management concepts will be evaluated using a custom modeling and simulation capability that is currently under development. The implementation of these approaches is anticipated to facilitate increased spectrum utilization efficiency and enhanced airspace capacity, which will better serve the needs of future applications.

Eric Knoblock↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides localization information and precise mapping of the physical environment without having much prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace, where vehicles and aircraft must operate in complex environments with traditional localization services that may be degraded or unavailable. This paper compares several pre-canned 3D SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM, A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC UAS flight test for simulated AAM approach and landing with SLAM algorithms provides an Alternative Precision Navigation and Timing solution based on distributed landmarks and fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a baseline comparison. The criteria of the performance comparison include robustness, accuracy, re-localization, response to environmental changes, and real-time effectiveness, which are currently qualitative but to be quantitative in the future.

computer vision↗

NASA NTRSDate not supplied

Comparison of Visual and LiDAR SLAM Algorithms using NASA Flight Test Data

Simultaneous Localization and Mapping (SLAM) is a promising technique that provides localization information and precise mapping of the physical environment without having much prior knowledge of the surroundings. SLAM may have a vital role in aeronautics and aerospace, where vehicles and aircraft must operate in complex environments with traditional localization services that may be degraded or unavailable. This paper compares several pre-canned 3D SLAM algorithms based on vision and LiDAR, namely ORB-SLAM, ORB-SLAM2, LOAM, A-LOAM, and F-LOAM on NASA UAS (Unmanned Aircraft System) flight test data. The NASA ARC UAS flight test demonstrates preliminary SLAM algorithm results, which serve as a stepping stone to simulated AAM (Advanced Air Mobility) concepts. Conducting AFRC UAS flight test for simulated AAM approach and landing with SLAM algorithms provides an Alternative Precision Navigation and Timing solution based on distributed landmarks and fiducials in the landing zone. These algorithms use the telemetry data as ground truth for a baseline comparison. The criteria of the performance comparison include robustness, accuracy, re-localization, response to environmental changes, and real-time effectiveness, which are currently qualitative but to be quantitative in the future.

computer vision↗

NASA NTRSDate not supplied

Integration of Automation Systems Flight Test Overview

This short presentation outlines an upcoming flight test to be performed as part of the Advanced Air Mobility (AAM) project. Referred to as the Integration of Automated Systems (IAS) flight test series, the objectives are to evaluate NASA research concepts and technologies for complex operations through integrated automation and candidate operational concepts and scenarios. The primary objective is to test mature AAM technologies in a relevant environment. The two primary systems under test are the Flight Path Management (FPM) and Hazard Perception and Avoidance (HPA) technology. This presentation focuses on the HPA technology developed by the FAA known as the Airborne Collision Avoidance System X for Rotorcraft (ACAS Xr) since the audience consists of committee members currently working on developing the minimum requirements for this system. The second half of the presentation explains the primary objectives and describes the scenarios expected to be tested in flight.

automation↗