Results for “Chemical processes” — Page 19

NASA NTRS2026

Parametric-Based Heat Rejection Trade Study for Lunar and Martian Surface Operations

Establishing and maintaining a sustained presence on the lunar and/or Martian surfaces will require a diverse portfolio of surface elements (e.g., habitation, mobility, power generation, etc.). Many of these systems generate excess heat that must be rejected across a wide range of magnitudes, temperatures, and duty cycles and under variable environmental conditions. To identify the most promising heat rejection approaches for this diverse portfolio, a heat rejection trade study was conducted to evaluate the performance of different technology approaches across a spectrum of surface environments and heat-load requirements. The trade study consisted of three stages: (1) development of a parametric-based modeling framework, (2) creation of a database of heat rejection technologies, surface elements, and environmental conditions for the Moon and Mars, and (3) execution of a quantitative analysis of various heat rejection technologies across different operating conditions and surface elements. The modeling framework is developed in Python and Excel to prioritize small model size and hence low computational cost to enable large parametric sweeps while avoiding the reliance on proprietary software. Individual heat rejection processes are represented as simple Excel models, and a centralized Python script interfaces with the models to coordinate the parametric study. These simple sizing models were developed to take heat load requirements and environmental parameters as inputs and compute mass, power, and volume as outputs. Rather than assess each heat rejection technology separately for each surface element, a unified parametric space was developed to evaluate all technologies across all elements. This parametric space includes factors related to heat load (e.g., magnitude or temperature) and environment (e.g., surface temperature, sky temperature, solar flux). This effort generated a database containing information on over 60 heat rejection technologies and 30 surface elements. For each surface element, the expected heat rejection requirements were documented and analyzed to determine the most common needs shared across all elements. Environmental conditions at various lunar and Martian latitudes were also established for worst-case hot and worst-case cold scenarios. High-fidelity heat rejection models are currently under development. Preliminary trades between heat rejection technologies including radiators, venting technologies, convective coolers, and more have been conducted to identify promising options. This presentation will summarize the preliminary trade results and provide an overview and discussion of the expected heat loads and thermal environments for sustained surface operations on the Moon and Mars.

Heat Rejection↗

NASA NTRS2010

Evaluation of Anomaly Detection Capability for Ground-Based Pre-Launch Shuttle Operations

This chapter will provide a thorough end-to-end description of the process for evaluation of three different data-driven algorithms for anomaly detection to select the best candidate for deployment as part of a suite of IVHM (Integrated Vehicle Health Management) technologies. These algorithms were deemed to be sufficiently mature enough to be considered viable candidates for deployment in support of the maiden launch of Ares I-X, the successor to the Space Shuttle for NASA's Constellation program. Data-driven algorithms are just one of three different types being deployed [3],[5]. The other two types of algorithms being deployed include a "rule-based" expert system, and a "model-based" system. Within these two categories, the deployable candidates have already been selected based upon qualitative factors such as flight heritage. For the rile-based system, SHINE (Spacecraft High-speed Inference Engine) has been selected for deployment, which is a component of BEAM (Beacon-based Exception Analysis for Multimissions) [4], a patented technology developed at NASA's JPL (Jet Propulsion Laboratory) and serves to aid in the management and identification of operational modes. For the "model-based" system, a commercially available package developed by QSI (Qualtech Systems, Inc.), TEAMS (Testability Engineering and Maintenance System) [1] has been selected for deployment to aid in diagnosis. In the context of this particular deployment, distinctions among the use of the terms "data-driven," "rule-based," and "model-based," call found in [5]. Although there are three different categories of algorithms that have been selected for deployment, our main focus in this chapter will be on the evaluation of three candidates for data-driven anomaly detection. These algorithms will be evaluated upon their capability for robustly detecting incipient faults or failures in the ground-based phase of pre-launch space shuttle operations, rather than based oil heritage as performed in previous studies [5]. Robust detection will allow for the achievement of pre-specified minimum false alarm and/or missed detection rates in the selection of alert thresholds. All algorithms will also be optimized with respect to all of these same criteria. Our study relies upon the use of Shuttle data to act as was a proxy for and in preparation for application to Ares I-X data, which uses a very similar hardware platform for the subsystems that are being targeted (TVC - Thrust Vector Control subsystem for the SRB (Solid Rocket Booster)).

False Alarms↗

NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

NASA NTRSDate not supplied

ARCTRON: A Rapid Experimental Proving Ground for TPS Experiments and Arcjet Technology Development

Innovation in high-enthalpy facilities is fundamentally limited by the cost and risk of experimentation. New concepts for plasma control, diagnostics, facility components, and plasma-material interaction often require repeated iterations that are impractical to perform in production arcjets. As a result, promising ideas may remain unexplored or reach operational facilities only after significant development effort. ARCTRON is being developed as a rapid experimental proving ground where new ideas in plasma science, arcjet engineering, diagnostics, and material response can be conceived, tested, and quantitatively evaluated before transition to large-scale facilities. The platform combines radio-frequency (RF) and DC arc plasma generation, externally applied magnetic fields, configurable gas composition, reduced-pressure operation, laser heating, electrical biasing, and modular diagnostic access. These capabilities permit the plasma source, applied forcing, test article, and measurement configuration to be modified independently, allowing individual physical mechanisms to be isolated more readily than in a traditional test environment. One class of investigations addresses fundamental plasma-surface interaction physics. Conventional material tests often expose a specimen simultaneously to convective heating, reactive species, pressure, shear, radiation, and surface-current effects. The resulting material response may be measured accurately, while the contribution of each mechanism remains difficult to identify. ARCTRON is designed to vary these effects selectively. Plasma chemistry can be changed independently through configurable gas mixtures; magnetic fields and electrical biasing can modify charged-particle transport; laser heating can provide a non-plasma thermal input; and pressure, flow, and discharge mode can be varied over a broad operating space. This enables controlled tests of hypotheses involving surface catalycity, reactive-species transport, plasma-assisted oxidation, electromagnetic effects, shear, and the relative contributions of thermal and chemical loading. A second class of investigations enabled by this approach concerns the engineering of high-enthalpy facilities themselves. Arc-heated facilities are limited by electrode erosion, unstable arc attachment, localized heating, and damage to nozzles and other plasma-facing components. ARCTRON provides a lower-cost environment for testing concepts intended to mitigate these limitations. Candidate investigations include the use of applied magnetic fields to alter current paths and reduce plasma interaction with nozzle walls, ExB forcing to introduce controlled plasma rotation, magnetic or geometric approaches for distributing arc attachment, and alternative electrode or discharge configurations intended to reduce erosion and improve stability. Because the platform is reconfigurable, these concepts can be evaluated through repeated design--build--test cycles before they are considered for implementation in operational facilities. The platform also supports the development and validation of diagnostics that may be difficult to introduce initially into a large arcjet. Current and planned measurements include spatially resolved optical emission spectroscopy, electrostatic probes, fast imaging, pyrometry, calorimetry, laser-induced fluorescence, and absorption spectroscopy. These diagnostics are intended not merely to document a nominal operating condition, but to constrain the local plasma state and its relationship to component or material response. The modular facility geometry allows diagnostic concepts to be tested, calibrated, and compared under repeatable conditions before deployment in more demanding environments. ARCTRON is also supported by an integrated software suite. Automated control and data acquisition allow discharge parameters, gas composition, magnetic fields, diagnostic timing, and test configuration to be recorded as part of each experiment (STARDAC - Software for Testing, Analysis, Research Data, and Control). The Backend for Experiment Analysis, Storage, and Traceability (BEAST) is a database that provides the infrastructure needed to associate heterogeneous measurements with facility configuration, specimen identity, calibration state, geometry, and analysis provenance. This backend is particularly important for exploratory campaigns, in which many related configurations may be tested, and the value of an individual experiment depends on its connection to earlier and subsequent iterations. Complementary analysis capabilities, including computer-vision-based transient response measurements (arcjetCV), three-dimensional surface reconstruction (STARSCAN), and model-based Bayesian inference (SHIELD), and tomography data analysis (TOMATO, PuMA) can be incorporated when required by a specific hypothesis without becoming the focus of every campaign. The central objective of ARCTRON is therefore not to maximize heat flux or reproduce a complete flight environment. Its purpose is to reduce the cost and time required to ask consequential questions about plasma behavior, plasma-facing materials, diagnostics, and arcjet technology. By providing a controlled environment for rapid reconfiguration, mechanism isolation, quantitative measurement, and iterative engineering, ARCTRON can help mature concepts that would otherwise remain too speculative or too risky for evaluation in production facilities. The resulting knowledge can then guide the design of material models, focus test objectives in larger arcjets, reduce facility-development risk, and improve the physical basis of high-enthalpy ground testing. This work will present the ARCTRON architecture, operating modes, diagnostic suite, and digital experimental workflow. Initial experimental results from the first integrated operation of the facility will be presented, including flow characterization, power limitations, and deployment of the initial diagnostic suite. Ongoing development efforts aimed at catalycity characterization, magnetic plasma control, and advanced optical diagnostics will also be discussed, illustrating how the platform supports rapid iteration from concept to experiment.

experimental diagnostics↗

NASA NTRS2026

Adaptation and Response in Drylands (ARID): A Dryland Research Agenda and Campaign Strategy

Dryland ecosystems cover 41% of Earth’s land surfaces, account for 44% of cultivated lands and 60% of food sources, and make large contributions to the global water and carbon cycles. However, these ecosystems are experiencing unprecedented extremes including heatwaves, floods, and droughts as well as hotter temperatures and often declining water resource availability. These ecosystems are some of the most challenging to monitor given their high temporal variability with rapid response to their environment as well as vast spatial variability with intermixing of different plant species and life forms amongst bare soil coverage. The Adaptation and Response in Drylands (ARID) campaign was selected by National Aeronautics and Space Administration (NASA) as a scoping study to develop a research agenda for a dryland field campaign. Here, we detail our ARID science research agenda and implementation plan that were developed based on an extensive community engagement effort in over 160 events with over a thousand scientists, land managers, and Tribal communities between 2023 and 2024. The selected science themes cover drought and climate variability, ecosystem structure, function, and biodiversity, carbon cycle interannual variability and trends, and social ecological systems (land management and adaptation). We then detail our remote sensing, modeling, and field-based strategies to capture high temporal and high spatial resolution processes. Finally, our implementation strategy is presented which includes focus area selections in a core intensive western U.S. domain and distributed international domains. This strategy includes our overarching guiding principles of using multi-temporal airborne acquisitions and super sites as well as enhancing land management in co-development with end-user partners. While originally developed for NASA, our ARID report creates a blueprint for any future dryland field campaign, at any scale, that can be implemented widely for foundational and applied science objectives.

drylands↗

NASA NTRS1997

Future Aerospace Technology in the Service of the Alliance: Sustained Hypersonic Flight - Volume 3

Sustained Hypersonic Flight capability is envisioned for several future military and civilian applications, such as long range immediate reaction reconnaissance, high speed interception of air targets, long range precision strike against hardened or time critical targets, and access to space. The inherent reduction in time-to-target and low vulnerability will permit new operational tactics. The symposium outlined mission needs and operational scenarios for hypersonic vehicles. Addressed were: Technological issues and challenges in external hypersonic aerodynamics and design, propulsion and engine/airframe integration, military ramjet applications, overall system design including structures and materials development needs, and test facilities. Ground based test facilities cannot provide full simulation at Mach numbers above 5. Therefore, the use of Computational Fluid Dynamics design tools is essential. For routine use of the computational tools in analysis, design and optimization, it is necessary to reduce the time for the entire computational process by two orders of magnitude. Hypersonic boundary layer transition remains a critical design issue because of the important impact on engine drag and on heating, which can affect the choice of materials and thermal protection systems. In addition, reliable prediction of scramjet net thrust is an absolute must in resolving hypersonic air breathing vehicle design issues. Due to current facility and computational shortfalls, the development of future hypersonic flight systems requires research flight tests in the technology areas of boundary layer transition and air-breathing propulsion engine performance. For sustained hypersonic flight beyond Mach 6, the supersonic combustion ramjet (scramjet) engine is the only choice for the near future. Only this air-breathing concept offers a significant promise of large reductions in required propellant fractions, increased payload fractions, and reduced size vehicles, together with a foreseeable technological feasibility. Airframe/engine integration, combustor design and thermal management are the predominant engineering tasks. Fuels, hydrogen or hydrocarbon, must be matched to the operational needs of military or civil use. Experience in existing ramjet propelled missiles capable of speeds up to Mach 4 can support the development effort. The potential mission and cost benefits of sustained hypersonic flight to both military and civil applications are tremendous. From the budget point of view, the possibility of sharing development costs between military and civil programs offers a specific advantage.

Combustion chambers↗

NASA NTRSDate not supplied

From Exploration Flight Test-1 to Artemis II--A NASA Langley's Orion Aerosciences Overview

The Orion Aerosciences program at NASA Langley has played a central role in advancing the aerodynamic and aeroheating prediction capabilities required for the Orion crew vehicle’s return from deep space. This presentation provides a technical overview of aerosciences contributions spanning Exploration Flight Test-1 (EFT-1), Artemis I, and the ongoing post-flight analysis of Artemis II. EFT-1 provided the first high-energy entry dataset for Orion, enabling critical validation of aerodynamic force and moment predictions, static and dynamic stability characteristics, and aeroheating environments at relevant flight Mach and Reynolds numbers. Flight-derived pressure data were used to refine the Flush Air Data System (FADS) methodology for atmospheric density reconstruction and to improve Best Estimated Trajectory (BET) solutions. The EFT-1 data also offered key insights into heat shield performance, including char layer recession, in-depth thermal response, and material retention behavior under flight conditions, informing updates to both thermal response models and uncertainty quantification practices. Building on EFT-1, Artemis I extended the database to true lunar-return conditions. Observations of heat shield performance, including localized char loss, bondline response, and recession variability, provided an unprecedented opportunity to reassess Thermal Protection System (TPS) and aeroheating modeling assumptions. Aerodynamic reconstruction efforts incorporated improved FADS calibration, enhanced atmospheric modeling, and refined force and moment databases to reduce trajectory and load uncertainties. Aeroheating comparisons between pre-flight predictions and flight data enabled targeted model updates, particularly in transitional flow environments and wake heating regions. For Artemis II, these lessons were systematically incorporated into the pre-flight prediction process. Updates included refined aerodynamic databases anchored to flight-validated corrections, improved density estimation and BET methodologies using enhanced database interpolation algorithm and FADS modeling, and revised aeroheating design environments informed by Artemis I material response observations. By the time of the workshop, Artemis II post-flight analysis will be underway, and preliminary findings will be presented where available, including early comparisons of aerodynamic reconstruction, atmospheric density estimation, and thermal protection system performance relative to updated predictions. Collectively, this body of work is a testament to the dedicated and multidisciplinary team whose sustained efforts have contributed to the program’s success and to the progressive maturation of Orion aerosciences modeling through numerical modeling, ground and flight data assimilation. The integrated advancement of aerodynamics, trajectory reconstruction, FADS-based density estimation, and aeroheating analysis has reduced predictive uncertainty and strengthened confidence for future crewed lunar and deep-space missions.

Orion↗

NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. In a previous publication, we discussed how we selected the three tools based primarily on their ability to be certified for human spaceflight and described our plans for the prototype. This abstract is due October 23, 2009, and the Ares I-X launch is currently scheduled for October 27, 2009. If this abstract is accepted, then the full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes. Also, the paper will describe the three tools and how they are used in the prototype. It will include a description of the TEAMS models of the Ares I-X first-stage TVC and associated ground hydraulics and how we adapted the TVC model for use in real-time diagnostics. It will describe the SHINE rules used for fault detection and mode identification and the software architecture that interfaces the various pieces of existing software that are part of the prototype to one another. It will describe how we selected the sensor values and commands that were used to train the IMS model and how we optimized the number of clusters in the IMS model. It will include screen shots of the graphical display that we developed in Java to display the outputs of the three tools. Because Ares I-X data was not yet available to us while we were developing the prototype, we used historical data from the Space Shuttle's Solid Rocket Booster (SRB) TVCs and the associated ground hydraulics to train IMS and to test the entire prototype. Because most of the failure modes that we modeled have never occurred in the Shuttle we inserted simulated failures into the Shuttle data. The Ares I-X first-stage TVC is very similar to the SRB TVC and we expect the data will be very similar. After the launch, we will determine how similar the data actually is and report how any differences in the data affected the diagnostic accuracy of the prototype. Finally, although we did not get the prototype certified, we designed it in a way that it could be certified and wrote a preliminary certification plan. The paper will include a brief summary of how we considered the need for certification in the design of the prototype, how we tested the prototype before deploying it to Hangar AE, and how we would propose to get it certified if it were deployed as an operational system. The paper will conclude with a description of some of the challenges we faced and some of the lessons learned in developing and deploying the prototype.

International Space Station↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗

NASA NTRSDate not supplied

NASA's Small Spacecraft and Distributed Systems: Development and Demonstration of Technologies Enabling Swarms and New Spacecraft Platforms with AI and Edge Computing

NASA’s Small Spacecraft & Distributed Systems (SSDS) within the Research and Technology Mission Directorate (RTMD) expands U.S. capability to execute unique missions through targeted investment, rapid development, and flight demonstration of small spacecraft technologies applicable to exploration, science and the commercial space sector. SSDS strategically invests in technology development and on-orbit demonstrations executed across NASA, other government agencies, industry, and academia. The program’s University SmallSat Technology Partnerships initiative awards academic researchers with the opportunity to collaborate with NASA to mature innovative technology. Capabilities aligned with RTMD’s technology shortfalls and interests - power, processing, propulsion, sensors, communications, autonomous navigation, architectures, and advanced applications like artificial intelligence (AI), machine learning, and edge computing - are prioritized in SSDS investments. These investments enable distributed, autonomous, and cooperative small spacecraft systems that support swarm missions extending beyond low Earth orbit into cislunar and deep space. This paper highlights representative SSDS flight demonstrations that mature these capabilities to enable a future operational infrastructure needed to support sustained exploration of the Moon and beyond. SSDS’s investment strategy emphasizes rapid development and on-orbit demonstration to validate spacecraft technologies required for swarms and distributed mission architectures. The Starling swarm technology demonstration mission exemplifies this approach by advancing distributed spacecraft autonomy, cooperative operations, and space situational awareness. Extended flight testing and ongoing studies of next generation swarm configurations and on-orbit space traffic monitoring and management continue to inform future swarm designs. DiskSat’s four-spacecraft demonstration mission represents SSDS’s strategic vision to expand the design space for future small spacecraft through its commitment to advance novel platform concepts that can impact how science is performed on orbit. Continuing to invest in future platforms, the notional PY12 concept is a 12-spacecraft swarm hosting neuromorphic processors and is envisioned as an on-orbit testbed for AI, edge computing, and positioning, navigation and timing technologies. SSDS also invests in single-spacecraft technology demonstrations that underpin the success of future swarm missions and accelerate the availability of validated technologies across the small spacecraft ecosystem. Examples of such demonstrations include Pathfinder Technology Demonstrator-3 (PTD-3), which performed high-rate optical communications; PTD-R, which demonstrated a camera capable of simultaneous ultraviolet and short-wave infrared optical sensing; and CAPSTONE, the Cislunar Autonomous Positioning System Technology and Operations Navigation Experiment, which validated autonomous navigation in cislunar space. Collectively, SSDS-funded demonstrations advance capabilities across swarms and illustrate a coordinated investment strategy to mature high-impact technologies required for autonomous, distributed, and cooperative small spacecraft systems for low Earth orbit, cislunar, and deep space applications. Technology demonstrations strengthen SSDS partnerships with industry, academia, and other government agencies, and promote small spacecraft community adoption of capabilities required to close technical gaps for swarm missions.

Jan Stupl↗

NASA NTRSDate not supplied

Investigation of Lunar-Inspired Geopolymer Concrete Formulations Mixed and Cured in Microgravity on the International Space Station (ISS)

The research outlined in this presentation investigates the use of various lunar regolith simulants in geopolymer lunar concrete mixes mixed and cured on the International Space Station (ISS). The motivation for this work is to study the effects of gravity on the microstructure of alkali-activated materials cured with heat, and to develop materials for the construction of long-term infrastructure on the lunar surface with in-situ resource utilization (ISRU). ISRU for construction materials reduces the cost and mass of payloads related to lunar construction. The advantage of geopolymer concrete as opposed to traditional portland cement concrete is that water acts as a medium for the polymerization reaction and leaves the system throughout the process, reducing its demand. Twelve samples of lunar regolith simulant and a solution composed of sodium hydroxide and sodium silicate were sent to the ISS. The three simulants were OPRH2N, OPRL2N, and JSC-1AF, using only particles less than 53 µm in diameter to increase reactivity of the simulant. Simulant to solution ratios were determined by workability while mixing. The simulant and solution were sealed Burst Pouches® along with 2 other sealed bags to prevent material from leaking. Crew member F-14 conducted testing on the ISS by introducing the solution to the simulant in the Burst Pouch®, mixing the sample with a spatula, and then clamping the specimen in the fresh state to prevent flow inside the Burst Pouch®. These specimens were then put in a thermos heated to 80C via sealed drinking water bags to cure for 24 hours with a temperature logger. The cured specimens remained in microgravity for at least 28 days and were returned from the ISS in February 2025. The specimens were then brought to the NASA Marshall Space Flight Center (MSFC) to analyze. Material characterization consisted of conducting Micro-CT tests of entire samples in their sealed apparatus to a resolution of 25µm. 2D image slices were saved in each orthogonal direction of each specimen at a 0.03 mm step size from the 3D model to conduct analytical porosity calculations. Representative samples from each specimen were sampled to perform helium gas pycnometery and were then mounted in resin for SEM imaging, EDS, and nanoindentation. Porosity was analyzed analytically using micromechanics modelling with the assistance of the NASA Multiscale Analysis Tool (NASMAT), as well as the NASA Advanced Supercomputing (NAS) servers (V. Saseendran & N. Yamamoto, 2024). Density was measured using helium gas pycnometery and was then compared to the theoretical density for experimental porosity calculation. Due to the samples’ non-uniform shape being cured in a pouch, traditional compression and tensile strength testing could not be performed. Nanoindentation was conducted at Clarkson University to determine the microhardness and reduced modulus of elasticity. Results from flight samples can be compared to ground samples currently in DLR’s possession to determine the effect on microstructure from being mixed and cured in microgravity. This study gives further insight and understanding of geopolymer lunar concrete and its viability as a lunar construction material with ISRU.

Adam Johnson↗

NASA NTRSDate not supplied

Performance Assessment of LunaNet’s Augmented Forward Signal

LunaNet provides a common set of interoperable specifications for communication and position, navigation and time (PNT) services and interfaces soon to be implemented in lunar vicinity. The LunaNet Interoperability Specification (LNIS) provides the design for the GNSS-like Augmented Forward Signal (AFS), which enables orbiting and surface users in lunar space, such as Artemis, to estimate their position, velocity, and time. The specification of AFS defines two orthogonal signal components on a single carrier: the in-phase component (AFS-I), a lower-chip-rate data channel tailored for applications where low SWaP (Size, Weight, and Power) is critical (e.g., IoT devices or search and rescue), and the quadrature component (AFS-Q), a high-chip-rate data-less pilot signal for high-precision, robust lunar navigation and positioning applications. An initial description of AFS was provided in LNIS 2023, with initial analysis results shown in Dafesh 2024 and Dafesh 2025, and the current signal in space description provided in LNIS 2025. As part of NASA's Lunar Communication Relay and Navigation Systems (LCRNS) project, this work expands upon the initial analysis results and proposes a new expanded set of AFS-Q spreading codes that exceed the cross-correlation and autocorrelation sidelobe performance of L1C and other GNSS signals, while providing additional expansion capabilities for future service satellites. A set of 420 codes was selected from a Weil-based code derived from the prime number 10247, which is larger than the 10243 prime number used to derive BeiDou’s B1C Weil sequences. Both the initial set of 210 codes and the expanded set of 420 codes are shown to provide the best cross-correlation of any 10230-chip satellite navigation codes. The performance is demonstrated for hierarchical sets of spreading codes optimized and organized in sets of 30 codes. The work also compares LunaNet’s AFS to terrestrial GNSS signals in terms of acquisition, tracking, and data demodulation performance. Performance is evaluated for receivers that only track the 1.023 MCPS data channel spreading code for low SWaP IoT use cases, as well as for receivers that track both the 1.023 MCPS data channel and the 5.115 MCPS pilot channel spreading code for high-performance use cases. Performance is assessed in the presence of interference and thermal noise. The analysis is performed in terms of expected operating conditions on the lunar surface. Several unique flexibility aspects of the augmented forward signal are described, including the use of the Q channel’s secondary and tertiary codes to enable variable coherent integrations during acquisition. This is compared to GNSS signals such as L5/E5 and MBOC in terms of achievable processing gain for interference mitigation versus acquisition complexity. The work details acquisition and tracking techniques used to optimally acquire and track the primary, secondary, and tertiary codes on the Q channel, as well as acquisition of the I channel spreading code. Acquisition of the 8 ms, Q channel spreading code is also compared to joint acquisition of the I and Q channel primary codes in noise and interference environments

LCRNS↗

NASA NTRSDate not supplied

Performance Assessment of LunaNet’s Augmented Forward Signal

LunaNet provides a common set of interoperable specifications for communication and position, navigation and time (PNT) services and interfaces soon to be implemented in lunar vicinity. The LunaNet Interoperability Specification (LNIS) provides the design for the GNSS-like Augmented Forward Signal (AFS), which enables orbiting and surface users in lunar space, such as Artemis, to estimate their position, velocity, and time. The specification of AFS defines two orthogonal signal components on a single carrier: the in-phase component (AFS-I), a lower-chip-rate data channel tailored for applications where low SWaP (Size, Weight, and Power) is critical (e.g., IoT devices or search and rescue), and the quadrature component (AFS-Q), a high-chip-rate data-less pilot signal for high-precision, robust lunar navigation and positioning applications. An initial description of AFS was provided in [1], with initial analysis results shown in [2] and [3] and the current signal in space description provided in [4]. As part of NASA's Lunar Communication Relay and Navigation Systems (LCRNS) project, this work expands upon the initial analysis results and proposes a new expanded set of AFS-Q spreading codes that exceed the cross-correlation and autocorrelation sidelobe performance of L1C and other GNSS signals, while providing additional expansion capabilities for future provider satellites. A set of 420 codes was selected from a Weil-based code derived from the prime number 10247, which is larger than the 10243 prime number used to derive Beidou’s B1C Weil sequences. Both the initial set of 210 codes and the expanded set of 420 codes are shown to provide the best cross-correlation of any 10230-chip satellite navigation codes. The performance is demonstrated for hierarchical sets of spreading codes optimized and organized in sets of 30 codes. The new codes were developed using an optimization approach and correlation methodology described in [5]. The work also compares LunaNet’s AFS to terrestrial GNSS signals in terms of acquisition, tracking, and data demodulation performance. Performance is evaluated for receivers that only track the 1.023 MCPS data channel spreading code for low SWaP IoT use cases, as well as for receivers that track both the 1.023 MCPS data channel and the 5.115 MCPS pilot channel spreading code for high-performance use cases. Performance is assessed in the presence of interference and thermal noise. The analysis is performed in terms of expected operating conditions on the lunar surface. Several unique flexibility aspects of the augmented forward signal are described, including the use of the Q channel’s secondary and tertiary codes to enable variable coherent integrations during acquisition. This is compared to GNSS signals such as L5/E5 and MBOC in terms of achievable processing gain for interference mitigation versus acquisition complexity. The work details acquisition and tracking techniques used to optimally acquire and track the primary, secondary, and tertiary codes on the Q channel, as well as acquisition of the I channel spreading code. Acquisition of the 8 ms Q channel spreading code is also compared to joint acquisition of the I and Q channel primary codes in noise and interference environments.

LCRNS↗

NASA NTRS2006

Nitrile/Buna N Material Failure Assessment for an O-Ring used on the Gaseous Hydrogen Flow Control Valve (FCV) of the Space Shuttle Main Engine

After the rollout of Space Shuttle Discovery in April 2005 in preparation for return-to-flight, there was a failure of the Orbiter (OV-103) helium signature leak test in the gaseous hydrogen (GH2) system. Leakage was attributed to the Flow Control Valve (FCV) in Main Engine 3. The FCV determined to be the source of the leak for OV-103 is designated as LV-58. The nitrile/Buna N rubber O-ring seal was removed from LV-58, and failure analysis indicated radial cracks providing leak paths in one quadrant. Cracks were eventually found in 6 of 9 FCV O-rings among the three Shuttle Orbiters, though none were as severe as those for LV-58, OV-103. Testing by EM10 at MSFC on all 9 FCV O- rings included: laser dimensional, Shore A hardness and properties from a dynamic mechanical analyzer (DMA) and an Instron tensile machine. The following test data was obtained on the cracked quadrant of the LV-58, OV-103 O-ring: (1) the estimated compression set was only 9.5%, compared to none for the rest of the O-ring; (2) Shore A hardness for the O.D. was higher by almost 4 durometer points than for the rest of the O-ring; and (3) DMA data showed that the storage/elastic modulus E was almost 25% lower than for the rest of the O-ring. Of the 8 FCV O-rings tested on an Instron, 4 yielded tensile strengths that were below the MIL spec requirement of 1350 psi-a likely influence of rubber cracking. Comparisons were made between values of modulus determined by DNA (elastic) and Instron (Young s). Each nitrile/Buna N O-ring used in the FCV conforms to the MIL-P-25732C specification. A number of such O-rings taken from shelf storage at MSFC and Kennedy Space Center (KSC) were used to generate a reference curve of DMA glass transition temperature (Tg) vs. shelf storage time ranging from 8 to 26 years. A similar reference curve of TGA onset temperature (of rubber weight loss) vs. shelf storage time was also generated. The DMA and TGA data for the used FCV O-rings were compared to the reference curves. Correlations were also made between the DMA modulus (at 22 C) and Shore A hardness for all 9 of the FCV O-rings used among the three Shuttle Orbiters. The radial cracking in the FCV O-rings was determined to be due to ozone attack, as nitrile/Buna N rubber is susceptible to such attack. Nitrile/Buna N material under MIL-P25732C should be used in a hydraulic fluid environment to help protect it from cracking. However, the FCV O-rings were used in an air only environment. The FCV design has as much as a 9-mil gap that allows the O.D. of the O-ring to be directly exposed to ozone, pressurized air and some elevated temperatures, accelerating the weathering process that leads to O-ring cracking. Space Shuttle flights will likely not continue past 2010. Therefore, Shuttle management decided to continue using the nitrile/Buna N material for the FCVs, but have each O-ring replaced after 3 years to minimize any chances for crack initiation.

Doug Wingard↗

NASA NTRS2026

Machine Learning for Predicting Team Functioning in HERA Missions

Team functioning is integral to success in future long term space exploration missions. Proactively detecting declines in team functioning can mitigate conflict and ensure mission success. This project developed a speech-based artificial intelligence (AI) system that unobtrusively predicts degradation in team functioning, including performance and cohesion, in the Human Exploration Research Analog (HERA) Campaigns 4 and 5. The AI system conducted automated analysis of the prosodic (tone of voice) and linguistic (language content) components of speech, modeling interpersonal dynamics at both the turn-taking and day-wide levels. We investigated team functioning via observing structured interactions (i.e., multi-mission space exploration vehicle-extra vehicular activity [MMSEV-EVA], team interaction battery [TIB]) and unstructured interactions before the MMSEV-EVA task. We developed machine learning models to predict team functioning (objective task accuracy, self reported team efficacy and self reported team cohesion) by analyzing OpenSmile acoustic features, linguistic descriptors extracted via the linguistic inquiry and word count (LIWC) dictionary, and semantic embeddings. In the TIB, static models using logistic regression and random forests were not able to predict task accuracy, but predicted team efficacy and cohesion during both the decision making and relational tasks to a moderate level (60-70%). Majority voting on the individual turns to predict day long team efficacy further increased accuracies (70-80%). Finally, long short-term memory (LSTM) models showed the best performance across all variables (80-91%), including task performance. In the MMSEV-EVA, static models achieved an accuracy of 60% with majority voting, which increased to 80% through the incorporation of mission day as a variable, accounting for the learning effect. A key finding across both tasks was the "team-dependent" nature of these interactions; models achieved much higher accuracy when trained on prior days of the same team's data rather than attempting to generalize across entirely different teams, with even 1-2 days of prior data per team achieving 5-15% improvement over team-independent models. In addition, the incorporation of pre-task data from the same team also improves model performance, e.g., incorporating data from the decision-making task of the TIB, which preceded the relational task, improved the prediction of team efficacy and cohesion during the latter. We compared model performance when trained on machine-generated data compared to data that had been further corrected by human annotators. Overall, models trained on human-corrected data exhibited a modest improvement in performance, particularly when acoustic features were used. We found no significant correlation between word error rate (WER) and model accuracy (r(55) = -0.08, p = 0.51), but model’s accuracy was significantly higher for medium/high quality transcription (0.74 (SD = 0.48)) compared to the low-quality group (0.64 (SD = 0.36)) (t(63)=2.82, p = 0.006). Based on these, several design recommendation emerge, that could inform Standards at NASA. Models predicting team functioning should incorporate at least one to two days of historical interaction data, include brief pre-task discussions, and explicitly model temporal learning effects, especially for longer operational tasks. Minimum quality standards for automated speech-processing pipelines are needed, given the performance gains observed with manually corrected acoustic data. Finally, systems should leverage both acoustic features and language embeddings in complementary ways, with modality choices and fusion strategies tailored to mission context, task demands, and data quality requirements.

Shrivatsa Mishra↗

NASA NTRS2009

General Purpose Data-Driven Monitoring for Space Operations

As modern space propulsion and exploration systems improve in capability and efficiency, their designs are becoming increasingly sophisticated and complex. Determining the health state of these systems, using traditional parameter limit checking, model-based, or rule-based methods, is becoming more difficult as the number of sensors and component interactions grow. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. System health can be monitored by comparing real-time operating data with these nominal characterizations, providing detection of anomalous data signatures indicative of system faults or failures. Data-driven techniques have a number of advantages over other methods for monitoring complex space vehicles. Unlike model-based systems, the developer does not need to understand or encode the internal operation of the system. The knowledge required to monitor the system is automatically derived from archived data from system operation. Unlike rule-based systems, data-driven systems do not require system analysts to define nominal relationships among sensors. Analysts can and often do determine these relationships for a system with few sensors; it is more difficult to analytically determine the nominal relationship among a large number of sensors. Data-driven techniques are not limited to low-dimensional spaces and work as effectively with dozens of parameters as they do with a few. Knowledge bases formed by data-driven techniques are also easy to update. As the operating envelope of the monitored system is expanded, data-driven techniques can be quickly retrained to incorporate the new behavior into the knowledge base. The expertise and time-consuming process of updating a model or rule base to maintain consistency with the new operation is not required. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or analysis of archived events. System data is compared with the nominal IMS model to produce a measure of how well current system behavior matches the normal behavior defined by the training data. Significant deviations from the nominal system model can provide alerts to system malfunctions or precursors of significant failures. The scope of IMS based data-driven monitoring applications continues to expand with current development activities. Successful IMS deployment in the International Space Station (ISS) flight control room to monitor ISS attitude control systems has led to applications in other ISS flight control disciplines, such as thermal control. It has also generated interest in data-driven monitoring capability for Constellation, NASA's program to replace the Space Shuttle with new launch vehicles and spacecraft capable of returning astronauts to the moon, and then on to Mars. Several projects are currently underway to evaluate and mature the IMS technology and complementary tools for use in the Constellation program. These include an experiment on board the Air Force TacSat-3 satellite, and ground systems monitoring for NASA's Ares I-X and Ares I launch vehicles. The TacSat-3 Vehicle System Management (TVSM) project is a software experiment to integrate fault and anomaly detection algorithms and diagnosis tools with executive and adaptive planning functions contained in the flight software on-board the Air Force Research Laboratory TacSat-3 satellite. The TVSM software package will be uploaded after launch to monitor spacecraft subsystems such as power and guidance, navigation, and control (GN&C). It will analyze data in real-time to demonstrate detection of faults and unusual conditions, diagnose problems, and react to threats to spacecraft health and mission goals. The experiment will demonstrate the feasibility and effectiveness of integrated system health management (ISHM) technologies with both ground and on-board experiments. Initially, the TVSM software will run open loop, providing system health information and recommendations to ground operators, without automatically performing fault-mitigating corrective actions. After the end of the satellite's mission, closed loop tests combining TVSM monitoring and diagnosis with reactive capabilities by the flight software will be performed. In addition to monitoring for long periods of actual operation, the experiment will include fault injection into TacSat-3 data as well as commanded operations to test and evaluate automatic ISHM monitoring and recovery under controlled conditions.

Satellites↗

NASA NTRSDate not supplied

Preliminary Assessment of the Impact on the V-Band Oxygen Channels From Satellite Communication Uplinks

We calculate the percentage of time that an ATMS-like instrument [1] will be illuminated by the uplink beam of one of the proposed V-band communication system and estimate the damage resulting from such exposure. Using a combination of openly available information and educated guesses about the location and characteristics of the up/down link terminals, we constructed the ground segment of a hypothetical high-speed communication network. The space segment of the network was constructed from the orbital data of the existing Starlink constellation [2] of 6223 communication satellites (comsats) which is used as strawman to represent any other possible constellation of communication satellites. It is shown that without a very delicate balance of frequency allocations (science vs telecommunications), coupled with extremely steep and deep bandpass-defining filters, and strict adherence to the agreed limits (i.e. no out-of-band transmissions) the deployment of the telecommunication network leads to almost-complete loss of some important geophysical data. For the analysis we use the spectral characteristics of the ATMS instrument with the ephemeris for the NOAA-21 satellite [3]. The analysis is conducted for the USA and the simulation covers 8 consecutive days in July 2024. Effective and accurate microwave remote sensing of the atmosphere depends on the availability of interference-free spectrum windows at frequencies which are prescribed by physical processes [e.g. 4]. The family of resonant lines of the oxygen molecule near 60 GHz provides a unique opportunity to sample the vertical distribution of temperature and density from space, and it has been exploited for weather and climate studies from polar-orbiting satellites since 1978 (MSU on TIROS-N [5]). It remains a staple in the payloads operated by Russia, China, USA, Japan, France, India, UK, Ukraine [6] which are built around a common blueprint: a few wide-band (hundreds of MHz) channels around 50 GHz to sample the atmosphere and the surface while several more channels with high spectral resolution (few MHz) sample the individual resonant lines. Accurate retrieval of the environmental parameters depends upon the data provided by both sets of channels, and the their location in frequency space is not arbitrary and cannot be altered at will [7, 8]. The introduction of 5G technology in 2019 has driven telecommunication companies to request more bandwidth to be dedicated to their devices. This additional bandwidth is only available in spectral regions traditionally reserved for environmental and astrophysical research, such as the V-band between 50 and 60 GHz for up/downlink between satellites in low-earth orbits and terminals connected to fiberoptics network for distribution to high-speed local internet services. The power broadcast by the uplink communication leg is many orders of magnitude greater than the natural thermal signal emitted from the Earth scene. If the ground antenna were to perfectly align with the passive instrument’s antenna, the spaceborne receiver would suffer permanent, irreparable damage. While a direct boresight-to-boresight conjunction is extremely unlikely (even with a large constellation of satellites the fraction of the celestial sphere occupied by the satellites remains minuscule) the finite size of the ground station’s antenna beam in the sky suggests that the ATMS will be in the near background (as seen from the ground station) of one of the communication satellites and will be illuminated by either the main lobe or the near sidelobes of the uplink antenna more often than it is desirable. For our analysis we first calculate the position of the ATMS with respect to each of the ground stations at a resolution of 0.2 sec, then calculate the position of each of the comsats which are at least 25 deg above the station’s local horizon; finally we calculate the angle between the line-of-sight of the ATMS and the line-of-sight of the comsat. We assume that the gain pattern of the ground station is circularly symmetric; the angle-off-station-boresight then provides an attenuation of the uplink power which we use to assess the likely effect upon the passive instrument’s operations. We assume that each ground station can communicate with all the comsats in its field of view; this implies that, on average, a ground station can engage with 46 comsats simultaneously. The analysis is repeated for the case when the uplink broadcast within the ATMS passive channels (in-band scenario) and for the case when the uplink is limited to frequencies adjacent to the ATMS channels (out-of-band scenario). The antenna of the ground station is modelled as having a HPBW (Half-Power Beam Width) of 0.16 deg and EIRP (Equivalent Isotropic Radiated Power) of 70 dBW. We account for the geometric dissipation of the signal caused by the satellite orbital altitude, the attenuation induced by atmospheric gasses at 51 GHz and the mismatch between the circular polarization of the ground-based transmitting antenna and the linear polarization of the satellite-borne receiving antenna. The damages on ATMS are estimated from bench-level measurement conducted at the ATMS’ manufacturer facilities [unpublished].

passive microwave↗