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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Airports as Energy Nodes (ÆNodes) Project Report

Aviation is experiencing an influx of electrified aircraft in the advanced air mobility (AAM) space. The goal of Airports as Energy Nodes (ÆNodes) is to evaluate the impact of AAM and other advanced aircraft adoption growth on the electrical system of existing small to medium hub regional airports to better prepare them for the future while also enhancing resiliency and helping surrounding communities. AAM small passenger air service encompasses vertical takeoff and landing (VTOL), general aviation (GA) and flight training, and nine to thirty passenger conventional aircraft for regional air mobility (RAM) applications all of which include electrical, hybrid, or hydrogen powered varieties. Some challenges that ÆNodes is trying to address include how to utilize existing airport infrastructure to meet the energy needs of AAM growth in a way that is robust to aid in the growth of AAM while also leveraging any airport-hosted energy assets for the surrounding community in emergency situations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Simulated Vision-based Approach and Landing System for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require precision approach and landing systems (PALS) in several environments, such as urban, suburban, and rural. It is challenging to implement current state-of-the-art methods approved for automated approach and landing for AAM operations with challenges such as GPS degradation in urban environments and visual navigation aids like the glideslope and localizer being narrow and not allowing alternative incoming landing angles at vertiports. However, existing technology and systems, i.e., the instrument landing system (ILS) with glideslope and localizer indicators that use vision, IR, radar, or GPS methods, provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL and computer vision feature correspondence methods to demonstrate a baseline navigation system while adhering to the Federal Aviation Administration requirements and regulations about heliport design (FAA AC 150/5390-2C), which is one of the closest references for vertiport requirements and regulations. The coplanar pose from orthography and scaling with iterations (COPOSIT) algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines IMU with vision to create a vision-based approach and landing (VAL) sensor fusion navigation solution for GPS-denied environments. The VAL navigation solution provides promising simulation results for AAM PALS with Hough circle detection and feature correspondence, which demonstrate robustness to false positives. This paper incorporates moderately high- fidelity simulations with computer graphics rendering to show a distributed sensor network to track an AAM aircraft during approach and landing to compare with the aircraft’s onboard vision-based navigation solution.

distributed sensing↗
NASA NTRSDate not supplied

VSLAM and Vision-based Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft have many challenges in landing accurately and safely in urban, suburban, and rural environments. Localization in large and open rural environments could utilize GPS, but AAM aircraft in urban environments will encounter GPS degradation. Another challenge involves flight operation time, i.e., flying during the day or at night. There are different guidelines, landmarks, and landing light configurations at runways, heliports, and vertiports for daytime and nighttime applications. Tailoring feature detection methods for AAM approach and landing during the day and night pose different issues and challenges. It is easier to detect edges, lines, and other runway markers during the day than at night. Conversely, it is easier to see landing light configurations and patterns at nighttime than daytime. Consequently, utilizing the same feature detector for daytime and nighttime operations may not be feasible. This paper focuses on a vision-based precision approach and landing (PAL) by comparing ORB SLAM 2, a Vision Simultaneous Localization and Mapping (VSLAM) algorithm, and a novel EKF that combines onboard IMU measurements with coplanar pose from orthography and scaling with iterations (COPOSIT). Conducting unmanned aerial system (UAS) flight tests at NASA Armstrong Flight Research Center (AFRC) with landmarks and fiducials distributed around the landing zone provides a simulated AAM approach and landing data to test vision-based PAL methods to provide Alternative Position, Navigation, and Timing (APNT) solutions for AAM PAL applications. The novel vision-based PAL EKF with IMU and COPOSIT provides accurate state estimation when distributed landmarks and fiducials are in the field of view.

distributed sensing↗
NASA NTRS1995

High-frequency variations in Earth rotation and the planetary momentum budget

The major focus of the subject contract was on helping to resolve one of the more notable discrepancies still existing in the axial momentum budget of the solid Earth-atmosphere system, namely the disappearance of coherence between length-of-day (l.o.d.) and atmospheric angular momentum (AAM) at periods shorter than about a fortnight. Recognizing the importance of identifying the source of the high-frequency momentum budget anomaly, the scientific community organized two special measurement campaigns (SEARCH '92 and CONT '94) to obtain the best possible determinations of l.o.d. and AAM. An additional goal was to analyze newly developed estimates of the torques that transfer momentum between the atmosphere and its underlying surface to determine whether the ocean might be a reservoir of momentum on short time scales. Discrepancies between AAM and l.o.d. at sub-fortnightly periods have been attributed to either measurement errors in these quantities or the need to incorporate oceanic angular momentum into the planetary budget. Results from the SEARCH '92 and CONT '94 campaigns suggest that when special attention is paid to the quality of the measurements, better agreement between l.o.d. and AAM at high frequencies can be obtained. The mechanism most responsible for the high-frequency changes observed in AAM during these campaigns involves a direct coupling to the solid Earth, i.e, the mountain torque, thereby obviating a significant oceanic role.

Rosen, Richard D.↗
NASA NTRS1997

Interannual Fluctuations in Atmospheric Angular Momentum Simulated by the National Centers for Environmental Prediction Medium Range Forecast Model

An earlier study established the existence of globally coherent interannual fluctuations in atmospheric angular momentum (AAM), associated with the El Nino-Southern Oscillation (ENSO) cycle. In this paper, we pursue the origin and the structure of these fluctuations using an ensemble of experiments generated by the National Centers for Environmental Prediction, medium range forecast model version 9. In the control experiments, where the observed sea surface temperatures (SSTs) were used as the lower boundary conditions, the model captures the characteristic V-like structure in time-latitude plots of zonally averaged AAM, while experiments with climatological SSTs and those with either perpetual warm or cold ENSO conditions superimposed on the climatological SSTs failed to reproduce this structure. The numerical results indicate that these AAM structures are related to SST variations associated with transitions between different phases of the ENSO cycle and have both propagating and standing components. The largest zonal wind contribution from the levels studied (850, 500, and 200 hPa) is at 200 hPa, where the tropical convective outflow is the strongest. Composites of zonal wind and geopotential height show a clear relationship between the stages of the global AAM oscillation and the ENSO cycle. The strong similarity between the simulated and observed AAM series attests to the model's ability to realistically simulate the interannual response of the atmosphere to ENSO SST anomalies.

Mo, Kingtse C.↗
NASA NTRSDate not supplied

Informing New Concepts for UAS and Autonomous System Safety Management using Disaster Management and First Responder Scenarios

As emerging flight operations become more prevalent and increasingly automated and distributed, the capabilities for managing safety of vehicles and operations will also need to evolve. To address this challenge, the National Academies has envisioned an In-Time Aviation Safety Management System (IASMS) capability for a wide range of aviation operations including current commercial operations as well as new entrants envisioned with advanced air mobility (AAM). The suite of IASMS services, functions, and capabilities (SFCs) would be implemented in a federated approach and would address trends as well as individual operations. Through predictive modeling and data analysis, IASMS is envisioned to identify arising risks so that they can be mitigated, in-time, before a safety incident occurs. IASMS and its requisite set of SFCs must leverage a wide range of information to perform. To better understand these new needs, FSF worked with the aviation and humanitarian communities to develop and validate scenarios that include traditional aviation operations and UAS operations intermingled as they are deployed for disaster management and first responder (DMFR) situations. The three scenarios developed include: • Post Natural disaster response, such as a hurricane, involving multiple parties utilizing traditional aviation and UAS to support rescue operations, surveil damage, and locate survivors needing assistance. • Wildfire fighting in remote locations with traditional aircraft for transport and fire-retardant delivery combined with UAS for surveillance of fire locations as well as to track individual firefighter locations. • Medical Operations and AAM in Urban Environments including passenger-carrying helicopters and AAM vehicles, medical missions (such as transport of radio-pharmaceuticals), and other UAS delivery operations (such as the delivery of defibrillators). Each scenario was developed and validated by representatives with expertise in humanitarian operations, urban and rural emergency response, air traffic management, UAS operations, and traditional flight operations. The scenario definitions address roles and responsibilities of individual actors, the appropriate utilization of UAS, and the actions taken by those actors to appropriately manage risks associated with the mission and environment. The risks to aviation traffic and to people on the ground explored included potential risks arising from incompatibilities in calculating reference altitudes (eg, differing uses of AGL, MSL, barometric, or GPS-derived values), loss of command and control (C2) communications, rapid changes in weather and winds, and physical interference. For each risk, IASMS SFCs were postulated in the context of monitoring services, risk assessment capabilities, and identifying appropriate mitigation strategies. The identified SFC capabilities were envisioned from known services postulated for IASMS and for UTM. For these unique environments, IASMS SFCs are needed to address conditions such as hazardous payloads, micro-climates and urban canyons, and the need to keep uninvolved air traffic out of the area where DMFR operations are being conducted. The second phase of analysis focused on inferring the specific information needs and the SFCs for IASMS, utilizing a structure of 16 information classes to organize requirements. For each of the risks identified in the workshops, it was postulated what data sources would be necessary to monitor critical aspects of the risk (eg, surrounding air traffic, ground population, terrain, etc). to be directly measured as well as data that would be derived, which implies additional SFCs for different actors to understand what information would likely be exchanged between parties. For an IASMS to be effective, additional research is needed to develop the advanced algorithms that can address the increasingly autonomous and complex operations in differing environments and to develop means of identifying unknown risks. Looking at these scenarios highlighted a number of research issues. These include the ability to quickly "cordon off" airspace thru temporary flight restrictions (TFRs) or other means, developing clear definitions to enable automation-based algorithms for prioritizing operations, defining airspace density metrics, standardization of altitude reporting, and establishing a basis for safety data metrics definition and collection. This paper seeks to outline the development of an IASMS in the context of the DMFR scenarios and resulting demonstrations. Utilizing this contextual approach, NASA will generate recommendations for an assured safety framework for AAM operations that enables AAM operations to safely access the NAS.

In Time Aviation Safety Management System↗
NASA NTRSDate not supplied

Community Integration of Advanced Air Mobility

This presentation was given on November 8, 2021 at a pre-Showcase Webinar prior to the November 10, 2021 Ohio AAM Showcase. Topics covered in the presentation include NASA's AAM Partnership Strategy including the AAM partnerships, NASA's AAM National Campaign Timeline, and how NASA is working with Community Partners to integrated AAM into their communities.

Nancy Mendonca↗
NASA NTRS2022

Vision-Based Precision Approach and Landing for Advanced Air Mobility

Advanced Air Mobility (AAM) aircraft require perception systems for precision approach and landing systems (PALS) in urban, suburban, rural, and regional environments. The current state-of-the-art methods approved for automated approach and landing will be difficult to utilize in support of AAM operational concepts. However, there are technology and systems from other applications and lower-TRL research that use vision, IR, radar, and GPS methods to provide baseline perception and sensing requirements for AAM aircraft approach and landing. This paper focuses on vision-based PAL to demonstrate a closed-loop baseline controller while adhering to the Federal Aviation Administration requirements and regulations. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter. Combining IMU with vision creates a sensor fusion navigation solution for GPS-denied environments. The state estimate leads to glideslope and localizer error computations, which will be pertinent for designing and deriving guidance laws and control laws for AAM PALS. The IMU and vision navigation solution provides promising simulation results for AAM PALS, and higher fidelity simulations will include computer graphics rendering and feature correspondence.

Evan Kawamura↗
NASA NTRSDate not supplied

NASA’s MBSE Approach for Advanced Air Mobility

NASA’s Aeronautics Research Mission Directorate (ARMD) has a significant research effort for advanced air mobility (AAM). AAM involves bringing aviation closer to where people live and work and offers the possibility of making aviation a normal part of everyday life. The scope of AAM is broad, including transformations in aircraft, airspace operations, and in how these new aviation capabilities will integrate into local communities. Due to the nature of AAM cutting across multiple aspects of the aircraft, airspace, and community domains, NASA is applying tools and techniques aligned with a model-based systems engineering approach. These are intended to capture and organize applicable system architectures and potential requirements in order to deliver a validated system architecture and recommended set of requirements that enable sustainable and scalable medium density AAM operations.

Advanced Air Mobility↗
NASA NTRSDate not supplied

National Association of State Aviation Officials (NASAO)

AAM Mission presentation given by the ARMD Associate Administrator, Robert Pearce, to the National Association of State Aviation Officials (NASAO) on March 2, 2022. The presentation includes an overview of the ARMD missions, AAM use cases, NASA's role to address AAM challenges and AAM mission priorities, National Campaign status, and the community partnerships for AAM.

Advanced Air Mobility↗
NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

NASA is conducting investigations into Advanced Air Mobility (AAM) concepts, aircraft, and operations. One of the most challenging scenarios for AAM will be enabling safe routine access into densely populated urban centers. To address challenges in the urban environment, a moderately high-fidelity simulation capability is needed to investigate AAM flight operations over a regional area for the development and evaluation of autonomy technologies. This paper describes a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (10's-100's of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to accurately simulate all phases of flight. Glass cockpit displays have been developed for monitoring aircraft operation over a detailed simulated urban environment. Simulation models have been integrated to simulate air and ground-based sensors, such as radar and LIDAR. The commercial X-Plane software package is used as a rendering engine to mimic vision-based sensors (such as onboard and ground-based cameras) at various times of day and in various weather conditions over a relatively detailed graphical model of the city. The paper presents a detailed illustration of the simulation and software architecture used for traffic over this urban region. This system is enabling the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward aircraft flight test evaluation.

Keerthana Kannan↗
NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Structurally-Adaptive↗
NASA NTRSDate not supplied

Enhancements, Verification, and VMS Integration of VTOL Concept Vehicle Simulation Models

Advanced Rotorcraft Technology (ART) and the NASA Ames Aeromechanics branch have jointly developed FLIGHTLAB simulation models for Advanced Air Mobility (AAM) VTOL concept vehicles. The overarching purpose of the simulation model development is to establish a set of well defined reference vehicles for FLIGHTLAB users and the rotorcraft community. The ongoing research effort and enhancement of these AAM simulation models to fulfill the role of quality reference vehicles is this paper’s focus. The content of this paper expands on the established characteristics of these AAM models in three primary areas. First, enhancement of the lift+cruise and tiltwing models with elastic airframe properties is discussed. The process of setting up the elastic airframe model in FLIGHTLAB, as well as the impacts on flight characteristics are explained. The introduction of the elastic airframe modeling allows these models to be used in flight dynamics, loads, and vibration analysis of the configuration designs. Next, linear model generation from the enhanced simulation model is covered. Confirming the validity of the linearized models is of importance, as these linear models are utilized for flight control design and tuning for these experimental configurations. For the final focus, the progress towards implementation of these models into the NASA Ames Vertical Motion Simulator (VMS) is described. This task seeks to demonstrate the procedures of integrating a FLIGHTLAB flight simulation in the VMS environment, test fully integrated simulation with communication between flight dynamics, control, and propulsion models, and explore the essential aspects of simulation model integration in a full flight simulator environment. This includes I/O definition, initialization, trim, flying, etc. By expanding the capabilities of the AAM simulation models, they continue to develop as valuable and approachable modeling references.

VMS↗
DOE OSTI2026

Model-based, in-situ, non-destructive qualification and certification of parts made by autonomous additive manufacturing

To address the significant productivity challenges associated with the qualification and certification (Q&C) tasks of additively manufactured (AM) parts, which have traditionally relied on rigorous post‐build inspection and testing, we propose an integrated framework that combines model‐based qualification and certification (MBQ&C) with autonomous additive manufacturing (AAM). MBQ&C employs high‐fidelity predictive models, developed within the Integrated Computational Materials Engineering (ICME) paradigm, to simulate process–structure–property–performance relationships for assessing a part’s fitness for use. Since predictive models are commonly machine learning (ML)-based or reduced-order surrogates of validated physics models, they run efficiently, enabling timely inference. In parallel, the self-driving AAM utilises ML-based adaptive, closed‐loop control strategies to avoid, mitigate, or repair defects and anomalies during fabrication, thereby increasing the likelihood of producing acceptable parts. A key feature of the combined AAM-MBQ&C framework is that predictive models explicitly incorporate defects or anomalies that persist after the build, using instance-specific data captured via in-situ sensing. This customisation enables a build‐specific assessment of fitness for use, rather than relying on nominal or generic parameters. Such individualised evaluation provides a robust basis for Q&C-related acceptance decisions relating to each build. Additionally, the rapid solution capabilities of ML or reduced-order models enable the determination of a part’s suitability for service shortly after build completion. As the framework matures, it has the potential to substantially reduce reliance on conventional point‐design approaches—such as time‐consuming post‐build computed tomography scanning and costly destructive testing. Thus, the AAM-MBQ&C framework represents a transformative, scalable strategy for quality assurance of AM components, as parts produced within a stable, validated, and certified envelope can be certified with reduced testing. Key benefits include: (1) significant gains in Q&C productivity through efficient, model-centric assessment; (2) performance-based classification of defects into critical and non-critical categories; (3) the ability to predict potential deviations in the performance of parts affected by real-time, adaptive process control interventions relative to those produced under a certified process, and (4) the enabling of virtual Q&C for service environments that are difficult, hazardous, or impractical to access or reproduce experimentally. Collectively, these capabilities strengthen the business case for AM, particularly for high‐consequence and mission‐critical applications. Finally, although this work focuses on powder-based AM, the proposed techniques could be extended to AM processes employing alternative feedstock forms.

Gunasegaram, Dayalan↗
NASA NTRS1986

Computations of atmospheric angular momentum, with emphasis on the merit period

Three-dimensional wind and pressure fields used to calculate effective angular momentum functions (EAMFs) for earth rotation/polar motion are produced by a complex 3-step system at the U.S. National Meteorological Center. The resulting series of functions have errors which can be estimated by comparisons with similar series produced by the European Center for Medium Range Weather Forecasts. For the axial wind component, related to length of day (lod) changes, the small differences between the two series of atmospheric angular momentum (AAM) are confined to the tropics and the higher latitudes of the Southern Hemisphere. In addition, differences between the seasonal components of the AAM series and those of lod are found to be significantly reduced by including the entire stratosphere's momentum. Comparisons of the EAMFs for 8 calendar years indicate substantial interannual variability exists in the annual amplitudes of these forcing terms. In particular, the axial AAM showed quite different behavior from normal in 1983 and 1984, the years of the MERIT campaign. These two years showed very high and low annual amplitudes for AAM, respectively, which can be attributed to the effect of the tropical Pacific sea surface warming (El Nino) and its aftermath.

Salstein, David A.↗
NASA NTRS1993

Excitation of earth's polar motion by atmospheric angular momentum variations, 1980-1990

We compute the polar-motion excitation function due to the atmospheric angular momentum (AAM) for both IB (inverted-barometer) and non-IB cases, as well as the excitation function from geodetically observed Earth orientation data for the period 1980-1990. The two are then compared in studying the AAM contribution to the polar motion excitation. The polar drifts with periods longer than about two years have similar characteristics, but the comparison is inconclusive because of data uncertainties. For the seasonal wobble excitation, the agreement is poor except for the prograde annual wobble, indicating the influence of other geophysical excitations than AAM. For the Chandler wobble excitation, a correlation coefficient of 0.53 for non-IB and 0.58 for IB are found for 1986-1990. Together with a coherence spectral analysis, they clearly demonstrate a strong contribution of AAM to the Chandler wobble excitation.

Chao, Benjamin F.↗
NASA NTRS1994

The extratropical 40-day oscillation in the UCLA general circulation model. Part 1: Atmospheric angular momentum

Variations in atmospheric angular momentum (AAM) are examined in a three-year simulation of the large-scale atmosphere with perpetual January forcing. The simulation is performed with a version of the University of California at Los Angeles (UCLA) general circulation model that contains no tropical Madden-Julian Oscillation (MJO). In addition, the results of three shorter experiments with no topography are analyzed. The three-year standard topography run contains no significant intraseasonal AAM periodicity in the tropics, consistent with the lack of the MJO, but produces a robust, 42-day AAM oscillation in the Northern Hemisphere (NH) extratropics. The model tropics undergoes a barotropic, zonally symmetric oscillation, driven by an exchange of mass with the NH extratropics. No intraseasonal periodicity is found in the average tropical latent heating field, indicating that the model oscillation is dynamically rather than thermodynamically driven. The no-mountain runs fail to produce an intraseasonal AAM oscillation, consistent with a topographic origin for the NH extratropical oscillation in the standard model. The spatial patterns of the oscillation in the 500-mb height field, and the relationship of the extratropical oscillation to intraseasonal variations in the tropics, will be discussed in Part 2 of this study.

Marcus, S. L.↗
NASA NTRS1994

The short-term prediction of universal time and length of day using atmospheric angular momentum

The ability to predict short-term variations in the Earth's rotation has gained importance in recent years owing to more precise spacecraft tracking requirements. Universal time (UT1), that component of the Earth's orientation corresponding to the rotation angle, can be measured by number of high-precision space geodetic techniques. A Kalman filter developed at the Jet Propulsion Laboratory (JPL) optimally combines these different data sets and generates a smoothed times series and a set of predictions for UT1, as well as for additional Earth orientation components. These UT1 predictions utilize an empirically derived random walk stochastic model for the length of the day (LOD) and require frequent and up-to-date measurements of either UT1 or LOD to keep errors from quickly accumulating. Recent studies have shown that LOD variations are correlated with changes in the Earth's axial atmospheric angular momentum (AAM) over timescales of several years down to as little as 8 days. AAM estimates and forecasts out to 10 days are routinely available from meteorological analysis centers; these data can supplement geodetic measurements to improve the short-term prediction of LOD and have therefore been incorporated as independent data types in the JPL Kalman filter. We find that AAM and, to a lesser extent, AAM forecast data are extremely helpful in generating accurate near-real-time estimates of UT1 and LOD and in improving short-term predictions of these quantities out to about 10 days.

Freedman, A. P.↗