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The aviation industry is evolving at an unprecedented pace, necessitating the development of efficient, secure, and interoperable systems to manage increasingly complex air traffic. Moreover, the demand for High-Altitude Operations (HAO) is increasing. Furthermore, air traffic control services are limited in HAO environments. HAO industry participants will need airspace access and flexibility to perform their missions in this airspace that provides provisions for scalability. The Discovery and Synchronization Service (DSS) will be a cornerstone of the HAO ecosystem, enabling the effective sharing of critical airspace data, including operational intent, aircraft trajectories, and airspace usage among various stakeholders and operators. The DSS architecture addresses these challenges with a distributed, decentralized, and interoperable system that facilitates seamless integration across diverse airspaces. It prioritizes secure data exchange while safeguarding data ownership. This white paper presents the vision, architecture, and benefits of the DSS for HAO, underscoring its potential to streamline operations, reduce redundancies, and establish a foundation for safe and efficient airspace management.

The National Aeronautics and Space Administration is supporting research to develop a prototype remote vehicle operations center at Langley Research Center to explore current and future advanced air mobility operations using small unmanned aerial systems vehicles as surrogates for future, larger-scale passenger carrying vehicles. The prototype facility known as the Remote Operations for Autonomous Missions (ROAM) Unmanned Aerial Systems (UAS) Operations Center is being used to explore different roles and responsibilities of remote operators managing multiple autonomous vehicles, with the goal of exploring human-autonomy teaming concepts that enable m:N operations (i.e., m operators managing N vehicles). ROAM has developed into a world-class research, development, and technology (RD&T) environment that can support both the collection of human factors data and the command and control of remote vehicles in beyond visual line of sight conditions. ROAM provides a key capability to enable full end-to-end hardware- and human-in-the-loop simulation testing, connecting with simulated small-UAS and creating a seamless Live-Virtual-Constructive (LVC) environment. This report describes the development of the ROAM UAS Operations Center from concept through design, culminating in the current implementation at NASA’s Langley Research Center.

Experimental results are obtained for a roamx-0201 type airfoil and the clf5605 airfoil at highsubsonic, low Reynolds number conditions using the Tohoku University Mars Wind Tunnel, Japan. The tests are conducted at a Mach number of M = 0.60, and a Reynolds number of Re = 20,000 to reflect representative aerodynamics of a rotor blade for Mars exploration. The angle of attack is varied between α = −2.0 deg and α = 6.0 deg. The roamx-0201 type airfoil is an unconventional airfoil optimized for the chosen tunnel operating conditions using the Evolutionary aLgorithm for Iterative Studies of Aeromechanics (ELISA), developed under the Rotor Optimization for the Advancement of Mars eXploration (ROAMX) project. ELISA is utilized here to optimize aerodynamic airfoil performance using a Genetic Algorithm and two-dimensional high-fidelity CFD simulations, ultimately resulting in a Pareto-optimal airfoil set. The clf5605 airfoil is the outboard airfoil used on the Ingenuity Mars Helicopter and provides a baseline against which the roamx-0201, as well as possible future airfoil profiles for the compressible low Reynolds number regime, can be compared against. Lift and drag data are recorded using a balance, pressure distributions are obtained using Pressure Sensitive Paint (PSP) application, and Schlieren images are obtained to visualize the flowfield. The data is tabulated to aid future research.

NASA is funding designers of small spacecraft at The Aerospace Corporation in El Segundo, California, to develop a technology demonstration of an evolutionary alternative to the CubeSat standard that maintains the benefits of that platform while overcoming key limitations. DiskSat is a plate-shaped satellite 40 inches in diameter and an inch thick that could offer more power and surface area for instruments, providing more opportunities for NASA to expand upon target mission objectives for small spacecraft.

This manual is intended to provide the information necessary for an applications engineer to configure, compile, and execute the GlennICE software. The intent of this document is not to detail the algorithms of the software. The intent of this document is to detail how a user can interact and configure the algorithms of this software in order to produce a desired simulation.

Acoustic data obtained using a small turbofan engine are further analyzed with the aim to ultimately enable improved prediction methods and mitigation techniques for turbofan core/combustor noise. The relative impact of this propulsion-noise source for civilian-transport aircraft on airport community noise is expected to significantly increase in the future. Simultaneous high-data-rate acoustic measurements acquired using a circumferential infinite-tube-probe array at the core-nozzle exit in conjunction with a far-field microphone array are processed. The test matrix contains engine operational points from idle to engine-maximum setting, with measurements repeated for different circumferential array configurations, as well as for redundancy. The combustor-noise contribution to the far-field noise signature is obtained using an advanced source-separation method that correlates far-field microphone measurements with a modal decomposition of the unsteady pressure field at the core-nozzle exit. The advantages of the present approach compared to the classical two-signal source-separation method are discussed.

Emissions from the aviation sector are expected to grow significantly in the next few decades due to growing demand for aviation. The U.S. fleet size has increased significantly in the past 20 years, growing from 2,132 aircraft to 5,485 aircraft. Similarly from 1990 to 2019, aviation emissions more than doubled, growing faster than any other transportation mode. These emissions include carbon dioxide (CO2), as well as non-CO2 sources including nitrogen oxides (NOx), sulfur dioxide (SO2), water (H2O), and particulate matter (soot). However, this report focuses specifically on CO2 emissions because the impacts from CO2 are well-understood and long-lived. There are also many climate goals tied to CO2 emissions, including the US Aviation Climate Action Plan which is pushing for net-zero in 2050. CO2 is a greenhouse, or heat-trapping, gas that comes from the extraction and burning of fossil fuels, from wildfires, and natural processes like volcanoes. Greenhouse gases absorb heat radiating from the Earth’s surface and release it in all directions, including back toward Earth’s surface. By adding more CO2 to the atmosphere, humans amplify the natural greenhouse effect, causing global temperature to rise. Since the onset of industrial times in 1750, human activities have raised atmospheric CO2 by 50%. Considering this challenge, the United States has set a climate goal to achieve net-zero CO2 emissions in the U.S. aviation sector by 2050 to reduce the environmental impact of aviation. Net-zero emissions refers to the amount of greenhouse gases (GHGs), such as CO2, methane or SO2, that are removed from the atmosphere being equal to those emitted by human activity. This contrasts with carbon-neutrality, which refers to no net release of carbon dioxide to the atmosphere, especially through offsetting emissions, that could be accomplished through a series of actions. As of 2022, the goal of net-zero has been adopted by much of the aviation industry including multiple airlines and countries. To reach net-zero by 2050, sustainable aviation fuels (SAFs) are identified as the technology that could provide the greatest impact within this timeframe. However, certain challenges may limit the production and/or adoption of SAFs in the marketplace, such as competition for feedstocks, production capacity, and high cost. In this case, SAFs would be insufficient to meet the net-zero CO2 emission goals. Therefore, in addition to SAFs, other strategies will be needed to close the gap on emissions produced as a result of SAF shortfalls or to enhance the impact of SAFs on reducing emissions. NASA’s Aeronautics Research Mission Directorate (ARMD) aims to examine these additional pathways to explore how its research may best contribute to the realization of net-zero emissions by 2050. Specifically, this effort considers strategies that can reduce emissions in the aviation sector in combination with the use of SAFs and carbon offsets while still meeting projected passenger demand. The strategies considered are not only feasible but also could be operationally scalable by 2050. The goal of this report is to investigate additional strategies such as alternative flight and ground operations, providing insight into the CO2 emission reduction potential and feasibility of strategy implementation by 2050.

From March 26th to 28th, 2024 the m:N UAS working group and its subgroups (Evaluation Methodologies, Exceptions/Interventions, and Initial Operating Capability for Airspace Integration) met at SAIC in Washington, D.C. for an in-person meeting. The subgroups meet virtually throughout the year, and twice a year participants from all the subgroups come together to further identify and discuss challenges and paths forward for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (DroneUp) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This includes identifying requirements, use cases, metrics, and the development of white papers to support organizations including the FAA, RTCA, and ASTM. A change from last year, the Large UAS and HAPS sub working groups have disbanded while the sUAS working group continues independently, currently working on a white paper titled Personnel Selection, Roles, and Training for sUAS. For 2024 the m:N sub working groups have been refocused to cover evaluation methodologies, interventions/exceptions, and initial operating capability for airspace integration; with the premise that the outcomes from these subgroups will be white papers. These white papers can inform one another to ultimately become a master whitepaper. Each subgroup lead is called out below: Evaluation Methodologies Subgroup Jay Shively, Adaptive Aerospace Interventions/Exceptions Subgroup Andy Thurling, DroneUp (Lead) Initial Operating Capability for Airspace Integration Subgroup Andy Lacher, NASA (Lead)

In order to refine an initial Concept of Operations, explore Concepts of Use, and expose/validate requirements for future In-Time Aviation Safety Management Systems (IASMS), testing architectures were created, along with a set of capabilities and underlying information exchange protocols. These systems were conceived and developed based on hazards associated with two envisioned urban area flight domains: (1) highly autonomous small uncrewed aerial systems (sUAS) operating at low altitudes, and (2) highly autonomous air taxis. The initial scope of this development is described in [1]; this report provides an update, focusing on the subsequent developments and test activities. As stated in [1], it is important to note that there are many capabilities already in use by the industry (or soon to be in use) that will play critical roles in future IASMS designs. Those reported here were developed to address a gap in the current state-of-the-art regarding specific hazards/risks, and/or to allow for investigation of the interplay between and across hazard types — particularly regarding how overall safety risk can be reduced or managed effectively. Results of testing and development activities are organized by the operational phase wherein a particular capability would be employed (i.e., preflight, in-flight, and post-flight/off-line). Pre-flight: A set of capabilities were developed to help mitigate safety risk prior to flight (e.g., during flight and mission planning). Results of testing summarize (1) validation activities to raise the Technology Readiness Level (TRL) and (2) evaluation activities where the capabilities were applied to flight/mission planning procedures and used by operators/pilots. For the latter, flight plans were automatically assessed, and operators/pilots were notified of hazardous flight segments so as to enable adjustment of the flight plan and re-evaluation, and/or to better inform go/no-go decisions. Capabilities addressed hazards associated with power consumption, third-party risk, wind, navigation system performance, radiofrequency interference, and proximity to geo-spatial threats (e.g., buildings, trees, and no-fly zones). In-flight: Flight experiments tested capabilities that detect and respond to hazards encountered during flight. In the first series, safety hazards were monitored and assessed onboard, and system-generated mitigation maneuvers were recorded (but not acted upon by the vehicle). In the second series, mitigation maneuver commands directed the aircraft in response to safety hazards (i.e., auto-mitigation). The sUAS used for testing is described in full, as is the test architecture, which included commercial avionics, research avionics, and onboard software designed to detect, assess, and respond to hazards. The onboard system was designed as a run-time assurance framework, consistent with [2] and supportive of both supervisory and automated modes. The primary functions included: real-time risk assessment (RTRA), auto-pilot monitoring, constraint monitoring, and contingency select/triggering. RTRA performs integrated risk assessment considering data from several hazard-related monitors (e.g., battery, motors, navigation, communications, population density, and loss-of-control). Post-flight/off-line: Data monitored and recorded during flights can enable IASMS capabilities that execute after flights have completed (or “off-line”). These include: (1) the ability to identify anomalies and trends that may only be observable when comparing data spanning a number of similar flights; (2) the ability to update and validate pre-flight and in-flight capabilities and any underlying models to improve their performance; (3) the ability to report anomalies/off-nominals that may indicate design changes or maintenance actions are needed; and (4) the ability for humans involved in operations to report safety-relevant observations to help in understanding the flight data and/or the operational context of a flight. Progress on three such capabilities is summarized; the first investigates anomaly detection given a limited set of flight logs and applies an approach previously used for space operations. The second explores what could be identified using a larger set of flight logs, including from web-based forums where flight logs are posted by sUAS autopilot users. The third creates a new means of collecting information on UAS incidents and accidents via the Aviation Safety Reporting System (ASRS).

From November 28th to 30th, 2023 the m:N UAS working group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM)] met at the NASA Langley Research Center in Hampton, VA for an in person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year however, participants from all the subgroups come together in person to further identify and discuss challenges, and path forward ideas for incorporating UAS into the airspace. The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the FAA, RTCA, and ASTM. Each subgroup is run by a government/industry team (see below). sUAS Subgroup Garrett Sadler (NASA) Scott Scheff (HF Designworks) Large UAS Subgroup Conrad Rory (NASA) Brandon Suarez (Reliable Robotics) HAPS Subgroup Andy Thurling (Thurling Aero Consulting) Jeff Homola (NASA) UAM Subgroup Mike Politowicz (NASA) Scott Scheff (HF Designworks), member-at-large

The NASA Mars Helicopter Ingenuity performed its maiden flight on April 19, 2021, and opened new possibilities for planetary exploration. Ingenuity has ended its mission after experiencing an anomaly during its 72nd flight on January 18, 2024. Due to the low density and low temperature of the surface atmosphere on Mars, significant challenges must be overcome to achieve aerodynamic performance. The low density of the Martian atmosphere and the relatively small-scale rotor result in flows with very low Reynolds number, reducing the lifting force and lifting efficiency, respectively. Until recently, compressible flow at Reynolds numbers around Re = 10,000 was of moderate practical interest but with the success of Ingenuity, the aerodynamic regime is appreciating considerable attention. The purpose of this report is to document the geometry (planform, twist, and airfoils) of the Mars Helicopter rotor, for use in future research on rotor performance in the Mars atmosphere.

Gas turbine engines are designed with sufficient margin to prevent stall under normal operating conditions throughout their life. This compromise ensures that during rapid accelerations, compressor operation remains stable, but at the cost of efficiency and thrust responsiveness. The design margin encompasses multiple sources of uncertainty and systematic deviances from the operating line, the largest of which is the transient allowance. This set-aside accounts for the temporary incoordination of the engine spools during an acceleration while still enabling it to meet the certification requirement to accelerate from low to high power within a specified time, and without experiencing overtemperature, surge, stall, or other detrimental factors. Electrification of the powertrain provides the opportunity to address this reserve and truly optimize the design. The addition of electric machines inherent in hybrid propulsion concepts offers a means to interact with the engine shafts such that the necessary margin can be reduced, which can positively impact the engine design. By adjusting the amount of power extracted from or injected to the engine spools by the electric machines during transient operation, excursions from the operating line can be minimized. Past work using a dynamic engine model has shown that optimization of the fuel flow schedule during acceleration can reduce the required margin while still meeting the time requirement, and results are further improved when combined with power injection and extraction. The current work uses machine learning through a genetic algorithm to address the problem holistically by concurrently optimizing the electric machine power command and fuel flow acceleration schedule using an updated, higher fidelity version of the original engine model.

The X-57 Maxwell is an all-electric airplane with a distributed electric propulsion system used for a high-lift system at takeoff and landing conditions. The Kestrel and USM3D flow solvers were used at NASA Langley to investigate the performance of the X-57 Maxwell in the development of an aerodynamic database. The configuration investigated in this paper had a 30° flap deflection, and in addition the pilot’s right aileron were deflected and undeflected in different cases. The solutions were computed at an airspeed of 58 KEAS, for an altitude of 6000 feet, and a flight Reynolds number of 0.588e+06 per foot. To evaluate the high-lift distributed electric propulsion system without aileron deflections, the solutions were computed for an angle-of-attack sweep from −2° to 20°, with the high-lift propellers blowing and the cruise propellers excluded from the simulation. To investigate the aileron effectiveness, the solutions were computed at angles of attack of −2° and 14°, for aileron deflections from −25° to 18° with the high-lift propellers blowing and the cruise propellers windmilling at idle-power. The high-lift propellers and the idle-power cruise propellers were modeled with an actuator disk. Results show negligible differences in lift, drag and pitching moment whether the idle-powered cruise propellers were included or excluded from the simulation. In general, the Kestrel and USM3D codes compared well for lift, drag and pitching moment for the landing configuration with no aileron control. The Kestrel code, using the Spalart-Allmaras turbulence model with the rotation corrections terms, predicted an increasing lift coefficient up to maximum lift coefficient of 4.65 at a 15° angle of attack. The USM3D code, using the Spalart-Allmaras turbulence model turbulence model with the Quadratic Constitutive Relation predicted an increasing lift coefficient up to maximum lift coefficient of 4.5 at a 12° angle of attack, with the lift remaining constant through a 14◦ angle of attack. A possible difference in lift coefficient between the codes for high angles of attack may result from the different available options used with the standard Spalart-Allmaras turbulence model. The Kestrel code predicted more drag across the range of angle of attack than the USM3D code. The codes compared well for pitching moment coefficient across the range of angle of attack. The Kestrel and USM3D codes compared well for aileron effectiveness at a 2° angle of attack. The Kestrel code predicts better aileron effectiveness than USM3D at a 14° angle of attack. There is more flow separation in the region outboard of the last high-lift nacelle for the USM3D solutions at a 14° angle of attack, which diminishes the ability of the aileron to be effective.

Electrified aircraft powertrains contain multiple interacting subsystems, making them much more complex than traditional aircraft propulsion systems in terms of integration and control. Electrification enables aircraft to have distributed thrust-producing fans that the flight control system can leverage for enhanced maneuverability, further increasing the control complexity. A NASA concept aircraft, the SUbsonic Single Aft eNgine (SUSAN) Electrofan, is such a vehicle. SUSAN is a series/parallel partial hybrid-electric single-aisle transport aircraft that takes advantage of its electrified powertrain to provide fuel burn and emissions benefits when compared to the state-of-the-art. Achieving these benefits requires an appropriately designed control architecture that coordinates the various powertrain and flight control subsystems. As such, the SUSAN aircraft is designed with a high level of automation, allowing it to properly manage coupled subsystems and react rapidly to failures and anomalies. To do this effectively, algorithms that perform component health management, fault detection, isolation, and accommodation, and continuous optimization, must be developed and implemented. This paper describes the development of some of these algorithms for system health management applied to the powertrain of the SUSAN concept aircraft.

The advent of hybrid-electric aircraft concepts with distributed propulsion has resulted in relatively complicated powertrain designs. Determining the optimal approach to display information about the hybrid system in the flightdeck has taken on great importance. At NASA Glenn Research Center, the concept SUbsonic Single Aft eNgine (SUSAN) hybrid airliner has been modeled inside a flight simulator where different flight deck displays can be evaluated. This work focused on ways to display information that reduce clutter and information overload without degrading the crew’s ability to operate the aircraft and respond properly to emergencies. The displays’ layout and symbologies were created to be intuitive to the pilots. This paper documents iterations of synoptic displays and discusses the rationale and tradeoffs behind each variation.

Hybrid electric aircraft propulsion is an emerging technology that presents a variety of potential benefits along with technical integration challenges. Developing these new propulsion architectures with their complex control systems, and ultimately proving their benefit, is a multistep process. This process includes concept development and analysis, dynamic simulation, hardware-in-the-loop testing, full-scale testing, and so on. This effort is being revolutionized and indeed enabled by new digital tools that support increasing the technology readiness level throughout the maturation process. As part of this Digital Transformation, NASA has developed a suite of publicly available digital tools that facilitate the path from concept to implementation. This paper describes the NASA-developed tools and puts them in the context of control system development for hybrid electric aircraft propulsion. The three MATLAB®-based software packages are the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS), the Electrical Modeling and Thermal Analysis Toolbox (EMTAT), and the Thermal Systems Analysis Toolbox (TSAT). These tools are interactive, complementary, and compatible with each other. T-MATS is a modular thermodynamic modeling framework designed for creating custom component level models of jet engines. EMTAT is a modeling framework used to simulate a variety of power electronic devices, using both physics-based and power flow calculations. TSAT is a framework for modeling and analysis of dynamic heat transfer. These packages all consist of graphical, drag-and-drop, parameterizable building blocks representing various components of the system to be modeled, e.g., compressors, turbines, motors, energy storage devices, etc. They are designed to enable the user to model and simulate the end-to-end dynamic operation of a hybrid electric gas turbine engine powertrain at the timescale of the turbomachinery, capturing mechanical, electrical, and thermal interactions. This paper demonstrates through multiple examples how these tools have been used successfully in a variety of applications, including several of the early stages of hybrid electric gas turbine engine propulsion system development, from the initial system modeling to real-time interactive pilot-in-the-loop simulation to physical hardware-in-the-loop testing, each step bringing the technology closer to fruition.

This document serves as an annual report of m:N Unmanned Aircraft Systems (UAS) subgroup activities, addressed challenges, and roadmaps for the future. The subgroups consist of small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Platform Systems (HAPS), and Urban Air Mobility (UAM). Also included in this report are participant lists for each subgroup (Appendix B) and future roadmap and outreach plans.The subgroups meet in a virtual format multiple times throughout the year. Twice a year, participants from all the subgroups come together as part of the m:N working group to brief each other in person on progress, challenges, and path forward ideas for successful incorporation of UAS into the airspace. Recent m:N in person meetings include: - November 29-30, 2022 at the NASA Ames Research Center in Mountain View, CA - May 9 & 11, 2023 at AUVSI’s Xponential conference in Denver, CO - The next in person working group meeting is planned for November 28-30, 2023 at NASA Langley in Hampton, Virginia The m:N UAS working group is run by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups including the Federal Aviation Administration (FAA) and Radio Technical Commission for Aeronautics (RTCA’s) SC-228 Detect and Avoid committee.

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

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.

This work outlines the development of a new natural gas/oxygen (NG/O2) fueled combustion rig located at the NASA Glenn Research Center for high-temperature environmental durability studies of advanced materials and components at atmospheric pressure. The NG/O2 burner rig can simulate the high-temperature, high-heat flux, and high-velocity thermal environments encountered in gas turbine engines. It also provides the capability to study environmental effects such as water vapor and other foreign contaminants relevant to these applications. The rig is anticipated to bridge the gap between other laboratory methods such as furnaces, jet-fueled burner rigs, high-heat flux lasers, and more expensive engine rig testing. The NG/O2 rig is expected to have maximum sample temperature capabilities over 3,000°F (1,649°C) and result in higher water vapor content compared to our Mach 0.3 to Mach 1.0 jet-A burner rigs which is important for characterizing current and next-generation environmental barrier coatings. This paper will provide an overview of the development of the NG/O2 burner rig, initial characterization, and current research and development efforts on environmental barrier coated ceramic matrix composites.

NASA is supporting the development of Electrified Aircraft Propulsion (EAP) technology due to its potential to reduce aircraft fuel burn, emissions, and noise as well as improving safety and performance. One focus of this research is the electrification of conventional turbomachinery propulsion systems, which offers ways to improve the performance and operability of turbine-engine powered aircraft through the addition of electro-mechanical systems. These hybrid-electric turbine engines provide additional actuation and energy management control opportunities for improving stability and transient response behavior. This paper summarizes the results of a Hardware-in-the-Loop (HIL) test performed at the NASA Electric Aircraft Testbed (NEAT) during the summer of 2022. The test demonstrates the feasibility and performance of an advanced energy management control strategy by integrating a simulated turbofan engine with scaled electro-mechanical hardware. A full-scale real-time reference model of a geared turbofan was run alongside a scaled electro-mechanical system representing the electrified turbofan components operating at a megawatt-scale power level. The model was interfaced with the hardware through a novel closed-loop control and scaling algorithm that emulated the dynamic speed and torque response of the turbofan shafts. The control strategy was implemented on the electrical machines connected to the emulated turbomachinery shafts. The results from the testbed are compared against simulations that predict the testbed and geared turbofan model operation. The energy management control strategy successfully changed the operating point of the engine model and improved its stability during throttle transients. These results also demonstrate the success of the novel closed loop control and scaling approach for emulating turbomachinery and elevate the Technology Readiness Level (TRL) of the energy management control strategy.

A metal spacecraft cabin ventilation fan suitable for aerodynamic and acoustic ground tests was designed and two copies of the fan assembly were fabricated. Both fans were tested for aerodynamic performance and acoustic levels in the NASA Glenn Research Center Acoustical Testing Laboratory. A new test rig for small axial flow fans was designed to accommodate the instrumentation and back-pressure adjustments. Measurements included were: static pressures for measuring performance, a 72-channel in-duct microphone array, external microphone measurements for acoustics, and inter-stage hot wire measurements of the fan wake. The present report documents the test setup. A series of follow on reports documents the results. These fan units, the data and the test facility described herein are valuable resources available for supporting NASA’s aeronautics research and space exploration missions.

Presenting a 40-minute Throughout the history of aviation, there has been a continual desire to develop aircraft that can go higher, faster, and farther. Within each of these broad goals, significant achievements have been made in aeronautics over the decades. Specialized military aircraft can reach the edges of the atmosphere and regularly fly at supersonic speeds, with the Concorde bringing transatlantic supersonic flight to the public. In addition, commercial subsonic aircraft regularly fly nonstop flights of over 7000 nautical miles with flight times lasting over 15 hours. Each of these achievements have been enabled by research and development on the technologies required to overcome these barriers. As major technical challenges for high-speed/hypersonic flight have been overcome, the next questions that arise relate to what precisely is a viable commercial high-speed/hypersonic vehicle and what is the market for it. In 2020, NASA funded two studies to provide an independent assessment of the economic viability of high-speed/hypersonic transportation and some high level guidance on the design requirements of such a vehicle. Results from these market studies will be presented as well as insight into how these results are being used to inform ongoing strategic planning efforts in this research area within NASA’s Hypersonic Technology project.

Hermes LunarG is a flight-proven research platform that can measure the effects of lunar gravity on different types of astromaterials using Blue Origin's reusable sub-orbital flight system. Hermes LunarG is part of the Strata-1 family of experiments that leverages International Space Station (ISS) flight hardware to investigate particle dynamics. The platform is a single payload locker consisting of four clear, polycarbonate tubes containing different regolith simulant materials. Onboard the New Shephard, once lunar gravity conditions are achieved, the payload releases the simulants to move freely in the tubes. The payload will capture images and sensor data to illustrate the dust settling effects and behavior of lunar regolith simulant in this environment. Funded by NASA STMD (Space Technology Mission Directorate) GCD (Game Changing Development) Program, the project has been developed by NASA, T STAR (Texas Space Technology Applications and Research) and Texas A&M's SOAR (Sub-Orbital Astromaterial Research) team to deliver a platform that will further address gaps in dust mitigation and improve the understanding of regolith in lunar gravity.

The removal of atmospheric CO2 from a spacecraft is of particular importance to NASA’s mission, and is an area of continual study and technological advancement. One of the more recent advancements has been with reusable sorbents being regenerated with a combination of heat and vacuum. One such technology is the Thermal Amine Scrubber (TAS) flight experiment on the ISS, though several others are currently flying or preparing to fly. A model was created of the TAS to predict chemical performance, using fundamental chemistry and physics based on principles rather than empirical relations. Since the physical laws are true across all conditions, such a model enables greater model accuracy outside the bounds of test data, and allows for virtual testing of the hardware at conditions that are prohibitively difficult or expensive to actually test. This paper details the model’s development, operation, and correlation to data from the flight unit. The model is then compared to a data set taken from the flight unit under different flow, CO2 partial pressure, and bed configuration conditions, resulting in only a 2% error. The equations and principles laid forth in this paper are applicable to a wide range of thermally regenerated sorbents, and additional models of a similar nature would allow for potentially the most straightforward and direct method of comparison of technologies available to date.

On May 9th and 11th, 2023, the NASA-led MultiVehicle (m:N) Working Group and its subgroups [small Unmanned Aircraft Systems (sUAS), Large UAS, High Altitude Pseudo Satellite (HAPS), and Urban Air Mobility (UAM)] met in Denver, CO at XPONENTIAL 2023, co-hosted by the Association for Uncrewed Vehicle Systems International (AUVSI) and Messe Düsseldorf North America (MDNA), for an in-person meeting. The subgroups meet multiple times throughout the year, virtually. Twice a year, however, participants from all the subgroups come together to brief each other on progress, challenges, and path forward ideas for incorporating UAS into the airspace. The m:N working group is co-chaired by Jay Shively (Adaptive Aerospace) and Andy Thurling (Thurling Aero Consulting) and is comprised of members from government, industry, and academia in an effort to identify and reduce barriers to m:N operations. This effort also includes identifying requirements, use cases, and metrics to support organizations and groups, including the FAA and RTCA’s SC-228 Detect and Avoid.

The Single-aisle Turboelectric AiRCraft with Aft Boundary Layer propulsor (STARC ABL) is a concept aircraft with a partial turboelectric powertrain. The complexity and integrated nature of the partial turboelectric powertrain architecture presents failure modes and hazards not found in conventional aircraft propulsion designs. Previously, various electrical and mechanical faults and associated recovery modes were demonstrated in a dynamic model of the powertrain. It was shown that certain faults were catastrophic without recovery logic, due specifically to the interaction of the subsystems. However, in each case, the logic, known as a reversionary control mode, enabled continued operation with assumed sufficient thrust to maintain safe flight. The current work evaluates the powertrain faults and recovery strategies using a full aircraft model in a piloted flight simulator, and places it in the context of current regulatory practice. Faults initiated in flight were successfully mitigated, with the accommodated aircraft subsequently evaluated against certification requirements for three-engine aircraft, which were shown to be appropriate for the STARC-ABL configuration.

Wildfires in the United States have been increasing significantly in both frequency and size in recent decades, requiring maximal efficiency on the part of wildfire management organizations. However, while the methodology and technology to combat wildfires has improved, theconnectivityinfrastructure in more ruralregionsof the country is either incompatible with newertechnology or altogether non-existent. Due to this, many modern communication methods are rendered uselessin areas where connection is needed most, complicating the overall fire management procedure. To improve communication and logistics between responders on the front lines and headquarters, a team of NASA researchassociateshas developed the schematic for a rapidly deployable mobile ad-hoc internet-enabled Wi-Fi network that can bring stable internet accessto any desired area.

To realize the potential of Urban Air Mobility (UAM), an assurance of cybersecurity is critical for public acceptance. UAM is a concept that proposes to develop short-range, point-to-point transportation systems in metropolitan areas using vertical takeoff and landing (VTOL) aircraft to overcome increasing surface congestion. The growth in the development of UAM systems, and the associated data exchange and service interactions will be at risk due to numerous types of cybersecurity attacks. The intent of this work is to leverage a permissioned blockchain to simulate secure data exchange and storage for the UAM operational intent use case. In this case study, two vehicle operators are operating in the same airspace. Their intent is to fly vehicles that land at a shared vertiport, securely.

The solid particle erosion (SPE) behavior of a state-of-the-art environmental barrier coating (EBC) was assessed at 2,192 °F (1,200 °C) using alumina (Al2O3) particulate. The effect of particle velocity, particle size, and impingement angle were investigated. Results show that the erosion damage in the EBC is primarily controlled by particle kinetic energy. In addition, the effect of impingement angle demonstrates the contribution of the tangential component of velocity.

Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.

Interest in characterization of the aerodynamic drag of acoustic liners has increased in the past several years. This report details experiments in the NASA Langley Grazing Flow Impedance Tube to quantify the relative drag of several perforate-over-honeycomb liner configurations at flow speeds of M=0.3 and 0.5. Various perforate geometries and orientations are investigated to determine their resistance factors using a static pressure drop approach. Comparison of these resistance factors gives a relative measurement of liner drag. For these same flow conditions, acoustic measurements are performed simultaneously with the drag measurements for tonal excitation from 400 to 3000 Hz at source sound pressure levels of 140 and 150 dB. Educed impedance and attenuation spectra are used to determine the impact of variations in perforate geometry on acoustic performance. The goal is to identify a perforate that will reduce the drag penalty associated with conventional round-hole perforates by 60%. One perforate design, based on a slot geometry, is shown to reduce this penalty by 50%. Further reductions may be possible but require a reduction in measurement uncertainty to allow a statistically rigorous evaluation.

The ability to accurately identify and isolate failures in the electrical power system (EPS) is critical to ensure the reliability of spacecraft. This paper proposes a novel solution to the problem of fault detection and diagnosis in direct current (DC) electric power systems for spacecraft. Autonomous operation becomes essential during deep space missions that lack the ability to monitor and control the spacecraft from ground locations. The current state of EPS fault supervision is insufficient to guarantee highly reliable operation. To solve this issue, a combination of model-based and knowledge-based techniques are used in a hierarchical framework to improve the diagnostic performance of the system. Noise, disturbances, and modeling errors are considered in the design of the fault detection system. Practical considerations related to spacecraft flight hardware and software are accounted for in the system design for flight applications. To assess the functionality of the design, a wide array of failures are simulated in a series of experiments. The experiments showed that the technique improved the capability of the autonomous system by increasing the number of fault types diagnosed. The significance of this study is to provide a framework capable of advanced diagnostics of an EPS with little to no interaction from human operators.

TESS Sector 62 observations include physical orbits 131 and 132 of the spacecraft around the Earth. Data collection was paused for a total of 0.63 days to download data. Data were downloaded approximately every seven days, once near apogee of each orbit and once near perigee. In total, there are 25.08 days of science data collected in Sector 62.

The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Computer simulations require the use of meshes to simulate geometries. These meshes capture important geometric features of the design and can be used in machine learning modeling. This report explores the use of graph neural networks (GNNs) to learn features from two-dimensional (2D) airfoil designs represented as a set of nodes connected using edges. This type of network is common in aerospace applications: most geometries are represented as a mesh in order to perform analysis. The objective of this work is to use GNNs to predict the performance of 2D airfoils generated using the program XFOIL. The predicted performance parameters include bulk quantities such as coefficients of lift (CL), drag (Cd, Cdp), moment (Cm), and node-specific quantities such as coefficient of pressure (Cp). In this report, a spline convolutional graph-based neural network is compared with deep learning neural networks to predict both bulk and node-specific quantities. The findings indicate the GNNs are able to predict bulk quantities quite well; however, when the number of outputs is increased, the deep neural network (DNN) proves to be better in its prediction capability. Two different normalization strategies were compared in the training of both GNNs and DNNs: minmax and standard deviation. In both types of networks, standard deviation scaling proved to be the best.

Construction of the unitary plan wind tunnel for the Ames Aeronautical Laboratory in Moffett Field, California. Beginning construction in 1950-51 and completing in 1955. Video shows the building of all stages of construction from the foundation to full completion and view of final building set up. Video should be download and viewed for best results and has run time of 7:07 with color but no sound.

The UAM Airspace research roadmap is being developed as a new System Engineering methodology leveraging Model Based System Engineering (MBSE) capabilities to help organize, integrate, and communicate NASA's UAM airspace research, with the goal of evolving UAM airspace to UML-4. It provides a basis for prioritizing and coordinating research efforts, and for integrating results that build towards NASA’s research goals. Version 2.0 is a baseline version of the roadmap, shared publicly to serve as a focal point for discussion and feedback. This version supersedes earlier publications, and will be superseded itself by later versions.

The National Aeronautics and Space Administration X-56A Multi-Utility Technology Testbed was successfully flown beyond flutter using active feedback control methods. This report discusses the flight-test methods used during envelope expansion and presents the data collected. The frequency and damping of rigid-body flight dynamic modes and the first two structural modes were estimated from the flight data. The flutter mechanism (body freedom flutter) was found to match predictions with coupling occurring between the short period and the first symmetric wing bending mode. Flights up to the instability threshold and beyond were conducted using active feedback control. Several tests were conducted in flight where the control system was disabled momentarily to observe the unstable flutter mode and then reengaged; thus, demonstrating active suppression.