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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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NASA NTRS1989

Thermal design of the Galileo spun and despun science

The delay in the launching of the Galileo spacecraft, which was to be launched aboard the Space Shuttle Atlantis, caused by the Challenger accident resulted in a decrease in the Radioisotope Thermoelectric Generator (RTG) power. A change to the Inertial Upper Stage from the more powerful Centaur G-Prime resulted in a trajectory that requires gravity assists, once by Venus and twice by earth. The resulting peak solar intensity of this roundabout trajectory is more than twice the previous design value for the direct trajectory. The resulting changes in solar intensity range and the RTG power output were substantial and forced major thermal design changes. This paper discusses the thermal design and redesign of the Galileo Spun and Despun sciences. Data are presented on the allowable temperatures for the Spun and Despun sciences, the energy balance calculated for the Despun science, and the Despun science test results.

Greenfield, M.↗
DOE OSTI2022

Combustion machine learning: Principles, progress and prospects

Progress in combustion science and engineering has led to the generation of large amounts of data from large-scale simulations, high-resolution experiments, and sensors. This corpus of data offers enormous opportunities for extracting new knowledge and insights—if harnessed effectively. Machine learning (ML) techniques have demonstrated remarkable success in data analytics, thus offering a new paradigm for data-intense analyses and scientific investigations through combustion machine learning (CombML). While data-driven methods are utilized in various combustion areas, recent advances in algorithmic developments, the accessibility of open-source software libraries, the availability of computational resources, and the abundance of data have together rendered ML techniques ubiquitous in scientific analysis and engineering. This article examines ML techniques for applications in combustion science and engineering. Starting with a review of sources of data, data-driven techniques, and concepts, we examine supervised, unsupervised, and semi-supervised ML methods. Various combustion examples are considered to illustrate and to evaluate these methods. Next, we review past and recent applications of ML approaches to problems in combustion, spanning fundamental combustion investigations, propulsion and energy-conversion systems, and fire and explosion hazards. Challenges unique to CombML are discussed and further opportunities are identified, focusing on interpretability, uncertainty quantification, robustness, consistency, creation and curation of benchmark data, and the augmentation of ML methods with prior combustion-domain knowledge.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

CHESS 2025: Orthorectified airborne RGB imagery from NEON AOP surveys

This dataset provides Level 1 (L1) and Level 3 (L3) orthorectified Red-Green-Blue (RGB) imagery collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). This high-resolution imagery is a photographic record of red, green, and blue visible light from sunlight reflected off of the Earth’s surface. The data comprise full-color images of the ground surface and are primarily intended to provide context to imaging spectroscopy and light detection and ranging (LiDAR) data. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. RGB images were acquired using the PhaseOne IXM-RS150F high-resolution digital camera onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). The package data include both an L1 product comprising one camera frame per file and an L3 mosaic aligned to the Universal Transverse Mercator (UTM) Zone 13N grid and the World Geodetic System (WGS) 84 projection. Both products are provided in geotif (.tif) format at 0.1 m ground resolution. The bulk of the imagery was collected during the main CHESS field campaign from June 13 to July 15, 2025. Additional images of a portion of the Upper Taylor (UPTA) domain were collected on September 18, 2025, to fill gaps in imagery identified after the main campaign was complete. RGB camera imagery is not radiometrically calibrated, and therefore pixel values should not be exploited for scientific analysis. Pixel values have undergone a manual adjustment to enhance feature identification. The imagery is rigorously geolocated which does allow for reliable geometric information to be retrieved. To generate the orthorectified imagery, the NEON AOP camera captured visible spectrum in red, green, and blue bands. The raw images were then processed using NEON’s camera orthorectification workflow. A boresight calibration flight was made to build a complete camera, distortion, and alignment model. Color balance/white balance and exposure correction were applied to the raw RGB images. The corrected images were orthorectified by ray-tracing image pixels to a lidar-derived digital surface model (DSM) mesh using the refined camera model, outputting orthorectified raster pixels on a regular grid. Flightline-level data were mosaicked by selecting per-pixel contributions from overlapping orthorectified images using line-of-sight (LOS) zenith angle minimization to reduce edge distortions. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1987

A Jupiter Data Analysis Program (JDAP) research grant on wave accessibility and attributes

For more than thirty years the intense decametric radio emissions from Jupiter (DAM) and the corresponding auroral kilometric radiation from the Earth (AKR) have remained major radio science mysteries. Part of the problem, aside from their inherent complexity, has been the difficulty of measuring their source location and emission properties from limited observations. Progress has been made on this problem by locating the source directly, i.e., by analysis of the faraday rotation observed with Voyager as the wave path crossed the Io plasma torus, and indirectly by comparing the peak frequencies of the decametric emission with that at the foot of the Io flux tube. Progress was also made on the general question of how the emissions originate by finding properties of both the AKR and DAM which would imply emission by natural radio lasing.

Calvert, Wynne↗
DOE OSTI2016

Observations and Modeling of the Green Ocean Amazon 2014/15: Hydroxyl Radical (OH) Chemical Ionization Mass Spectrometer (CIMS) Field Campaign Report

The University of California, Irvine, science team (Dr. Saewung Kim, Dr. Roger Seco, Dr. Alex Guenther, and Dr. Jim Smith) deployed a chemical ionization mass spectrometer system for hydroxyl radical (OH) and sulfuric acid quantifications. As part of the GoAmazon 2014/15 field campaign. Hydroxyl radical determines tropospheric oxidation capacity and had been expected to be very low in the pristine rain forest region such as the Brazilian Amazon because of the presence of significant levels of highly reactive biogenic volatile organic compounds and very low levels of NO, which is an OH recycling agent. However, several recent in situ OH observations provided by a laser-induced fluorescence system reported unaccountably high OH concentrations. To address this discrepancy, a series of laboratory and theoretical studies has postulated chemical reaction mechanisms of isoprene that may regenerate OH in photo-oxidation processes. Along with these efforts, potential artifacts on the laser induced fluorescence system from isoprene and its oxidation products also have been explored. Therefore, the first chemical ionization mass spectrometer observations at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility’s T3 site in Manacapuru, Brazil, are expected to provide a critical experimental constraint to address uncertainty in constraining oxidation capacity over pristine rain forest environments. In addition, we deployed a National Center for Atmospheric Research (NCAR) proton transfer reaction time-of-flight mass spectrometer to characterize atmospheric volatile organic compound levels, especially isoprene and its oxidation products, which are critical input parameters for box modeling to simulate OH with different isoprene photo-oxidation schemes. As there has been no report on noticeable new particle formation events, our first in situ sulfuric acid observations in the Amazon rain forest were expected to constrain the reasons behind such observations. The planned field observations during Intensive Observational Periods I and II, post-field campaign calibrations, and preliminary data reports have been completed. We presented preliminary data analysis results at the 2014 American Geophysical Union Fall meeting and the GOAmazon Science Meeting in Boston (May 2015). We are in the process of submitting two more abstracts to the 2015 American Geophysical Union fall meeting while we are preparing two manuscripts to be submitted to (tentatively) the GOAmazon special issue of Atmospheric Chemistry and Physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

TRACER CLAMPS1 Doppler Lidar Vertical Stare Data

These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline Doppler lidar. While not conducting other scans, the lidar directs the beam to zenith, allowing for the measurement of vertical velocity. These data were collected during the TRACER project.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Dust distribution of Comet Halley from the Giotto radio science experiment

Measurements from the Giotto radio science experiment representing the Doppler frequency shift and the intensity level of the X-band downlink signal of the Giotto spacecraft during its encounter with comet Halley are shown. Continuous data reception was maintained throughout the encounter. The Doppler shift measured within a time interval of 100 sec is due to drag effects in the cometary atmosphere causing a deceleration of the spacecraft. The total change of velocity of Giotto is 23.2 cm/sec resulting in a best estimate of 0.32 g for the total cometary mass impacting Giotto during the Halley flyby. A dust jet structure for the inner coma of comet Halley is derived. Several sharply confined dust jets are distinguishable; for the most prominent one, characteristic properties are deduced. Features of this jet structure are shown to correlate with measurements from on-board Giotto experiments and with Earth-based observations.

Edenhofer, P.↗
NASA NTRS2000

BOREAS RSS-20 POLDER Radiance Images From the NASA C-130

These Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-20 data are a subset of images collected by the Polarization and Directionality of Earth's Reflectance (POLDER) instrument over tower sites in the BOREAS study areas during the intensive field campaigns (IFCs) in 1994. The POLDER images presented here from the NASA ARC C-130 aircraft are made available for illustration purposes only. The data are stored in binary image-format files. The POLDER radiance images are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Leroy, M.↗
DOE OSTI2015

Recent evolution of the offline computing model of the NOvA experiment

The NOvA experiment at Fermilab is a long-baseline neutrino experiment designed to study ν e appearance in a ν μ beam. Over the last few years there has been intense work to streamline the computing infrastructure in preparation for data, which started to flow in from the far detector in Fall 2013. Major accomplishments for this effort include migration to the use of off-site resources through the use of the Open Science Grid and upgrading the file-handling framework from simple disk storage to a tiered system using a comprehensive data management and delivery system to find and access files on either disk or tape storage. NOvA has already produced more than 6.5 million files and more than 1 PB of raw data and Monte Carlo simulation files which are managed under this model. In addition, the current system has demonstrated sustained rates of up to 1 TB/hour of file transfer by the data handling system. NOvA pioneered the use of new tools and this paved the way for their use by other Intensity Frontier experiments at Fermilab. Most importantly, the new framework places the experiment's infrastructure on a firm foundation, and is ready to produce the files needed for first physics.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

A Long-Term GOES Satellite Overshooting Cloud Top and Anvil Cloud Climatology Over South America

The modern-era GOES satellite series began in 1994 with the GOES-8 satellite, and was augmented in 2018 with higher spatial resolution and more frequent imaging when GOES-16 became operational. GOES imagery has provided forecasters and researchers new perspectives into cloud top patterns associated with severe convection, and the ability to better forecast convection in regions without adequate ground-based weather radar coverage. While much attention has been given to convection over North America, convection over South America can be equally, if not more, intense and frequent. Recent studies have demonstrated that overshooting cloud tops (OT) and surrounding anvil clouds can be detected within infrared satellite imagery. Relative storm updraft intensity metrics such as the tropopause-relative infrared brightness temperature, the prominence of an OT relative to its surrounding anvil, and cloud top height can also be derived using automated methods combined with reanalysis data. These automated OT detection and intensity estimation methods have recently been applied to all GOES images collected over South America, in combination with the MERRA-2 reanalysis, from 1995 to 2022 at NASA Langley within a project supported by the NASA Applied Sciences Disasters program. Innovative aggregation methods have merged these products into daily, monthly, annual, and multi-annual composites, with hourly time bins, at ~4 km pixel spacing to enable researchers a new opportunity to study South American convective processes throughout the diurnal cycle. These products have recently become publicly available from NASA. This presentation will overview this new dataset, and novel insights into South American convection depicted by the data.

Kristopher Bedka↗
DOE OSTI2015

Method of monitoring photoactive organic molecules in-situ during gas-phase deposition of the photoactive organic molecules

A method for in-situ monitoring of gas-phase photoactive organic molecules in real time while depositing a film of the photoactive organic molecules on a substrate in a processing chamber for depositing the film includes irradiating the gas-phase photoactive organic molecules in the processing chamber with a radiation from a radiation source in-situ while depositing the film of the one or more organic materials and measuring the intensity of the resulting photoluminescence emission from the organic material. One or more processing parameters associated with the deposition process can be determined from the photoluminescence intensity data in real time providing useful feedback on the deposition process.

14 SOLAR ENERGY↗
DOE OSTI2014

Atmospheric Research Measurement Climate Research Facility - Atmospheric System Research High-Resolution Modeling Workshop, May 19-20, 2014

The Atmospheric Radiation Measurement (ARM) Climate Research Facility is a scientific user facility within the U.S. Department of Energy (DOE) Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER). ARM is an observation facility whose purpose is to provide ground-based observations of the atmosphere to support climate research and to support the improvement of global climate models. ARM is closely affiliated with the Atmospheric System Research (ASR) program, which uses ARM data to address key atmospheric science issues and improve the parameterization of physical processes in climate models. A common methodology for using ARM data to improve climate models has been to carry out multi-model evaluation projects, typically in conjunction with field campaigns in which intensive radiosonde launches support the development of model forcing data sets. These exercises have yielded important insights. However, in recent years, there has been a growing interest in supporting high-resolution model simulations on a more routine basis. An important aspect of ARM sites is the continuous collection of high-resolution data over long periods of time. More routine model simulations would take better advantage of these long-term data sets. Nevertheless, accomplishing this will require careful attention to the requirements for running and evaluating these models together with a research program that maximizes the benefit of the model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

A study of ion composition and dynamics at Comet Halley

This report details the participation by Lockheed co-investigators in the reduction, analysis, and interpretation of data obtained by the Ion Mass Spectrometer onboard the Giotto mission to Comet Halley. The data analysis activities and much of the scientific collaboration was shared by this team. One objective of the effort under this contract was to use data obtained by the Giotto Ion Mass Spectrometer (IMS) during the encounter with comet Halley for the purpose of advancing our understanding of the chemistry and physics of the interaction of the solar wind with comets and obtaining new information on the comet's composition. An additional objective was to make this unique data set available in a format which can be easily used by the reset of the cometary science community for other analysis in the future. The IMS has two sensors: the High Intensity Spectrometer (HIS) and the High Energy Range Spectrometer (HERS).

Shelley, E. G.↗
DOE OSTI2021

Characterization of Extremes and Compound Impacts: Applications of Machine Learning and Interpretable Neural Networks

Focal Area: This white paper responds to Focal area III by exploring data fusion, learning and explainable AI methods in characterizing hydrological extremes and interconnections. It also addresses Focal area II by using probabilistic AI and ensemble ML for predicting extremes and compound extremes. Science Challenge: A key question associated with the integrated water (or hydrological) cycle grand challenge in the Earth and Environmental Systems Sciences Division (EESSD) strategic plan, is how the frequency and intensity of hydrological events will change. Prediction of the tail behavior (extremes) of the hydrological cycle is especially challenging, because of their stochasticity and low probability. These extreme events and their compound impacts have significant societal and economic consequences. It is anticipated for the next-generation Earth System models (ESMs), that model predictability of the water cycle will improve with increased resolution (e.g., regionally refined E3SM), advanced software and computational architectures, and improved model physics based on the data from ARM measurements and high-fidelity models. However, the challenges for predictability of low-probability high-impact extreme events will unlikely be alleviated with conventional modeling and data-driven approaches, as ESMs are calibrated largely for capturing the high-frequency mean climate states. Recent AI and ML applications have shown great potential in quantifying well-defined climate extremes (e.g., supervised learning of tropical cyclones/atmospheric rivers by ClimateNet1) but few efforts are dedicated to compound events, extreme drivers and uncertainty estimation. We envision the opportunity to develop and apply ML and interpretable AI methods extended on the existing efforts, specifically, for: (1) identification of compound extremes, (2) diagnosing drivers of extremes, (3) bias correction in extreme predictions and (4) probabilistic modeling of extremes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences

Capturing and quantifying the world in three dimensions (x,y,z) using light detection and ranging (lidar) technology drives fundamental advances in the Earth and Ecological Sciences (EES). However, additional lidar dimensions offer the possibility to transcend basic 3-D mapping capabilities, including i) the physical time (t) dimension from repeat lidar acquisition and ii) laser return intensity (LRIλ) data dimension based on the brightness of single- or multi-wavelength (λ) laser returns. The additional dimensions thus add to the x,y, and z dimensions to constitute the five dimensions of lidar (x,y,z, t, LRIλ1... λn). This broader spectrum of lidar dimensionality has already revealed new insights across multiple EES topics, and will enable a wide range of new research and applications. Here, we review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3-D. Our review outlines the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications and insights across the full range of EES applications.

Eitel, Jan U. H.↗
DOE OSTI2016

Morphology and Evolution of 14Cr Powder During Mechanical Alloying for Oxide Dispersion Strengthened Ferritic Alloys

Oxide Dispersion Strengthened (ODS) Ferritic steel alloys are candidate materials for Generation IV and Fusion reactor concepts due to their high temperature material properties and their irradiation stability. However, these material properties are strongly influenced by nanocluster dispersion and grain size distributions, which are directly correlated to mechanical alloying methodologies and parameters. Current powder processing strategies use ball milling times greater than or equal to 40 hrs to achieve sufficient dispersoid, i.e Y{sub 2}O{sub 3}, incorporation within the metal matrix, but these time intensive milling cycles are neither practical nor economical. This work aims to investigate how the particle morphology evolves during mechanical alloying during the processing of a 14Cr ODS ferritic alloy, and if higher-intensity milling, shorter milling cycles can still achieve sufficient mixing of yttria in comparison to the dispersion and incorporation observed in conventional lower-intensity milling methodologies. Comparisons of milled particle size distributions are presented for low and high intensity milling cycles, while microscopy and high-temperature tensile data are presented for consolidated samples using higher-intensity milling parameters. (authors)

36 MATERIALS SCIENCE↗
DOE OSTI2019

Volumetric Segmentation via Neural Networks Improves Neutron Crystallography Data Analysis

Crystallography is the powerhouse technique for molecular structure determination, with applications in fields ranging from energy storage to drug design. Accurate structure determination, however, relies partly on determining the precise locations and integrated intensities of Bragg peaks in the resulting data. Here, we describe a method for Bragg peak integration that is accomplished using neural networks. The network is based on a U-Net and identifies peaks in three-dimensional reciprocal space through segmentation, allowing prediction of the full 3D peak shape from noisy data that is commonly difficult to process. The procedure for generating appropriate training sets is detailed. Trained networks achieve Dice coefficients of 0.82 and mean IoUs of 0.69. Carrying out integration over entire datasets, it is demonstrated that integrating neural network-predicted peaks results in improved intensity statistics. Furthermore, using a second dataset, the possibility of transfer learning between datasets is shown. Given the ubiquity and growing complexity of crystallography, we anticipate integration by machine learning to play an increasingly important role across the physical sciences. These early results demonstrate the applicability of deep learning techniques for integrating crystallography data and suggest a possible role in the next generation of crystallography experiments.

Sullivan, Brendan T.↗