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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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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Plasma membrane ordering agent pluronic F-68 (PF-68) reduces neurotransmitter uptake and release and produces learning and memory deficits in rats

A substantial body of evidence indicates that aged-related changes in the fluidity and lipid composition of the plasma membrane contribute to cellular dysfunction in humans and other mammalian species. In the CNS, reductions in neuronal plasma membrane order (PMO) (i.e., increased plasma membrane fluidity) have been attributed to age as well as the presence of the beta-amyloid peptide-25-35, known to play an important role in the neuropathology of Alzheimer's disease (AD). These PMO increases may influence neurotransmitter synthesis, receptor binding, and second messenger systems as well as signal transduction pathways. The effects of neuronal PMO on learning and memory processes have not been adequately investigated, however. Based on the hypothesis that an increase in PMO may alter a number of aspects of synaptic transmission, we investigated several neurochemical and behavioral effects of the membrane ordering agent, PF-68. In cell culture, PF-68 (nmoles/mg SDS extractable protein) reduced [3H]norepinephrine (NE) uptake into differentiated PC-12 cells as well as reduced nicotine stimulated [3H]NE release. The compound (800-2400 microg/kg, i.p., resulting in nmoles/mg SDS extractable protein in the brain) decreased step-through latencies and increased the frequencies of crossing into the unsafe side of the chamber in inhibitory avoidance training. In the Morris water maze, PF-68 increased the latencies and swim distances required to locate a hidden platform and reduced the time spent and distance swam in the previous target quadrant during transfer (probe) trials. PF-68 did not impair performance of a well-learned working memory task, the rat delayed stimulus discrimination task (DSDT), however. Studies with 14C-labeled PF-68 indicated that significant (pmoles/mg wet tissue) levels of the compound entered the brain from peripheral (i.p.) injection. No PF-68 related changes were observed in swim speeds or in visual acuity tests in water maze experiments, rotorod performance, or in tests of general locomotor activity. Furthermore, latencies to select a lever in the DSDT were not affected. These results suggest that PF-68 induced deficits in learning and memory without confounding peripheral motor, sensory, or motivational effects at the tested doses. Furthermore, none of the doses induced a conditioned taste aversion to a novel 0.1% saccharin solution indicating a lack of nausea or gastrointestinal malaise induced by the compound. The data indicate that increases in neuronal plasma membrane order may have significant effects on neurotransmitter function as well as learning and memory processes. Furthermore, compounds such as PF-68 may also offer novel tools for studying the role of neuronal PMO in mnemonic processes and changes in PMO resulting from age-related disorders such as AD.

Poloxamer/pharmacology↗
NASA NTRSDate not supplied

LAADS DAAC Migrates to the Cloud: Lessons Learned from Communicating About Earth Science Data on the Cloud

The Level-1 and Atmosphere Archive Distribution System (LAADS) Distributed Active Archive Center (DAAC) is migrating data to the cloud. As one of twelve DAACS supported by NASA’s Earth Science Data and Information System (ESDIS), LAADS is using moving away from on premise data storage facilities to migrating to Amazon Web Services, where the massive archive of data from the Moderate Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) will be available for download and post-processing transformations online. The migration is happening in three phases and LAADS is concluding its beta testing period. This poster shows the lessons learned from communicating with a select group of users about how to effectively educate data users on using data in the cloud.

Tassia Owen↗
NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗
NASA NTRS2001

Lessons Learned (3 Years of H2O2 Propulsion System Testing Efforts at NASA's John C. Stennis Space Center)

John C. Stennis Space Center continues to support the Propulsion community in an effort to validate High-Test Peroxide as an alternative to existing/future oxidizers. This continued volume of peroxide test/handling activity at Stennis Space Center (SSC) provides numerous opportunities for the SSC team to build upon previously documented 'lessons learned'. SSC shall continue to strive to document their experience and findings as H2O2 issues surface. This paper is intended to capture all significant peroxide issues that we have learned over the last three years. This data (lessons learned) has been formulated from practical handling, usage, storage, operations, and initial development/design of our systems/facility viewpoint. The paper is intended to be an information type tool and limited in technical rational; therefore, presenting the peroxide community with some issues to think about as the continued interest in peroxide evolves and more facilities/hardware are built. These lessons learned are intended to assist industry in mitigating problems and identifying potential pitfalls when dealing with the requirements for handling high-test peroxide.

Taylor, Gary O.↗
DOE OSTI2024

Machine Learning Classification Strategy to Improve Streamflow Estimates in Diverse River Basins in the Colorado River Basin

Streamflow in the Colorado River Basin (CRB) is significantly altered by human activities including land use/cover alterations, reservoir operation, irrigation, and water exports. Climate is also highly varied across the CRB which contains snowpack-dominated watersheds and arid, precipitation-dominated basins. Recently, machine learning methods have improved the generalizability and accuracy of streamflow models. Previous successes with LSTM modeling have primarily focused on unimpacted basins, and few studies have included human impacted systems in either regional or single-basin modeling. We demonstrate that the diverse hydrological behavior of river basins in the CRB are too difficult to model with a single, regional model. We propose a method to delineate catchments into categories based on the level of predictability, hydrological characteristics, and the level of human influence. Lastly, we model streamflow in each category with climate and anthropogenic proxy data sets and use feature importance methods to assess whether model performance improves with additional relevant data. Overall, land use cover data at a low temporal resolution was not sufficient to capture the irregular patterns of reservoir releases, demonstrating the importance of having high-resolution reservoir release data sets at a global scale. On the other hand, the classification approach reduced the complexity of the data and has the potential to improve streamflow forecasts in human-altered regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Unraveling Hydrogen Induced Geochemical Reaction Mechanisms through Coupled Geochemical Modeling and Machine Learning

Underground hydrogen storage (UHS) provides a promising large-scale, long-term energy storage solution. A reasonable recovery of stored hydrogen is critical for a successful storage scheme. However, in subsurface reservoirs hydrogen is subject to active geochemical reactions that might result in hydrogen loss. In this study, we implemented a geochemical modeling approach coupled with an unsupervised machine learning technique called non-negative matrix factorization (NMF) to unravel the complex brine-rock-H 2 geochemical processes responsible for hydrogen losses, with particular focus on sulfate reduction reactions. NMF is applied to modeled mineral evolution and fluid component profiles to retrieve profiles that can be interpreted to more easily assess competing processes. NMF decouples simulated competing equilibrium reactions. This facilitates separation of overlapping reaction profiles from redox processes, dissolution fronts, and secondary precipitation while considering the effects of simulation parameters such as salinity, temperature, and total H 2 pressure. NMF successfully discriminates these competing effects in nonlinear ways, allowing robust interpretation. In addition, NMF reveals subtle coupled mineral associations and reaction fronts that are invisible to conventional model analysis. This integrated approach strengthens the conceptual understanding of complex nonlinear hydrogen-brine-rock interactions and advances geochemical research on UHS systems to resolve complexities in modeled geochemical systems without the need for direct experiments or prior knowledge. Furthermore, this study highlights the efficacy of combining geochemical modeling with machine learning techniques to enhance the interpretability of the intricate geochemical simulation output through deciphering the overlapping reaction path that cannot be achieved only using conventional analysis of geochemical models alone.

08 HYDROGEN↗
NASA NTRS1988

Igneous activity in the early solar system

Although the main emphasis of this book is on what can be learned about the early solar system from material that has escaped secondary processing, the study of differentiated meteorites can provide unique insights into the processes of basalt generation and core formation. Such processes would have been of fundamental importance during the evolution of planetary objects, including the terrestrial planets, early in solar-system history. The properties of igneous meteorites are studied with attention given to the howardite-eucrite-diogenite (HED) suite. Geochemical and petrologic trends in those meteorites are discussed with the objective of defining the thermal and chemical evolution of the HED parent body. A major issue is the nature of the dominant source of heat in the early solar system.

Hewins, R. H.↗
NASA NTRS2005

Learning Effectiveness of the NASA Digital Learning Network

Student participation in actual investigations which develop inquiry and intellectual skills has long been regarded as an essential component of science instructions (Schwab, 1962; White, 1999). Such investigations give students an opportunity to appreciate the spirit of science and promote an understanding of the nature of science. However, classroom research conducted over the past 20 years describes science teaching as primarily teacher centered. Typical instruction consists of whole class, noninteractive activities in which individual seatwork has constituted the bulk of classroom interactions (Tobin and Gallagher, 1997). Students typically learn science from textbooks and lectures. Their main motivation is to do reasonably well on tests and examinations (Layman, 1999). During the past five years, infrastructure constraints have reduced to the point that many schools systems can now afford low cost, high quality video conferencing equipment (International Society for Technology in Education, 2003). This study investigates the use of interactive video conferencing vs. face to face interaction with hands-on, inquiry based activities. Some basic questions to be addressed are: How does the delivery method impact the students understanding of the goals of the experiment? Are students explanation of the strategies of experimentation different based on the method of instruction that was provided. Do students engaged in a workshop with the instructor in the room vs. an instructor over video conferencing have different perception of the understanding of the subject materials?

Hix, Billy↗
DOE OSTI2025

Machine learning guided selection of broad-spectrum epitope-specific functional antibodies for "Disease X"

Our project established and demonstrated a transfer learning framework that enables prediction of antibody–antigen interactions across related viruses. The approach focused on three major activities: 1. Conserved region and epitope identification – We compared viral protein structures and sequences to identify shared receptor-binding domains and neutralizing epitope regions across variants and related viruses. These conserved features formed the foundation for discovering broadly functional antibodies. 2. Machine learning model development – We built neural network–based models that integrate epitope features with antibody sequence information. Instead of relying solely on structural or physical properties, the models learned transferable patterns that describe antibody binding potential across different viral families. 3. Transfer learning and validation – Using SARS-CoV-2 and Ebola as source systems, we successfully transferred learned epitope features to predict antibody interactions for SARS CoV-1 and Marburg virus. Iterative cycles of dataset generation, retraining, and evaluation improved generalization and predictive power, ensuring the framework can adapt to new threats.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

A Preliminary Assessment of Physical Demand during Simulated Lunar Surface Extravehicular Activities

Returning to the moon requires many advances in current space technology. One major aspect of this development is a new exploration spacesuit (xEMU). Taking lessons learned from Apollo era suitsand the Extravehicular Mobility Unit (EMU) used on the International Space Station (ISS), xEMU will have increased mobility, dust mitigation, headspace, glove fit, and life support capabilities. Artemis astronauts in xEMU will complete a far more rigorous Extravehicular Activity (EVA) schedule than Apolloand ISS. Notably, metabolic rates during Apollo lunar EVA tasks were observed to be up to 50% lower than similar tasks performed in a ground analog environment under simulated partial gravity with newer suits. Therefore, understanding the physical demands of lunar surface exploration operations is criticalto ensuring best outcomes operating within the constraints of xEMU and planning for exploration EVA activities. This study utilized the Active Response Gravity Offload System (ARGOS) to simulate the lunar environment and continuously offload subjects to lunar gravity. Two male subjects completed two days of EVAs wearing the pressurized Mark III spacesuit, completing suit fit and mobility checks, as well as simulated lander operations, cable routing, crew rescue, geology, payload relocation, and traverse tasks in an end-to-end EVA (E2E) task block and standalone (SA) task blocks. We recorded continuous values of metabolic rate (MR) and heart rate (HR) to assess physical demand. During the E2E task block, subjects did not rest between tasks to simulate continuous effort from task to task, as in real EVAs. In comparison, subjects had a 5-minute break after each SA task block to allow for the metabolic rate and heart rate to return to baseline.MR values were categorized as low (≤ 700 BTU/hr), medium (700-1000 BTU/HR), and high (≥ 1000 BTU/hr), while HR values were categorized as low (≤150) and high (>150). During the 16 tasks in the E2E block, subjects averaged low MR in 6% of tasks, medium MR in 47% of tasks, and high MR in 47% of tasks. While MR was consistent between subjects, Subject 1 averaged low HR for 100% of these tasks, while Subject 2 averaged low HR in 44% of tasks. During the 23 tasks in the SA task blocks, subjects averaged low MR in 26% of tasks, medium MR in 52% of tasks, and high MR in 22% of tasks. Again, HR was different between subjects, with subject 1 averaging low HR in 100% of these tasks while subject 2 averaged low HR in 70%. Across all tasks in this study, subjects reached maximum MR and HR values during a 500m traverse at 30% grade in the E2E block (subject 1: 1747 BTU/hr, 150 BPM; subject 2: 1656 BTU/hr, 177 BPM).Understanding the physical demand to complete exploration EVA tasks will be instrumental to the future success of exploration spacesuit designs and missions. Further work in this study will be needed to characterize MR during exploration EVA tasks, including expanding the subject pool and testing new suit designs.

Taylor E Schlotman↗
NASA NTRS2018

Using Machine-Learning Methods and Expert Prediction Probabilities to Forecast Solar Flares

It has long been known that studying connection between solar flares and properties of magnetic field in active regions is very important for understanding the flare physics and developing space weather forecasts. The Helioseismic and Magnetic Imager onboard the Solar Dynamics Observatory (SDO/HMI) obtains tremendous amounts of magnetic field data products. However the operational NOAA Space Weather Prediction Center (SWPC) forecasts of solar flares still represent prediction probabilities issued by the experts. In this research we investigate the possibilities to enhance the daily operational flare forecasts performed at the SWPC by developing a synergy of the expert predictions and physics-based criteria, and by employing machine-learning methods. Among the physics-based criteria we consider the descriptors of the Polarity Inversion Line (PIL) and Space weather HMI Active Region Patches (SHARP), and derive from them daily characteristics of the entire Sun. We also consider the daily descriptors of the GOES Soft X-Ray (SXR) 1-8 Angstroms flux such as the flare history of the previous days and averaged X-Ray flux. We estimate the effectiveness in separation of flaring and non-flaring cases for each characteristic, as well as for the expert prediction probabilities, and find that some PIL, SHARP and SXR descriptors are as effective as the expert prediction probabilities and should be considered to issue the flare forecast. Finally, we train and test several Machine-Learning classification algorithms (Support Vector Classifiers with various kernel functions, k-Nearest Neighbor Classifier, Random Forest Classifier, and Neural Networks) using the most effective descriptors and expert prediction probabilities, and compare the obtained predictions with the current SWPC forecasts.

Machine-Learning↗
DOE OSTI2025

Leveraging High-throughput Computation and Machine Learning to Discover and Understand Low-Temperature Fast Oxygen Conductors (Final Technical Report)

The major goals of this work are twofold: (1) to enable transformative basic understanding of structure-property-performance relationships governing oxygen transport in oxygen-active materials and (2) facilitate the discovery and rational design of new oxygen-active materials which transport oxygen efficiently at low temperature. Transformative understanding and materials design will be accomplished by synergistically combining materials data mining, machine learning, high-throughput computation and targeted experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

MSU IETC LSTM Ethernet Decode (AN EDGE)

This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.

Full Duplex Signals↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2025

DoE as a “Digital Innovation” Sponsor of the WCRP OSC2023 (Final Report)

The WCRP Open Science Conference (https://wcrp-osc2023.org/) was a once-in-a-decade opportunity to jointly explore the transformative actions urgently needed to ensure a sustainable future. Held in Kigali, Rwanda on October 23 -27, 2023, it showcased advances in climate science, helped identify gaps and opportunities, and provided a forum for communities to jointly develop future activities. Scientists, practitioners, politicians, policy makers, intergovernmental agencies and NGOs showcased their work, learned from each other, and explored new ways to work together.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Technology advancements for servicing of future spacecraft systems

Problems associated with the in-orbit repair and maintenance of spacecraft systems are examined with reference to experience gained from three servicing missions: in-orbit capture, repair, and reflight of the Solar Maximum Mission satellite, capture and return to earth of the Palapa and Westar communications satellites, and in-orbit repair of Syncom 3. It is then shown how the lessons learned from the three servicing missions are applied to current and future servicing activities. In particular, planned servicing missions for the Hubble Space Telescope, tools and servicing facilities development, and the development of the Explorer Platform are discussed.

Cepollina, F. J.↗
NASA NTRS1989

Evaluation and analysis of the orbital maneuvering vehicle video system

The work accomplished in the summer of 1989 in association with the NASA/ASEE Summer Faculty Research Fellowship Program at Marshall Space Flight Center is summarized. The task involved study of the Orbital Maneuvering Vehicle (OMV) Video Compression Scheme. This included such activities as reviewing the expected scenes to be compressed by the flight vehicle, learning the error characteristics of the communication channel, monitoring the CLASS tests, and assisting in development of test procedures and interface hardware for the bit error rate lab being developed at MSFC to test the VCU/VRU. Numerous comments and suggestions were made during the course of the fellowship period regarding the design and testing of the OMV Video System. Unfortunately from a technical point of view, the program appears at this point in time to be trouble from an expense prospective and is in fact in danger of being scaled back, if not cancelled altogether. This makes technical improvements prohibitive and cost-reduction measures necessary. Fortunately some cost-reduction possibilities and some significant technical improvements that should cost very little were identified.

Moorhead, Robert J., II↗