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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Human Factors Research Considerations for Terminal Area Urban Air Mobility Operations

In this presentation, we discuss the human factors research challenges from introducing greater levels of automation in a future air transportation concept called Urban Air Mobility (UAM). UAM is an air transportation concept that aims to provide air transportation services to the daily commuter, as well as emergency response and package delivery. The principal innovation over current day large air transport system is the greater distribution of important safety functions to automated and human agents; these functions include air traffic management, traditionally an air traffic controller responsibility. A central aspect of UAM is the development of an automated air traffic manager, whose primary responsibility is to approve airspace access for vehicle operators. Vehicle operator roles may include onboard and remote pilots, as well as a human manager who will supervise an entire fleet. Alternatively, both fleet manager and vehicle operators can be merged into a single role – a feasible option if UAM aircraft are autonomous. In lieu of tower controllers, vertiport managers, with the assistance of automation, will manage arrival and departure schedules between vertiports, as well as supervise surface operations. Our approach here will be to introduce use cases currently being developed by NASA, and then provide preliminary definitions for each of the roles introduced above and how coordination between them can be configured to support the operations within the use cases described. Subsequently, we review the tools and interfaces being developed to support the various roles. To conclude, we present current human factors work related to defining the roles above and suggest future work to advance the UAM concept.

trial planning↗
NASA NTRSDate not supplied

Missed Approach Procedures in Advanced Air Mobility: Conceptual Exploration

The High Density Vertiplex Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been in collaboration with a team from Wisk Aero focusing on vertiport operations, procedures, and concept development. A particular area of focus has been on the development of missed approach scenarios and procedures that highlight the potential changes in the nearer- and further-term operational time frames. Such changes relate to topic areas such as airspace design, automation and autonomy, roles and responsibilities of actors and stakeholders, airspace management services and systems, as well as technologies specific to vertiport operations management. This presentation encompasses the content of the associated paper that presents the current state of joint concept development through the established collaboration and the application of elements in ongoing testing as part of NASA’s High Density Vertiplex Sub-Project’s research strategy.

vertiport↗
NASA NTRSDate not supplied

The Viability of See-and-Avoid for Midair Collision Avoidance for Urban Air Mobility (UAM)

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRS2023

Advanced Air Mobility Missions for Public Good

Advanced Air Mobility (AAM) brings together novel technologies to produce innovative capabilities that have the potential to enhance the current aviation market and transportation network. However, other AAM missions may provide direct benefits to the public while also supporting the advancement of the commercial AAM market. This assessment explores AAM missions for public good, defining what public good means in the context of AAM and detailing use cases, metrics, and requirements to determine similarities to the broader AAM industry.

advanced air mobility↗
NASA NTRSDate not supplied

NASA and Advanced Air Mobility (AAM) National Campaign (NC) Tech Talk: Integrated Data Product

NASA Advanced Air Mobility (AAM) National Campaign has hosted multiple flight tests in a effort to promote public confidence and accelerate the realization of emerging aviation markets for passenger and cargo transportation in urban, suburban, rural, and regional environments. The goals and objectives for each flight test are intended to assess the performance of the vehicle’s capacity to operate safely, efficiently, and sustainably in urban areas. The Airspace Test and Infrastructure (ATI) team processes the flight data to evaluate the effectiveness of air vehicle and airspace management integration, while its analysis capabilities support the assessment of the efficacy of proposed vehicles and ground systems required to enable the vehicles’ operations. The ATI data services team creates many products from the data, first by transforming and merging the data into a single Integrated Data Product (IDP). This aligns data from many sources by time, so that, for example, the winds that impacted a flight can be aligned with its ground track and speed. A detailed IDP enables many types of statistical computation and visualizations that help researchers understand what transpired during the flight and how it can be explained. This Tech Talk will give an overview of the IDP process, including examples of the resulting products created by the data services team. It will also share an application that will allow participants to perform their own data analysis.

National Campaign, Advanced Air Mobility, Integrat↗
NASA NTRS2023

The Viability of See-and-Avoid for Urban Air Mobility Operations

Urban Air Mobility (UAM) is an emerging aviation concept that could supplement today’s ground and air transportation systems. For UAM, it is generally assumed that the private sector will manage separation and not rely on the Federal Aviation Administration air traffic control system. To date, discussions of initial operations focus on using the visual abilities of the pilot to “see and avoid” (SAA) other aircraft. Decades of research on SAA has demonstrated that it is inadequate for reliable detection of aircraft that might pose a collision risk. The literature on multi-object tracking is also reviewed for findings on how well humans can visually track objects. The research shows that humans have limited resources for tracking and that this may be affected by object characteristics and cognitive skills. The conclusion is that SAA is a risky method for avoiding midair collisions. It is recommended that flight deck displays and automated collision avoidance systems be implemented for all UAM aircraft at the outset of their introduction.

urban air mobility↗
NASA NTRS2023

Ch. 12. A Theoretical Approach to Management of Limited Attentional Resources to Support the m:N Operation in Advanced Air Mobility Ecosystem

Advanced air mobility (AAM) technologies incorporate increasingly autonomous systems that allow fully remote, independent, and intelligent operation of air vehicles to support the transportation of goods and passengers within and across urban and rural areas. With a myriad of automated technologies enabling the AAM ecosystem, the human operator’s role will likely be a passive supervisory monitor of the air vehicles, involving increasingly fewer humans (m) that manage many more autonomous systems (N), or m:N operations. Unfortunately, the human performance literature suggests that human operators will exhibit poor supervision of numerous autonomous agents due to the limits of attentional resources in the operators. In the general human information-processing model, a human operator exercises a limited pool of attentional resources to engage various information-processing stages including detecting, perceiving, comprehending, and predicting objects around them. Yamani and Horrey (2018) expanded the human information-processing model to characterize a tradeoff between information-processing demand and resource relief that automation brings in the context of automated driving. In their model, a driver interacting with an automated driving system is assumed to reallocate resources “freed” by automation to support other information-processing stages required for successful task performance. A future AAM ecosystem enabled by an orchestration of advanced automated systems, however, requires a single operator to interact with more than one air vehicle with varying levels and degrees of automated systems, making the traditional framework of human-automation interaction insufficient. To address this gap, we provide a review of the literature on situation assessment and trust, two constructs identified as critical for a fuller understanding of intimate and intricate interactions between a human operator and multiple air vehicles equipped with increasingly autonomous systems. Then, we propose an expansion of Yamani and Horrey’s (2018) model to motivate systematic research on the human operator’s role, identify factors that influence resource allocation and guide human-centered design of an interface supporting the m:N operation in the AAM environment.

Advanced Air Mobility↗
NASA NTRSDate not supplied

High-Fidelity Simulations of Lift+Cruise VTOL Urban Air Mobility Concept Aircraft in Hover

This paper presents progress of high-fidelity multidisciplinary simulations for the NASA lift+cruise vertical takeoff and landing (VTOL) urban air mobility concept aircraft in hover. The study focuses on the unsteady flow solutions featuring strong wake interactions between rotors and the airframe. The simulations couple a high-fidelity aerodynamic model with a comprehensive rotorcraft aeromechanics tool with rotor rotation-speed trim. The aerodynamic model is based on the Reynolds-averaged Navier-Stokes equations using the one-equation Spalart-Allmaras turbulence model with rotation correction. Unsteady aerodynamic flows are computed on a dynamic, deformable, unstructured, overset grid system. An integrated overset-grid assembler is used to construct the composite grid from 23 component grids and facilitate communications between individual component grids. Rotor performance and airframe forces and moments are computed and compared on two unstructured grids. The study demonstrates capabilities of high-fidelity multidisciplinary analysis tools to capture the strong unsteady flowfield around this multi-rotor aircraft in hover.

CFD↗
NASA NTRSDate not supplied

High-Fidelity Simulations of Lift+Cruise VTOL Urban Air Mobility Concept Aircraft in Hover

This presentation presents progress of high-fidelity multidisciplinary simulations for the NASA lift+cruise vertical takeoff and landing (VTOL) urban air mobility concept aircraft in hover. The study focuses on the unsteady flow solutions featuring strong wake interactions between rotors and the airframe. The simulations couple a high-fidelity aerodynamic model with a comprehensive rotorcraft aeromechanics tool with rotor rotation-speed trim. The aerodynamic model is based on the Reynolds-averaged Navier-Stokes equations using the one-equation Spalart-Allmaras turbulence model with rotation correction. Unsteady aerodynamic flows are computed on a dynamic, deformable, unstructured, overset grid system. An integrated overset-grid assembler is used to construct the composite grid from 23 component grids and facilitate communications between individual component grids. Rotor performance and airframe forces and moments are computed and compared on two unstructured grids. The study demonstrates capabilities of high-fidelity multidisciplinary analysis tools to capture the strong unsteady flowfield around this multi-rotor aircraft in hover.

CFD↗
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

Utilizing Advanced Air Mobility Rotorcraft Tools for Wildfire Applications

Over the past decade, due in large part to heavy investment in the field of Advanced Air Mobility (AAM), significant progress in rotorcraft-focused modeling tools has been made. Such progress has notably increased AAM rotorcraft modeling capabilities in the topics of conceptual design, preliminary design, and more recently flight dynamics. Yet, due to recent and persistent increases in extreme weather events, an emerging interest has been raised in utilizing such modeling capabilities for aiding in emergency relief efforts and other public good missions. This paper uses wildfire fighting as a representative public good mission and demonstrates the relevance of the NASA Revolutionary Vertical Lift Technology (RVLT) rotorcraft toolchain to such missions. An emphasis is placed on flight dynamics modeling and control because of the hazards and challenges associated with the atmospheric environment of wildfires. In this work, the NASA FlightCODE tool was used to analyze both a UH-60 and the NASA six-passenger quadcopter reference model hovering in an experimentally informed wildfire turbulent environment. Preliminary results of this study estimate actuator usage exceedances and disturbance rejection capabilities of the vehicles’ translational rate command systems. Leveraging the RVLT toolchain, refinement and expansion of this work could lead to handling qualities envelope estimation and design optimization for wildfire turbulent environments. This would provide pilots with additional information to make real-time decisions in high-risk scenarios and begins preparations for simulating these dangerous environments for pilot training and experimentation.

Rotorcraft↗
NASA NTRS2024

Toward a Psychoacoustic Annoyance Model for Urban Air Mobility Vehicle Noise

A psychoacoustic test was performed to obtain annoyance responses to noise from a quadrotor Urban Air Mobility (UAM) vehicle to aid in the development of a model of annoyance to UAM vehicle noise. Previous analysis of that test concluded that a psychoacoustic annoyance (PA) model, including the effects of loudness, sharpness, fluctuation strength and roughness, correlated well with the collected annoyance responses. This motivated the assessment of other PA models available in the literature, as well as the development of a new PA model based on the previously collected annoyance responses to UAM noise. To build the PA model for UAM noise, annoyance ratings to individual sounds and annoyance comparisons between pairs of sounds are first combined into a latent annoyance scale that has a correlation coefficient of 0.98 with the raw responses and that is based on just-noticeable-differences (JNDs) in annoyance. Examples for two sounds that differ in annoyance by 1 JND show when there is 75% agreement about which sound is more annoying. This latent annoyance (JND) scale also gives insight into the interplay of various perceptual components, such as overall loudness, temporal effects and spectral effects. The proposed PA model for UAM noise is fit to the latent annoyance scale, includes the sound quality effects mentioned above, as well as a term for tonality, and offers a step forward in the prediction of annoyance to UAM vehicle noise.

Urban Air Mobility↗
NASA NTRSDate not supplied

On the Modeling of Urban Air Mobility Vehicle Takeoff and Landing Operations in the FAA Aviation Environmental Design Tool

Urban air mobility (UAM) vehicles are anticipated to operate in close proximity to the public. A possible barrier to the introduction of UAM vehicles as a transit solution is their community noise impact, particularly around vertiports. The Federal Aviation Administration Aviation Environmental Design Tool (AEDT) is the mandated tool to assess aircraft noise and other environmental impacts due to federal actions at civilian airports, vertiports, or in U.S. airspace for commercial flight operations. However, AEDT was designed to model fixed-wing aircraft and conventional helicopter operations, not UAM vehicles. Prior work by the authors showed significant differences in noise contours of UAM departures and approaches when modeling operations as fixed-wing and helicopter types in AEDT. This paper identifies the sources of those differences. Additionally, a NASA time-marching simulation tool is used to generate noise exposure predictions for equivalent operations to help identify differences associated with the noise model implemented in AEDT and offer possible changes to assess UAM community noise impact more consistently with simulation-based modeling.

aeroacoustics↗
NASA NTRSDate not supplied

Annoyance Model Assessments of Urban Air Mobility Vehicle Operations

As urban air mobility (UAM) vehicles begin to enter service, consideration must be given to vertiport siting and flight routing to help reduce community noise impact and promote adoption by the community. Notwithstanding environmental justice concerns, a possible early strategy is to operate out of existing heliports and fly along established helicopter routes, many of which follow uninhabited waterways and/or roadways (with the thought for the latter that an already noisy ambient environment will mask the sound of the aircraft). However, no prior annoyance model has been fielded that both takes audibility into account and that may be applied to such a real-world strategy. This paper reviews a recently developed annoyance model that includes audibility as a factor and applies it to two flight operations. A simple overflight case is first undertaken to demonstrate the approach. A point-to-point operation in the New York City area is then considered to demonstrate how annoyance varies across an urban soundscape. The cases considered use modeled UAM vehicle noise propagated to a set of ground observers as the signals and either recorded or modeled ambient acoustic data as the maskers.

aircraft community noise↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Helmet and Extravehicular Visor Assembly (EVVA) Chamber B Thermal Vacuum Testing Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test methodology, hardware setup, and results from the xEMU helmet and extravehicular visor assembly (EVVA). Two helmets/EVVAs were tested simultaneously in Chamber B, with different thermal environments and EVVA configurations. For the helmet/EVVA on the Short xEMU (SxEMU) test article, five thermal profiles were tested during five simulated EVAs, with four different visor and shade configurations. For the helmet/EVVA on the second xEMU, eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing, with a single visor and shade configuration. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and through two separate heater cages surrounding each respective test article. The thermal effects of the Exploration Informatics (xINFO) lights and camera on the helmet/EVVA was also tested. Twenty-two temperature sensors were used to collect data in critical locations in the xEMU helmet/EVVA assembly. This paper will document the testing results and compare the test data against the xEMU helmet/EVVA and system-level thermal models for model validation.

xEMU↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Lunar Boot Chamber B Thermal Vacuum Testing Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test methodology, hardware setup, and results from the xEMU lunar boots. Eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing. This paper will address only the cold environment testing results. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and through a heater cage surrounding the boots. Notably, the xEMU boots also contacted the liquid-nitrogen chilled floor inside of Chamber B, which provided a conduction pathway to simulate the thermal effects of the lunar surface. Test hardware was developed to extend the water tubing from the Liquid Cooling Ventilation Garment (LCVG) into the boots to set the internal thermal boundary nominally provided by the astronaut’s foot. Thirty-three temperature sensors were used to collect data in critical locations in the xEMU boot assembly as well as for calorimetry to determine heat flux to and from the boots. This paper will document the testing results and provide a high-level interpretation of the testing results. To conclude, this paper will address possible forward work and knowledge gaps present in lunar boot thermal performance and testing.

xEMU↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Chamber B Thermal Vacuum “Suit 2” Pressure Garment System Hardware and Test Design

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation space suit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test hardware design and test methodology for the “Suit 2” Pressure Garment System (PGS) test article. The uncrewed “Suit 2” PGS test article consisted of a full PGS assembly with simulated Portable Life Support System (PLSS) functionality provided by test equipment, including a ventilation loop and two distinct thermal control loops. This paper will discuss in depth the test hardware design, including internal suit thermal boundary simulation, sensor quantity and placement, test support equipment rigs for gas flow, water flow, and power. Custom hardware designed to add additional penetrations to the suit or provide additional interfaces for sensors will also be discussed. This paper will also address the assembly and integration sequence for the test article. In addition to test hardware design, this paper will provide insights into the test methodology for this test article, including a discussion of thermal steady-state testing, simultaneous dual suit thermal vacuum testing, and hazard mitigation and controls.

xEMU↗
NASA NTRS2024

Exploration Extravehicular Mobility Unit (xEMU) Chamber B Thermal Vacuum “Suit 2” Pressure Garment System Test Article Results

NASA’s Exploration Extravehicular Mobility Unit (xEMU) is the government reference next-generation spacesuit design and is engineered to protect astronauts from extreme lunar environmental temperatures. To evaluate the xEMU hardware thermal requirements, the xEMU Testing Team invented, designed, and executed a dual-suit, uncrewed thermal vacuum (TVAC) test at Johnson Space Center’s (JSC) Chamber B. This paper details the test results from the “Suit 2” Pressure Garment System (PGS) test article. The primary objective of the “Suit 2” PGS test article was to evaluate system-level suit heat leak and Environmental Protection Garment (EPG) thermal performance. Eleven unique thermal profiles were tested including both cold and hot environmental cases over the course of five continuous days of testing. The radiative thermal environment was controlled through exposure to liquid-nitrogen shrouds on the chamber walls and a heater cage surrounding the test article. This paper will principally focus on system-level thermal results from the “Suit 2” PGS test article. This paper will examine data collected from one-hundred and seventy thermocouples located in critical locations inside and outside of the suit, as well as seven resistance thermometers (RTDs) for calorimetry to determine total heat flux in and out of the suit. The test data will be compared against the system-level PGS thermal models for model validation. To conclude, this paper will address knowledge gaps presented by unmanned xPGS thermal vacuum testing and the current state of lunar xPGS thermal modeling and testing.

xEMU↗