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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

Double Layers in Expanding Plasmas and Their Relevance to the Auroral Plasma Processes

When a dense plasma consisting of a cold and a sufficiently warm electron population expands, a rarefaction shock forms [Bezzerides et al., 1978]. In the expansion of the polar wind in the magnetosphere, it has been previously shown that when a sufficiently warm electron population also exists, in addition to the usual cold ionospheric one, a discontinuity forms in the electrostatic potential distribution along the magnetic field lines [Barakat and Schunk, 1984]. Despite the lack of spatial resolution and the assumption of quasi-neutrality in the polar wind models, such discontinuities have been called double layers (DLs). Recently similar discontinuities have been invoked to partly explain the auroral acceleration of electrons and ions in the upward current region [Ergun et al., 2000]. By means of one-dimensional Vlasov simulations of expanding plasmas, for the first time we make here the connection between (1) the rarefaction shocks, (2) the discontinuities in the potential distributions, and (3) DLs. We show that when plasmas expand from opposite directions into a deep density cavity with a potential drop across it and when the plasma on the high-potential side contains hot and cold electron populations, the temporal evolution of the potential and the plasma distribution generates evolving multiple double layers with an ,extended density cavity between them. One of the DLs is the rarefaction-shock (RFS) and it forms by the reflections of the cold electrons coming from the high-potential side; it supports a part of the potential drop approximately determined by the hot electron temperature. The other DLs evolve from charge separations arising either from reflection of ions coming from the low-potential side or stemming from plasma instabilities; they support the rest of the potential drop. The instabilities forming these additional double layers involve electron-ion (e-i) Buneman or ion-ion (i-i) two-stream interactions. The electron-electron two-stream interactions on the high-potential side of the RFS generate electron-acoustic waves, which evolve into electron phase-space holes. The ion population originating from the low-potential side and trapped by the RFS is energized by the e-i and i-i instabilities and it eventually precipitates into the high-potential plasma along with an electron beam. Applications of these findings to the auroral plasma physics are discussed.

Singh, Nagendra↗
NASA NTRSDate not supplied

Ground-Testing of One-Way Ranging from a Lunar Beacon Demonstrator Payload (Lunar Node -1)

Similar to lighthouses providing navigation aids and awareness to ships sailing along the coast, in-situ navigation beacons can be used to provide redundancy, awareness, and improved onboard position knowledge for vehicles operating in and around the lunar sphere of influence. Lunar Node - 1 is a radio frequency navigation beacon that is manifested on Intuitive Machines' NOVA-C lunar lander flight in 2022 for operation during cruise and from a mid-latitude landing location. This payload utilizes commercial components developed for Low Earth Orbit applications and packages them into an integrated package to provide a navigation reference signal as part of a large lunar-centric navigation network. As part of its operations, Deep Space Network ground stations will operate as the notional "user" of this service, allowing for performance characterization, calibration, and operation of the beacon payload. This paper provides an overview of the payload, how its fits into a large concept of operations, and ground testing results. A specific focus is on ground testing of the payload with DSN ground receivers as part of Radio Frequency (RF) Compatibility Testing. A primary goal of this testing was to characterize a pseudo-noise ranging code sequence generated by the payload. The results in this paper define the testing sequence, unique operational constraints with two-way ranging ground equipment, and applications of the ground-derived error model to predicted in-flight performance of the ranging code.

Evan J Anzalone↗
NASA NTRSDate not supplied

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗
NASA NTRS2006

Standardizing an End-to-end Accounting Service

Currently there are no space system standards available for space agencies to accomplish end-to-end accounting. Such a standard does not exist for spacecraft operations nor for tracing the relationship between the mission planning activities, the command sequences designed to perform those activities, the commands formulated to initiate those activities and the mission data and specifically the mission data products created by those activities. In order for space agencies to cross-support one another for data accountability/data tracing and for inter agency spacecraft to interoperate with each other, an international CCSDS standard for end-to-end data accountability/tracing needs to be developed. We will first describe the end-to-end accounting service model and functionality that supports the service. This model will describe how science plans that are ultimately transformed into commands can be associated with the telemetry products generated as a result of their execution. Moreover, the interaction between end-to-end accounting and service management will be explored. Finally, we will show how the standard end-to-end accounting service can be applied to a real life flight project i.e., the Mars Reconnaissance Orbiter project.

recommendations↗
DOE OSTI2025

Remote sensing images, DEM, and point clouds associated with “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds”

This data package is associated with the publication “Accuracy evaluation of cost-effective 3D reconstruction approaches for hydrobiogeochemical processes in non-perennial stream riverbeds” published in Frontiers in Environmental Science, Environmental Informatics and Remote Sensing (Bao et al., 2026; doi: 10.3389/fenvs.2026.1725258). This data package includes the drone photos for a section of Umtanum Creek in Washington, Unted States. The photos were used to reconstruct the 3-dimensional (3D) digital elevation model (DEM) of the riverbed for the investigated stream section. The reconstruction results from four approaches are provided: (1) unoccupied aerial vehicle (UAV, colloquially known as drone) imagery-based Structure-from-Motion (SfM), (2) a machine learning-based 3D reconstruction model, Visual Geometry Grounded Deep Structure from Motion (VGGSfM), (3) Visual Geometry Grounded Transformer for long sequence of images (VGGT-Long), and (4) handheld smartphone LiDAR scanning. The ground truth measurements by tripod-mounted optical level kit and ground control points GPS locations for evaluating the accuracy of the four reconstruction approaches are also provided in this data package. A preliminary version of this data package was published in October 2025 at the time of manuscript submission. It was updated in March 2026, at the time of manuscript acceptance, to include additional metadata (this readme, data dictionary, and file level metadata). The data did not change. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) 8 folders; (2) the detailed flight configuration html files; (3) field metadata; (4) a readme; (5) a data dictionary; and (6) file-level metadata. The folders “2024_10_18_d01” and “2024_10_18_d02” contain the original drone photos for the two drone flights (d01 and d02) on October 18, 2024. The reconstruction results from each of the approaches are in the folders called “ODM_SfM”, “VGGSfM”, “VGGTLong”, and “LiDAR”. The ground truth measurements are in the folder called “optical_level_kit”. Lastly, results comparing the different approaches are in the folder called “comparisons”. All files are .csv, .html, .jpg, .obj, .txt, and .npy. For information on using the .obj and .npy files, see the readme files within the same folder as the files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Electronic and Structural Properties of the Polymer-Electrolyte Interphase in Electrochemically Doped Polymers

The advance of soft, polymer-based (photo)electrochemical energy transformation and storage applications requires a framework for polymer and electrolyte design that includes a deep understanding of the polymer–electrolyte interphase. Here, we report on the investigations of two napthalenediimide (NDI)–bithiophene (T2)-based semiconducting copolymers using computational modeling and in situ, ex situ, and operando techniques to reveal how changes in electrolyte and polymer chemistry modulate the electronic and structural properties of polymer electrodes during electrochemical (de)doping. These systems are shown to host an ensemble of polarons, in contrast with the single polaron-like character often reported, whose properties vary with the nature of the local environment. Importantly, these polarons serve as reporters of the nanoscale environments in which they reside. We demonstrate that controlling the polymer and electrolyte chemistry regulates the nature of the charge carriers generated upon electrochemical doping and/or exciton dissociation in a photoelectrochemical solar cell: For instance, divalent counterions enable polaron and bipolaron formation at lower reducing potentials, while supporting more bipolaron formation than monovalent counterions. A novel application of NEXAFS reveals insights into charge (de)localization, providing a pathway for future investigation of electron transport mechanisms. Finally, simulations of polymer swelling of an amorphous interphase show that charge formation has a large impact on polymer swelling and ion penetration. These studies deliver insights to enable the control of charge-carrier and ion transport, the rates of electron transfer and catalytic efficiency, device stability, and overall device performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗
DOE OSTI2024

Analytical ab initio hessian from a deep learning potential for transition state optimization

Identifying transition states—saddle points on the potential energy surface connecting reactant and product minima—is central to predicting kinetic barriers and understanding chemical reaction mechanisms. In this work, we train a fully differentiable equivariant neural network potential, NewtonNet, on thousands of organic reactions and derive the analytical Hessians. By reducing the computational cost by several orders of magnitude relative to the density functional theory (DFT) ab initio source, we can afford to use the learned Hessians at every step for the saddle point optimizations. We show that the full machine learned (ML) Hessian robustly finds the transition states of 240 unseen organic reactions, even when the quality of the initial guess structures are degraded, while reducing the number of optimization steps to convergence by 2–3× compared to the quasi-Newton DFT and ML methods. All data generation, NewtonNet model, and ML transition state finding methods are available in an automated workflow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2017

Space Launch System Spacecraft and Payload Elements: Progress Toward Crewed Launch and Beyond

While significant and substantial progress continues to be accomplished toward readying the Space Launch System (SLS) rocket for its first test flight, work is already underway on preparations for the second flight - using an upgraded version of the vehicle - and beyond. Designed to support human missions into deep space, SLS is the most powerful human-rated launch vehicle the United States has ever undertaken, and is one of three programs being managed by the National Aeronautics and Space Administration's (NASA's) Exploration Systems Development division. The Orion spacecraft program is developing a new crew vehicle that will support human missions beyond low Earth orbit (LEO), and the Ground Systems Development and Operations (GSDO) program is transforming Kennedy Space Center (KSC) into a next-generation spaceport capable of supporting not only SLS but also multiple commercial users. Together, these systems will support human exploration missions into the proving ground of cislunar space and ultimately to Mars. For its first flight, SLS will deliver a near-term heavy-lift capability for the nation with its 70-metric-ton (t) Block 1 configuration. Each element of the vehicle now has flight hardware in production in support of the initial flight of the SLS, which will propel Orion around the moon and back. Encompassing hardware qualification, structural testing to validate hardware compliance and analytical modeling, progress is on track to meet the initial targeted launch date. In Utah and Mississippi, booster and engine testing are verifying upgrades made to proven shuttle hardware. At Michoud Assembly Facility (MAF) in Louisiana, the world's largest spacecraft welding tool is producing tanks for the SLS core stage. Providing the Orion crew capsule/launch vehicle interface and in-space propulsion via a cryogenic upper stage, the Spacecraft/Payload Integration and Evolution (SPIE) element serves a key role in achieving SLS goals and objectives. The SPIE element marked a major milestone in 2014 with the first flight of original SLS hardware, the Orion Stage Adapter (OSA) which was used on Exploration Flight Test-1 with a design that will be used again on the first flight of SLS. The element has overseen production of the Interim Cryogenic Propulsion Stage (ICPS), an in-space stage derived from the Delta Cryogenic Second Stage, which was manufactured at United Launch Alliance (ULA) in Decatur, Alabama, prior to being shipped to Florida for flight preparations. Manufacture of the OSA and the Launch Vehicle Stage Adapter (LVSA) took place at the Friction Stir Facility located at Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Marshall is also home to the Integrated Structural Test of the ICPS, LVSA, and OSA, subjecting the stacked components to simulated stresses of launch. The SPIE Element is also overseeing integration of 13 "CubeSat" secondary payloads that will fly on the first flight of SLS, providing access to deep space regions in a way currently not available to the science community. At the same time as this preparation work is taking place toward the first launch of SLS, however, the Space Launch System Program is actively working toward its second launch. For its second flight, SLS will be upgraded to the more-capable Block 1B configuration. While the Block 1 configuration is capable of delivering more than 70 t to LEO, the Block 1B vehicle will increase that capability to 105 t. For that flight, the new configuration introduces two major new elements to the vehicle - an Exploration Upper Stage (EUS) that will be used for both ascent and in-space propulsion, and a Universal Stage Adapter (USA) that serves as a "payload bay" for the rocket, allowing the launch of large exploration systems along with the Orion spacecraft. Already, flight hardware is being prepared for the Block 1B vehicle. Welding is taking place on the second rocket's core stage. Flight hardware production has begun on booster components. An RS-25 engine slated for that flight has been tested. Development work is taking place on the EUS, with contracts in place for both the stage and the RL10 engines which will power it. (The EUS will use four RL10 engines, an increase from one on the ICPS.) For the crew configuration of the Block 1B vehicle, the SLS SPIE element is managing the USA and accompanying Payload Adapter, which will accommodate both large payloads co-manifested with Orion and small-satellite secondary payloads. This co-manifested payload capacity will be instrumental for missions into the proving ground around the moon, where NASA will test new systems and demonstrate new capabilities needed for human exploration farther into deep space.

Schorr, Andrew A.↗
NASA NTRS2017

Space Launch System Spacecraft and Payload Elements: Progress Toward Crewed Launch and Beyond

While significant and substantial progress continues to be accomplished toward readying the Space Launch System (SLS) rocket for its first test flight, work is already also underway on preparations for the second flight - using an upgraded version of the vehicle - and beyond. Designed to support human missions into deep space, Space Launch System (SLS), is the most powerful human-rated launch vehicle the United States has ever undertaken, and is one of three programs being managed by the National Aeronautics and Space Administration's (NASA's) Exploration Systems Development division. The Orion spacecraft program is developing a new crew vehicle that will support human missions beyond low Earth orbit (LEO), and the Ground Systems Development and Operations program is transforming Kennedy Space Center into a next-generation spaceport capable of supporting not only SLS but also multiple commercial users. Together, these systems will support human exploration missions into the proving ground of cislunar space and ultimately to Mars. For its first flight, SLS will deliver a near-term heavy-lift capability for the nation with its 70-metric-ton (t) Block 1 configuration. Each element of the vehicle now has flight hardware in production in support of the initial flight of the SLS, which will propel Orion around the moon and back. Encompassing hardware qualification, structural testing to validate hardware compliance and analytical modeling, progress in on track to meet the initial targeted launch date. In Utah and Mississippi, booster and engine testing are verifying upgrades made to proven shuttle hardware. At Michoud Assembly Facility in Louisiana, the world's largest spacecraft welding tool is producing tanks for the SLS core stage. Providing the Orion crew capsule/launch vehicle interface and in-space propulsion via a cryogenic upper stage, the Spacecraft/Payload Integration and Evolution (SPIE) element serves a key role in achieving SLS goals and objectives. The SPIE element marked a major milestone in 2014 with the first flight of original SLS hardware, the Orion Stage Adapter (OSA) which was used on Exploration Flight Test-1 with a design that will be used again on the first flight of SLS. The element has overseen production of the Interim Cryogenic Propulsion Stage (ICPS), an in-space stage derived from the Delta Cryogenic Second Stage, which was manufactured at United Launch Alliance in Decatur, Alabama, prior to being shipped to Florida for flight preparations. Manufacture of the Orion Stage Adapter and the Launch Vehicle Stage Adapter (LVSA) took place at the Friction Stir Facility located at Marshall Space Flight Center in Huntsville, Alabama. Marshall is also home to the Integrated Structural Test of the ICPS, LVSA, and OSA, subjecting the stacked components to simulated stresses of launch. The SPIE Element is also overseeing integration of 13 "CubeSat" secondary payloads that will fly on the first flight of SLS, providing access to deep space regions in a way currently not available to the science community. At the same time as this preparation work is taking place toward the first launch of SLS, however, the Space Launch System Program is actively working toward its second launch. For its second flight, SLS will be upgraded to the more-capable Block 1B configuration. While the Block 1 configuration is capable of delivering more than 70 metric tons to low Earth orbit, the Block 1B vehicle will increase that capability to 105 t. For that flight, the new configuration introduces two major new elements to the vehicle - an Exploration Upper Stage (EUS) that will be used for both ascent and in-space propulsion, and a Universal Stage Adapter (USA) that serves as a "payload bay" for the rocket, allowing the launch of large exploration systems along with the Orion spacecraft. Already, flight hardware is being prepared for the Block 1B vehicle. Welding is taking place on the second rocket's core stage. Flight hardware production has begun on booster components. An RS-25 engine slated for that flight has been tested. Development work is taking place on the Exploration Upper Stage, with contracts in place for both the stage and the RL10 engines which will power it. (The EUS will use four RL10 engines, an increase from one on the ICPS.) For the crew configuration of the Block 1B vehicle, the SLS SPIE element is managing the USA and accompanying Payload Adapter, which will accommodate both large payloads co-manifested with Orion and small-satellite secondary payloads. This co-manifested payload capacity will be instrumental for missions into the Proving Ground around the moon, where NASA will test new systems and demonstrate new capabilities needed for human exploration farther into deep space.

Schorr, Andrew A.↗
NASA NTRS1987

A coupled dynamic-thermodynamic model of an ice-ocean system in the marginal ice zone

Thermodynamics are incorporated into a coupled ice-ocean model in order to investigate wind-driven ice-ocean processes in the marginal zone. Upswelling at the ice edge which is generated by the difference in the ice-air and air-water surface stresses is found to give rise to a strong entrainment by drawing the pycnocline closer to the surface. Entrainment is shown to be negligible outside the areas affected by the ice edge upswelling. If cooling at the top is included in the model, the heat and salt exchanges are further enhanced in the upswelling areas. It is noted that new ice formation occurs in the region not affected by ice edge upswelling, and it is suggested that the high-salinity mixed layer regions (with a scale of a few Rossby radii of deformation) will overturn due to cooling, possibly contributing to the formation of deep water.

Hakkinen, Sirpa↗
NASA NTRS1998

Mission Operations with an Autonomous Agent

The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DSI), the first deep space mission of the NASA's New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which PA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, such as parameter updates, model updates, and diagnostic information, without interfering with the activities of the RA or leaving the system in an inconsistent state. The resulting architecture supports incremental autonomy, in which a basic agent can be delivered early and then used in an increasingly autonomous manner over the lifetime of the mission. It also supports variable autonomy, as it enables ground operators to benefit from autonomy when L'@ey want it, but does not inhibit them from obtaining a detailed understanding and exercising tighter control when necessary. These issues are critical to the successful development and operation of autonomous spacecraft.

Pell, Barney↗
NASA NTRS2024

A Thermally-Conductive Martian Core and Implications for Its Dynamo Cessation

Mars experienced a dynamo process that generated a global magnetic field ~4.3 (or earlier) to 3.6 billion years ago (Ga). The cessation of this dynamo strongly affected Mars’ history and is expected to be linked to thermochemical evolution of Mars’ iron-rich liquid core, which is strongly influenced by its thermal conductivity. Here, we directly measured thermal conductivities of solid iron-sulfur alloys to pressures relevant to the Martian core and temperatures to 1023 Kelvin. Our results show that a Martian core with 16 weight % sulfur has a thermal conductivity of ~19 to 32 Watt meter −1 Kelvin −1 from its top to the center, much higher than previously inferred from electrical resistivity measurements. Our modeled thermal conductivity profile throughout the Martian deep-mantle and core indicates a ~4- to 6-fold discontinuity across the core-mantle boundary. The core’s efficient cooling resulting from the depth-dependent, high conductivity diminishes thermal convection and forms thermal stratification, substantially contributing to cessation of Martian dynamo.

Wen-Pin Hsieh↗
NASA NTRS1993

A new technology for manufacturing scheduling derived from space system operations

A new technology for producing finite capacity schedules has been developed in response to complex requirements for operating space systems such as the Space Shuttle, the Space Station, and the Deep Space Network for telecommunications. This technology has proven its effectiveness in manufacturing environments where popular scheduling techniques associated with Materials Resources Planning (MRPII) and with factory simulation are not adequate for shop-floor work planning and control. The technology has three components. The first is a set of data structures that accommodate an extremely general description of a factory's resources, its manufacturing activities, and the constraints imposed by the environment. The second component is a language and set of software utilities that enable a rapid synthesis of functional capabilities. The third component is an algorithmic architecture called the Five Ruleset Model which accommodates the unique needs of each factory. Using the new technology, systems can model activities that generate, consume, and/or obligate resources. This allows work-in-process (WIP) to be generated and used; it permits constraints to be imposed or intermediate as well as finished goods inventories. It is also possible to match as closely as possible both the current factory state and future conditions such as promise dates. Schedule revisions can be accommodated without impacting the entire production schedule. Applications have been successful in both discrete and process manufacturing environments. The availability of a high-quality finite capacity production planning capability enhances the data management capabilities of MRP II systems. These schedules can be integrated with shop-floor data collection systems and accounting systems. Using the new technology, semi-custom systems can be developed at costs that are comparable to products that do not have equivalent functional capabilities and/or extensibility.

Hornstein, R. S.↗
NASA NTRS2007

Using High Frequency Passive Microwave, A-train, and TRMM Data to Evaluate Hydrometer Structure in the NASA GEOS-5 Data Assimilation System

Validating water vapor and prognostic condensate in global models remains a challenging research task. Model parameterizations are still subject to a large number of tunable parameters; furthermore, accurate and representative in situ observations are very sparse, and satellite observations historically have significant quantitative uncertainties. Progress on improving cloud / hydrometeor fields in models stands to benefit greatly from the growing inventory ofA-Train data sets. ill the present study we are using a variety of complementary satellite retrievals of hydrometeors to examine condensate produced by the emerging NASA Modem Era Retrospective Analysis for Research and Applications, MERRA, and its associated atmospheric general circulation model GEOS5. Cloud and precipitation are generated by both grid-scale prognostic equations and by the Relaxed Arakawa-Schubert (RAS) diagnostic convective parameterization. The high frequency channels (89 to 183.3 GHz) from AMSU-B and MRS on NOAA polar orbiting satellites are being used to evaluate the climatology and variability of precipitating ice from tropical convective anvils. Vertical hydrometeor structure from the Tropical Rainfall Measuring Mission (TRMM) and CloudSat radars are used to develop statistics on vertical hydrometeor structure in order to better interpret the extensive high frequency passive microwave climatology. Cloud liquid and ice water path data retrieved from the Moderate Resolution Imaging Spectroradiometer, MODIS, are used to investigate relationships between upper level cloudiness and tropical deep convective anvils. Together these data are used to evaluate cloud / ice water path, gross aspects of vertical hydrometeor structure, and the relationship between cloud extent and surface precipitation that the MERRA reanalysis must capture.

Robertson, Franklin↗
NASA NTRS2023

Predicting Two-Dimensional Airfoil Performance Using Graph Neural Networks

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 (C L ), drag (C d , C dp ), moment (C m ), and node-specific quantities such as coefficient of pressure (C p ). 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.

machine learning↗