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NASA NTRS2019

The NASA Urban Air Mobility Testbed Flight Research Aircraft

The National Aeronautics and Space Administration (NASA) is leading government, industry and academic research effort known as Urban Air Mobility (UAM). The UAM activity goal is to develop the technology needed to make possible an urban air transportation system that makes use of human-crewed and crewless vehicles using automation, artificial intelligence and other technologies to safely and efficiently air transport people and goods within an urban environment. The NASA Langley Research Center (LaRC) created a UAM Flight Research Testbed Aircraft from a Cessna LC40 general aviation aircraft. The testbed has updated digital avionics and research systems needed to conduct flight research. The aircraft has two separate autopilots; a standard Federal Aviation Ad-ministration (FAA) certified system, and a modified research autopilot. The research autopilot has modifications that allow increased authority and enhancements to allow more automation and artificial intelligence controls. A network of three research computers hosts software to research several activities including automated air traffic sense and avoid, ground collision avoidance and obstacle avoidance. These UAM research activities involve automation and artificial intelligence technologies developed at three NASA centers. The NASA Langley, NASA Armstrong, and NASA Ames Research Centers are working together to develop and test these UAM technologies. This paper provides details of the systems, capabilities and research projects of the UAM Testbed Research Aircraft.

Howell, Charles T.↗
NASA NTRS2019

Simulating Fleet Noise for Notional UAM Vehicles and Operations in New York

This paper presents the results of systems-level simulations using Metrosim that were conducted for notional Urban Air Mobility (UAM)-style vehicles analyzed for two different scenarios for New York (NY). UAM is an aviation industry term for passenger or cargo-carrying air transportation services, which are often automated, operating in an urban/city environment. UAM-style vehicles are expected to use vertical takeoff and landing with fixed wing cruise flight. Metrosim is a metroplex-wide route and airport planning tool that can also be used in standalone mode as a simulation tool. The scenarios described and reported in this paper were used to evaluate a fleet noise prediction capability for this tool. The work was a collaborative effort between the National Aeronautics and Space Administration (NASA), Intelligent Automation, Inc (IAI), and the Port Authority of New York and New Jersey (PANYNJ). One scenario was designed to represent an expanded air-taxi operation from existing helipads around Manhattan to the major New York airports. The other case represented a farther term vision case with commuters using personal air vehicles to hub locations just outside New York, with an air-taxi service running frequent connector trips to a few key locations inside Manhattan. For both scenarios, the trajectories created for the entire fleet were passed to the Aircraft Environmental Design Tool (AEDT) to generate Day-Night Level (DNL) noise contours for inspection. Without data for actual UAM vehicles available, surrogate AEDT empirical Noise-Power-Distance (NPD) tables used a similar sized current day helicopter as the Baseline, and a version of that same data linearly scaled as a first guess at possible UAM noise data. Details are provided for each of the two scenario configurations, and the output noise contours are presented for the Baseline and reduced noise DNL cases.

Glaab, Patricia↗
NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗
NASA NTRSDate not supplied

Helicopter Pilot Assessments of the Airborne Collision Avoidance System XR With Automated Maneuvering

The Airborne Collision Avoidance System X (ACAS X) is a next-generation collision avoidance tool developed to support different aircraft types and operations. The rotorcraft variant – referred to as ACAS XR – is designed to accommodate existing helicopter platforms as well as in-development, electric vertical takeoff and landing concepts, which are critical to the emerging concept of operations referred to as Advanced Air Mobility. The fundamental role of ACAS XR is to provide Detect and Avoid (DAA) and/or Collision Avoidance (CA) protection against airborne traffic. DAA alerting and guidance in the context of ACAS XR is caution-level and “suggestive,” and is to be used by the pilot if, and when, they decide to maneuver against an identified threat to DAA “well clear.” The CA alerting, by contrast, is warning-level and “directive,” with the pilot required to comply with the associated guidance to prevent a predicted Near Midair Collision (NMAC). The CA alerts generated by ACAS XR are referred to as Resolution Advisories (RAs) consistent with previous CA systems. Unlike earlier CA systems, ACAS XR issues RAs in the horizontal and vertical dimensions as well as multi-axis RAs (referred to as “Blended” RAs). According to the Minimal Operational Performance Standards of DAA systems for Unmanned Aircraft Systems, maneuvers to comply with RAs may be automated, whereas maneuvers based on DAA alerting assume a manual response. The current study was a human-in-the-loop simulation that presented rotorcraft pilots with ACAS XR alerts and guidance in a fixed-base eVTOL simulator with varying levels of automation. Objective results showed that pilots complied with all RAs within the expected 5-second time window and responded to DAA alerts quicker than in earlier studies with ACAS XU. Pilots often made larger horizontal deviations during Manual RAs, but often favored vertical and blended maneuvers. No NMACs occurred, and losses of well clear were mainly attributed to the obligation of the pilots and system to maneuver only after the CA phase of the encounter had begun. Other losses were due to pilots’ noncompliance or disregard for ACAS XR’s alerting and guidance. Noncompliance with RAs most frequently occurred when pilots determined they were too close to the terrain to continue to follow Descend RAs, performing vertical maneuvers instead of following Horizontal RAs, or rejecting Horizontal RA updates because they felt that enough maneuvering had been performed. Subjectively, pilots found the DAA and RA alerting and guidance intuitive and useful for VFR helicopter operations. Slightly more pilots preferred the Automated RA condition to the Manual RA condition. Lastly, they also felt that ACAS led to occasional unsafe Descend RAs. Caveats and future implications are discussed.

eVTOL↗
NASA NTRSDate not supplied

Multi-Vehicle (m:N) Operations in the NAS - NASA's Research Plans

The Advanced Air Mobility movement is occurring across the world with goals of enabling, affordable, efficient, accessible, and safe air transportation at a much larger scale than today’s operations, largely enabled by electrification and automation. Transformative and disruptive innovations are emerging that will support an ecosystem designed to transport goods and people to locations not traditionally served by air transportation. To realize the full vision of AAM, technology will be needed to allow a few operators to operate many vehicles (m:N). The benefits of m:N operations are described, along with the current state-of-the-art, barriers, need, and NASA’s plans to address some of the barriers, including a Multi-Vehicle (m:N) Working Group with goals of producing a community-developed operational approval roadmap for various domains.

multi-vehicle↗
NASA NTRSDate not supplied

MPATH (Measuring Performance for Autonomy Teaming with Humans) Ground Control Station: Design Approach and Initial Usability Results

Envisioned future Advanced Air Mobility (AAM) operations will require a transition of aircraft command and control from onboard pilots to remote operators. The National Aeronautics and Space Administration (NASA) has developed a research ground control station (GCS) software called MPATH (Measuring Performance for Autonomy Teaming with Humans) to study the human factors of remote operators in a representative AAM environment, where small uncrewed aerial systems (sUAS; simulated or real) act as surrogates for larger AAM aircraft. A primary focus of the research being conducted with the MPATH GCS is scalability (i.e., one human managing multiple vehicles). MPATH has demonstrated to be a useful capability for human factors research and remote operations. Two initial usability studies and a multi-vehicle control assessment were recently conducted, and usability data and operator feedback were collected. Generally, participants rated MPATH high on usability and interface quality, whereas information quality was rated slightly lower. These results were supported by specific feedback. Several generalized GCS design recommendations are proposed based on the results and feedback from these flight activities. Future updates to MPATH will incorporate the proposed recommendations. In practice, these recommendations could be used by any GCS software designer or developer to promote usability and safety.

Advanced Air Mobility↗
NASA NTRS2004

Small Aircraft Transportation System Concept and Technologies

This paper summarizes both the vision and the early public-private collaborative research for the Small Aircraft Transportation System (SATS). The paper outlines an operational definition of SATS, describes how SATS conceptually differs from current air transportation capabilities, introduces four SATS operating capabilities, and explains the relation between the SATS operating capabilities and the potential for expanded air mobility. The SATS technology roadmap encompasses on-demand, widely distributed, point-to-point air mobility, through hired-pilot modes in the nearer-term, and through self-operated user modes in the farther-term. The nearer-term concept is based on aircraft and airspace technologies being developed to make the use of smaller, more widely distributed community reliever and general aviation airports and their runways more useful in more weather conditions, in commercial hired-pilot service modes. The farther-term vision is based on technical concepts that could be developed to simplify or automate many of the operational functions in the aircraft and the airspace for meeting future public transportation needs, in personally operated modes. NASA technology strategies form a roadmap between the nearer-term concept and the farther-term vision. This paper outlines a roadmap for scalable, on-demand, distributed air mobility technologies for vehicle and airspace systems. The audiences for the paper include General Aviation manufacturers, small aircraft transportation service providers, the flight training industry, airport and transportation authorities at the Federal, state and local levels, and organizations involved in planning for future National Airspace System advancements.

Small aircraft transportation system↗
NASA NTRS1983

Surface and allied studies in silicon solar cells

Two main results are presented. The first deals with a simple method that determines the minority-carrier lifetime and the effective surface recombination velocity of the quasi-neutral base of silicon solar cells. The method requires the observation of only a single transient, and is amenable to automation for in-process monitoring in manufacturing. This method, which is called short-circuit current decay, avoids distortion in the observed transient and consequent inacccuracies that arise from the presence of mobile holes and electrons stored in the p/n junction spacecharge region at the initial instant of the transient. The second main result consists in a formulation of the relevant boundary-value problems that resembles that used in linear two-port network theory. This formulation enables comparisons to be made among various contending methods for measuring material parameters of p/n junction devices, and enables the option of putting the description in the time domain of the transient studies in the form of an infinite series, although closed-form solutions are also possible.

Lindholm, F. A.↗
NASA NTRS2013

NASA Tech Briefs, September 2013

Topics include: ISS Ammonia Leak Detection Through X-Ray Fluorescence; A System for Measuring the Sway of the Vehicle Assembly Building; Fast, High-Precision Readout Circuit for Detector Arrays; Victim Simulator for Victim Detection Radar; Hydrometeor Size Distribution Measurements by Imaging the Attenuation of a Laser Spot; Quasi-Linear Circuit; High-Speed, High-Resolution Time-to-Digital Conversion; Li-Ion Battery and Supercapacitor Hybrid Design for Long Extravehicular Activities; Ultrasonic Low-Friction Containment Plate for Thermal and Ultrasonic Stir Weld Processes; High-Powered, Ultrasonically Assisted Thermal Stir Welding; Next-Generation MKIII Lightweight HUT/Hatch Assembly; Centrifugal Sieve for Gravity-Level-Independent Size; Segregation of Granular Materials; Ion Exchange Technology Development in Support of the Urine Processor Assembly; Nickel-Graphite Composite Compliant Interface and/or Hot Shoe Material; UltraSail CubeSat Solar Sail Flight Experiment; Mechanism for Deploying a Long, Thin-Film Antenna From a Rover; Counterflow Regolith Heat Exchanger; Acquisition and Retaining Granular Samples via a Rotating Coring Bit; Very-Low-Cost, Rugged Vacuum System; Medicine Delivery Device With Integrated Sterilization and Detection; FRET-Aptamer Assays for Bone Marker Assessment, C-Telopeptide, Creatinine, and Vitamin D; Multimode Directional Coupler for Utilization of Harmonic Frequencies from TWTAs; Dual-Polarization, Multi-Frequency Antenna Array for use with Hurricane Imaging Radiometer; Complementary Barrier Infrared Detector (CBIRD) Contact Methods; Autonomous Control of Space Nuclear Reactors; High-Power, High-Speed Electro-Optic Pockels Cell Modulator; Covariance Analysis Tool (G-CAT) for Computing Ascent, Descent, and Landing Errors; Enigma Version 12; Micrometeoroid and Orbital Debris (MMOD) Shield Ballistic Limit Analysis Program; Spitzer Telemetry Processing System; Planetary Protection Bioburden Analysis Program; Wing Leading Edge RCC Rapid Response Damage Prediction Tool (IMPACT2); ISSM: Ice Sheet System Model; Automated Loads Analysis System (ATLAS); Integrated Main Propulsion System Performance Reconstruction Process/Models. Phoenix Telemetry Processor; Contact Graph Routing Enhancements Developed in ION for DTN; GFEChutes Lo-Fi; Advanced Strategic and Tactical Relay Request Management for the Mars Relay Operations Service; Software for Generating Troposphere Corrections for InSAR Using GPS and Weather Model Data; Ionospheric Specifications for SAR Interferometry (ISSI); Implementation of a Wavefront-Sensing Algorithm; Sally Ride EarthKAM - Automated Image Geo-Referencing Using Google Earth Web Plug-In; Trade Space Specification Tool (TSST) for Rapid Mission Architecture (Version 1.2); Acoustic Emission Analysis Applet (AEAA) Software; Memory-Efficient Onboard Rock Segmentation; Advanced Multimission Operations System (ATMO); Robot Sequencing and Visualization Program (RSVP); Automating Hyperspectral Data for Rapid Response in Volcanic Emergencies; Raster-Based Approach to Solar Pressure Modeling; Space Images for NASA JPL Android Version; Kinect Engineering with Learning (KEWL); Spacecraft 3D Augmented Reality Mobile App; MPST Software: grl_pef_check; Real-Time Multimission Event Notification System for Mars Relay; SIM_EXPLORE: Software for Directed Exploration of Complex Systems; Mobile Timekeeping Application Built on Reverse-Engineered JPL Infrastructure; Advanced Query and Data Mining Capabilities for MaROS; Jettison Engineering Trajectory Tool; MPST Software: grl_suppdoc; PredGuid+A: Orion Entry Guidance Modified for Aerocapture; Planning Coverage Campaigns for Mission Design and Analysis: CLASP for DESDynl; and Space Place Prime.

Source record↗
NASA NTRS2016

UAV Research at NASA Langley: Towards Safe, Reliable, and Autonomous Operations

Unmanned Aerial Vehicles (UAV) are fundamental components in several aspects of research at NASA Langley, such as flight dynamics, mission-driven airframe design, airspace integration demonstrations, atmospheric science projects, and more. In particular, NASA Langley Research Center (Langley) is using UAVs to develop and demonstrate innovative capabilities that meet the autonomy and robotics challenges that are anticipated in science, space exploration, and aeronautics. These capabilities will enable new NASA missions such as asteroid rendezvous and retrieval (ARRM), Mars exploration, in-situ resource utilization (ISRU), pollution measurements in historically inaccessible areas, and the integration of UAVs into our everyday lives all missions of increasing complexity, distance, pace, and/or accessibility. Building on decades of NASA experience and success in the design, fabrication, and integration of robust and reliable automated systems for space and aeronautics, Langley Autonomy Incubator seeks to bridge the gap between automation and autonomy by enabling safe autonomous operations via onboard sensing and perception systems in both data-rich and data-deprived environments. The Autonomy Incubator is focused on the challenge of mobility and manipulation in dynamic and unstructured environments by integrating technologies such as computer vision, visual odometry, real-time mapping, path planning, object detection and avoidance, object classification, adaptive control, sensor fusion, machine learning, and natural human-machine teaming. These technologies are implemented in an architectural framework developed in-house for easy integration and interoperability of cutting-edge hardware and software.

Davila, Carlos G.↗
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 NTRS2021

Fit2Fly Project

NASA’s Fit2Fly project wants to make future commercial drone fleet operations routine, safe, and boring. How can future operators of commercial drone fleets know without a doubt that their aircraft are fit to fly? It is a tough problem, because every flight will effectively break the chain of custody for their aircraft, and having technicians do detailed inspections after each flight is not viable for large, busy fleets.

airworthiness↗
NASA NTRSDate not supplied

AEGIS: Autonomous Entity Global Intelligence System for Urban Air Mobility

This paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.

Artificial Intelligence↗
NASA NTRSDate not supplied

Distributed Sensing and Computer Vision Methods for Advanced Air Mobility Approach and Landing

Advanced Air Mobility (AAM) aircraft require precision approach and landing systems (PALS) in several types of environments such as urban, suburban, and rural. It is difficult to implement current state-of-the-art methods approved for automated approach and landing for AAM operations. However, existing technology and systems that use vision, IR, radar, and 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. The coplanar algorithm determines pose estimation, which feeds into an Extended Kalman filter that combines IMU with vision to create 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. This paper builds on previous work by incorporating high fidelity simulations with computer graphics rendering to demonstrate a distributed sensor network to track an AAM aircraft during approach and landing to compare with the aircraft's onboard navigation solution.

Evan Kawamura↗
NASA NTRS1990

The mobile servicing system

Viewgraphs on the mobile servicing system (MSS) are presented. Topics covered include: MSS overall configuration; MSS (space segment) hierarchy; functional requirements for special purpose dexterous manipulator (SPDM); SPDM configuration; objective of advanced technology development basic requirements for advanced technology; automation and robotics technologies MSS baseline; and automation and robotics technologies planned for incorporation.

Hunter, David G.↗
NASA NTRSDate not supplied

Geometry Systems for Lattice-Based Reconfigurable Space Structures

We describe analytic methods for the design of the discrete elements of ultralight lattice structures. This modular building block strategy allows for relatively simple element manufacturing as well as relatively simple robotic assembly of low mass-density structures on orbit, with potential for disassembly and reassembly into highly varying and large structures. This method also results in a structure that is easily navigable by relatively small, mobile robots. The geometry of the cell can allow for high packing efficiency to minimize wasted payload volume while maximizing structural performance and constructability. We describe the effect of geometry choices on the mechanical properties and automated robotic constructability of a final system. Geometric properties considered include number of attachments per voxel, number of attachments per coefficient of volume, and effects of vertex, edge, and face connectivity of the unit cell. Mechanical properties considered include strength scaling, modulus scaling, and packing efficiency of the lattice. Automated constructibility metrics include volume allowance for an end-effector, strut clearance angle for an end-effector, and packing efficiency. These metrics were applied to six lattice unit cell geometries: cube, cuboctahedron, octahedron, octet, rhombic dodecahedron, and truncated octahedron. A case study is presented to determine the most suitable lattice system for a specific set of strength and modulus scaling requirements while optimizing for ease of robotic assembly.

digital materials↗
NASA NTRSDate not supplied

A Human-In-The-Loop Simulation for Urban Air Mobility in the Terminal Area

In this paper researchers propose a human-in-the-loop experiment to study human performance when tasked with tactical deconfliction in terminal area air taxi operations. The air taxi operations being considered herein are an advanced air transportation concept called Urban Air Mobility (UAM). The UAM concept aims to support not only air taxi operations, but also package delivery and emergency response among other use cases. The key innovation over current air transportation lies with the introduction of highly automated aircraft and air traffic management systems. Development of the UAM system will include transitional midterm phases where some operational services will be provided by a mixture of automation and human actors. Midterm operations present a unique challenge, since the scope of responsibility of automated systems is largely undefined, suggesting the need for direct human participation with little to inform how much human intervention is necessary. Here it is assumed that traffic management responsibilities require coordination between human actors and automated systems and focus on arrival flows for midterm operations. In the proposed human-in-the-loop simulation, virtual UAM traffic is strategically deconflicted by a Provider of Services for UAM at departure, then tactically managed by a human at the arrival facility. Generated traffic consists of UAM participants flying in UAM exclusive airspace structures, thus isolated from traditional traffic. The human operator is tasked with managing spacing of arrival traffic and executing speed adjustments as deemed necessary. Researchers propose the investigation of three levels of automation assistance: 1) no assistance; 2) spacing violation detection; 3) spacing violation detection and speed adjustment recommendations. Quantitative measures like throughput and delay are used to assess the human's capacity for accommodating airborne delays. Qualitative evaluations such as surveys and open-ended feedback are used to gain insight into human factors. These factors could introduce additional capacity constraints on traffic, independent of physical or technical constraints. Although findings for this study will not be reported as the study has not yet been executed, the authors conclude with potential outcomes informed by previous simulations in the literature and suggestions for the structure and procedures of midterm human-automation air traffic management.

UAM↗
NASA NTRSDate not supplied

Rapid Aero Modeling for Urban Air Mobility Aircraft in Wind-Tunnel Tests

Rapid Aero Modeling (RAM) applied to wind tunnel testing, RAM-T, is an approach to efficiently and automatically obtain aerodynamic models during testing. The approach saves time and resources by responding to the demand for experimental efficiency and model fidelity. Motivation for this demand is more acute when investigating a class of vehicles categorized as Urban Air Mobility (UAM) aircraft where many features from both aircraft and rotorcraft are present. RAM-T provides a feedback loop around the test facility to guide the test toward high-fidelity, statistically rigorous aircraft models. The general RAM approach is applicable to computational or physical experiments. It combines concepts from design of experiment theory and aircraft system identification theory that allow the user the freedom to choose, in advance of the test, a specific level of fidelity in terms of prediction error. RAM only collects data required to meet the user-specified fidelity and fidelity is only limited by the facility and test article capabilities. This paper presents results from tests conducted for development of an automated RAM-T technology. The results highlight some of the unique features of RAM applied to eVTOL configurations.

Aerodynamics↗