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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 NTRS1989

NASA flight cell and battery issues

The author presents the important battery and cell problems, encompassing both test failures and accidents, which were encountered during the past year. Practical issues facing programs, which have to be considered in the development of a battery program strategy, are addressed. The problems of one program, the GRO (Gamma Ray Observatory), during the past year are focused on to illustrate the fundamental types of battery problems that occur. Problems encountered by other programs are briefly mentioned to complete the accounting. Two major categories of issues are defined, namely, whose which are quality and design related, i.e., problems having inherent manufacturing-process-related aspects with an impact on cell reliability, and these which are accident triggered or man induced, i.e., those operational issues having an impact on battery and cell reliability.

Schulze, N. R.↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗
NASA NTRS2026

Mechanical Properties of Carbon Fiber Reinforced Composites Exposed to Cryogenic Conditions and Space Radiation via Simulation and Testing

As NASA missions extend beyond low Earth orbit, increasing reliance is placed on carbon fiber reinforced polymer (CFRP) composites for spacecraft structures where mass efficiency, durability, and long-term reliability are critical. In service, these materials are subjected to a combination of ultraviolet radiation, vacuum, ionizing radiation, atomic oxygen, and extreme thermal excursions under sustained mechanical loading. Flight systems such as the Boeing Starliner and SpaceX Dragon employ external composite structures that will experience these environments for extended durations. Although prior spaceflight and ground studies have reported limited changes in bulk mechanical properties, the synergistic effects of these environments on composite microstructure, particularly at the fiber matrix interphase, remain insufficiently characterized and represent a potential qualification and reliability risk. This study investigates the effects of short-term cryogenic exposure on a radiation shielding carbon epoxy composite, SC2020, as a ground-based analog for space relevant thermal extremes. The SC2020 material system has previously flown on the International Space Station under the Materials International Space Station Experiment (MISSE) program. Composite specimens were exposed to liquid nitrogen for 6 and 24 hours and evaluated using a multiscale characterization framework that combined ASTM D3039 tensile testing, Atomic Force Microscopy (AFM) based interphase analysis, and helium gas permeability measurements. Tensile testing showed no statistically significant or permanent degradation in global strength or modulus following cryogenic exposure. In contrast, AFM measurements revealed reductions in interphase modulus, weakened adhesion, and increased nanoscale heterogeneity, indicating localized degradation mechanisms not captured by conventional bulk testing. Gas permeability measurements showed a progressive increase in helium diffusion with exposure duration, consistent with micro-void formation or partial interfacial debonding. The results indicate that cryogenic exposure initiates degradation at the fiber matrix interphase while leaving global mechanical properties largely unchanged over short durations. These findings underscore the importance of multiscale diagnostics for identifying early-stage damage mechanisms that may influence long term performance and qualification margins for spaceflight composite structures. The data presented establish a cryogenic baseline for comparison with forthcoming MISSE flight exposure results and support ongoing NASA Established Program to Stimulate Competitive Research (EPSCoR) efforts aimed at improving composite qualification methodologies, risk assessment, and reliability prediction for space environments.

composite reliability↗
NASA NTRS2026

List of Commercial and Advanced Developmental Niobium-Based Alloys of the Space Age

This report compiles a list of commercial and developmental niobium-based alloys developed during the Space Age (late 1950s through mid-1970s) for extreme-temperature applications, including rocket engine thrust chambers, hypersonic re-entry thermal protection systems, and space fission reactor loops. Niobium (Nb) was widely pursued because it provided the lowest density (~8.6 g/cc) among the primary refractory metals, a high melting temperature (~2470°C), exceptional low-temperature ductility, good formability, and compatibility with liquid alkali metals. An evaluation of physical metallurgy mechanisms, focusing on solid-solution strengthening via heavy refractory solutes (W, Mo, Ta), dual-purpose reactive solutes (Hf, Zr, Ti), and dispersion strengthening using carbides, nitrides, and oxides is presented. Additionally, the report compares Western and Soviet Union metallurgical approaches, explaining how supply chain factors and manufacturing infrastructure influenced element selection, interstitial chemistry, and alloy identification/naming conventions. Cataloging these historical alloy chemical compositions serves as a foundational reference for modern alloy additive manufacturing, thermodynamic CALPHAD modeling, and machine-learning discovery pipelines for next-generation extreme-environment niobium-based alloys.

Physical Metallurgy↗
NASA NTRSDate not supplied

Qualification Progress and Mission-Enabling Capabilities of the 12-kW Hall-Effect, Advanced Electric Propulsion System (AEPS) Thruster

An update of the 12 kW Advanced Electric Propulsion System (AEPS) qualification and flight thruster status is provided. Three flight thrusters completed acceptance testing in 2025 and have been delivered to NASA. Environmental qualification is complete and preparations are under way for long-duration life-demonstration testing. A brief overview of three of SEP’s programmatic lessons learned are discussed: risk management, contract oversight, and requirement definition. In addition, the extensibility of the AEPS thruster to enable a variety of NASA missions is presented.

Mars Exploration↗
DOE OSTI2026

Hazard and risk analysis framework for nuclear power plant–based integrated energy systems

Employing integrated energy systems (IESs) with nuclear power plants (NPPs) can improve NPP utilization by leveraging dedicated thermal and electric power delivery, but it may also increase operational safety risks. This paper presents a framework to identify and quantify hazards and risks for such IESs. The framework combines accidentology to review past industrial accidents with failure modes and effects analysis (FMEA) to identify potential future incidents. Hydrogen explosion and toxic chemical release hazards are of particular concern. Explosion consequences are quantified using the Bauwens-Dorofeev (Bauwens) and trinitrotoluene equivalent mass (TNT-EM) methods, while chemical release consequences are computed using the Gaussian atmospheric dispersion method. Operational disturbances from direct electrical and thermal integration that may affect NPP safety are modeled using probabilistic risk analysis (PRA). Hazards and risks are then evaluated for regulatory compliance. The framework is applied to IESs comprising pressurized or boiling water reactors supplying three levels of thermal and electrical power to industrial customers. Case studies include high-temperature steam electrolysis hydrogen plants of varying capacities and a synthetic fuel production plant. Sensitivity analysis examines piping component failures in the PRA model as a precursor to cost estimation for thermal extraction line design. Additionally, Fussel-Vessely (FV) and risk increase importance (RII) measures identify risk-informed design improvements for the thermal extraction system. FMEA highlights hazards such as loss of offsite power, prompt loss of electrical load, loss of thermal output, and immediate steam diversion, in addition to hydrogen explosions and toxic chemical releases. Both Bauwens and TNT-EM methods suggest maintaining several hundred meters of separation between the NPP and hydrogen facility to mitigate explosion risks. PRA results show a maximum initiating event frequency increase of 1.15% and an overall risk increase of 0.28%. Importance measure analysis identifies upstream pipe leak isolation components as critical. Evaluating the results against safety regulations, it is concluded that hazards and risks can be managed to comply with regulations through risk-informed thermal and electrical connection designs, component selection, maintenance programs, and safe separation distances between NPPs and integrated industrial facilities.

08 - HYDROGEN↗
DOE OSTI2021

Tradeoffs and Synergies in Tropical Forest Root Traits and Dynamics for Nutrient and Water Acquisition: Field and Modeling Advances

Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, we know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions, and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, we identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. We also identify interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions, rather than reducing mycorrhizal colonization of fine roots as might be expected, increased colonization rates under scenarios of water scarcity in some forests. Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between the emphasis in models characterizing nutrient and water uptake rates and carbon costs versus the emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing us to better predict tropical forest-climate feedbacks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

X-ray linear dichroic tomography of crystallographic and topological defects

Abstract The functionality of materials is determined by their composition 1–4 and microstructure, that is, the distribution and orientation of crystalline grains, grain boundaries and the defects within them 5,6 . Until now, characterization techniques that map the distribution of grains, their orientation and the presence of defects have been limited to surface investigations, to spatial resolutions of a few hundred nanometres or to systems of thickness around 100 nm, thus requiring destructive sample preparation for measurements and preventing the study of system-representative volumes or the investigation of materials under operational conditions 7–15 . Here we present X-ray linear dichroic orientation tomography (XL-DOT), a quantitative, non-invasive technique that allows for an intragranular and intergranular characterization of extended polycrystalline and non-crystalline 16 materials in three dimensions. We present the detailed characterization of a polycrystalline sample of vanadium pentoxide (V 2 O 5 ), a key catalyst in the production of sulfuric acid 17 . We determine the nanoscale composition, microstructure and crystal orientation throughout the polycrystalline sample with 73 nm spatial resolution. We identify and characterize grains, as well as twist, tilt and twin grain boundaries. We further observe the creation and annihilation of topological defects promoted by the presence of volume crystallographic defects. The non-destructive and spectroscopic nature of our method opens the door to operando combined chemical and microstructural investigations 11,18 of functional materials, including energy, mechanical and quantum materials.

Science & Technology - Other Topics↗
DOE OSTI2026

Mechanics and Cooling of the CMS Phase-II Tracker Barrel

The CMS upgrade for the High-Luminosity LHC (HL-LHC) era will allow for an incredibly high rate of data collection, to be used in the next generation of precision measurements and searches for beyond the standard model physics. A key element of this upgrade is the full replacement of the silicon strip tracker to enable level-1 triggering on track level primitives, produced by pixel-strip (PS) and strip-strip (2S) modules, tiled together to form the new detector. Mechanically supporting and cooling these modules is not trivial, as material budget in the tracker volume is highly constrained, and as the modules accrue radiation damage they risk becoming entirely non-functional if the silicon sensors are not sufficiently cooled. To address this, each region of the tracker has a bespoke solution for housing and cooling their modules, mostly depending on carbon-fiber and carbon-foam structures, with dual phase CO2 cooling. For the most central part of the tracker, the Flat Barrel with PS modules (Flat TBPS) covers the pseudorapidity range out to $|\eta|=0.4$, and the mechanical structures are manufactured, assembled, and tested entirely by Fermilab and UC Davis. This presentation will describe the intense design and testing requirements for these components, and how they will ensure strong performance of the tracker through the HL-LHC era.

Cosby, Christopher [Fermilab]↗
NASA NTRS1998

Change Detection Analysis in Urban and Suburban Areas Using Landsat Thematic Mapper data: Case of Huntsville, Alabama

Two Landsat Thematic Mapper (TM) images, from July 1984 and July 1992, were used to identify land use/cover changes in the urban and suburban fringe of the city of Huntsville, Alabama. Image difference was the technique used to quantify the change between the two dates. The eight-year period showed a 16% change, mainly from agricultural lands to urban areas generated by the settlement of industrial, commercial, and residential areas. Visual analysis of the change map (i.e., difference image) supported this phenomenon by showing that most changes were occurring in the vicinity of the major roads and highways across the city.

Dana Kuan↗
NASA NTRS2026

Towards an Improved Understanding of the Antarctic Coastal Zone and Its Contribution to Future Global Sea Level

Understanding the coastal zone of the Antarctic Ice Sheet (AIS), where it interacts with the Southern Ocean and warmer air masses, is crucial for predicting Antarctica's influence on the global climate and sea level. This region has multiple tipping mechanisms that could trigger large, rapid, and potentially irreversible changes in the AIS, the Southern Ocean and their global connections in the coming centuries. The AIS remains the largest source of uncertainty in future sea-level projections. Bed topography beneath the ice shelves and the coastal ice sheet is not yet well documented, and is a major source of this uncertainty. This review assesses current knowledge of the coastal zone and highlights methods to investigate it, including aerogeophysical surveys, ground- and ship-based measurements, satellite observations, and computer modeling. An ensemble analysis of published bed topography data sets identifies significant data gaps and their regional distribution, framed in the context of current ice-sheet behavior and potential instability. We propose scientific priorities and guidelines for future aerogeophysical surveys, advocating for a comprehensive, coordinated international effort to build a next-generation data set of Antarctic bed properties. Such an initiative would significantly advance understanding of the role of coastal processes in ice-sheet dynamics, reducing uncertainties in sea-level rise projections and improving predictions of future ocean and climate changes.

Kenichi Matsuoka↗
NASA NTRS1992

Development and application of a 3-D geometry/mass model for LDEF satellite ionizing radiation assessments

A computer model of the three dimensional geometry and material distributions for the LDEF spacecraft, experiment trays, and, for selected trays, the components of experiments within a tray was developed for use in ionizing radiation assessments. The model is being applied to provide 3-D shielding distributions around radiation dosimeters to aid in data interpretation, particularly in assessing the directional properties of the radiation exposure. Also, the model has been interfaced with radiation transport codes for 3-D dosimetry response predictions and for calculations related to determining the accuracy of trapped proton and cosmic ray environment models. The methodology is described used in developing the 3-D LDEF model and the level of detail incorporated. Currently, the trays modeled in detail are F2, F8, and H12 and H3. Applications of the model which are discussed include the 3-D shielding distributions around various dosimeters, the influence of shielding on dosimetry responses, and comparisons of dose predictions based on the present 3-D model vs those from 1-D geometry model approximations used in initial estimates.

B. L. Colborn↗