SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
RdTools is a set of Python tools for analysis of photovoltaic data. In particular, PV production data is evaluated over several years to obtain rates of performance degradation over time. Rdtools can handle both high frequency (hourly or better) or low frequency (daily, weekly, etc.) datasets. Best results are obtained with higher frequency data.
Introduction: The loss of bone mineral in astronauts during spaceflight has been investigated throughout the more than 40 years of bone research in space. Consequently, it is a medical requirement at NASA that changes in bone mass be monitored in crew members by measurements of bone mineral density (BMD) with dual-energy x-ray absorptiometry (DXA). This report is the first to evaluate medical data to address the recovery of bone mineral that is lost during spaceflight. Methods: DXA scans are performed before and after flight in astronauts who serve on long-duration missions (4-6 months) to ensure that medical standards for flight certification are met, to evaluate the effects of spaceflight and to monitor the restoration to preflight BMD status after return to Earth. Through cooperative agreements with the Russian Space Agency, the Bone and Mineral Lab at NASA Johnson Space Center (Houston, TX), also had access to BMD data from cosmonauts who had flown on long-duration missions yielding data from a total of 45 individual crew members. Changes in BMD (between 56 different sets of pre- and postflight measurements) were plotted as a function of time (days after landing); plotted data were fitted to an exponential mathematical model that determined i) BMD change at day 0 after landing and ii) the number of days after which 50% of the lost bone was recovered ("Recovery Half-Life"). These fits were performed for BMD of the lumbar spine, trochanter, pelvis, femoral neck and calcaneus. Results: In sum, averaged losses of bone mineral after spaceflight ranged between 2-9% for sites in the axial and appendicular skeleton. The fitted postflight BMD values predicted a 50% recovery of bone loss for all sites within 9 months.
Utah has a wealth of potential geological reservoirs for carbon dioxide storage (CS) and a long history of geologic research resulting in an abundance of available subsurface data to evaluate CS potential. Reservoirs may include sandstone, carbonate, and basalt; these rock types are plentiful in Utah’s subsurface and the complex Phanerozoic history throughout the state requires evaluating each geologic region individually for promising reservoir-seal pairs for CO2 storage. Classifying Utah by geologic provinces (or “geo-regions”) allows for customized thinking about suitable CS reservoir and seal distribution, CO2 point sources, land use, and existing infrastructure. Preliminary results from this study highlight the geologic CS potential across 15 geo-regions. Four regions stand out as having high CS potential: the Uinta Basin, San Rafael Swell, Paradox Basin, and the southern Basin and Range Province. The Uinta Basin and San Rafael Swell geo-regions are well suited for CS and have several projects ongoing to evaluate Cretaceous Frontier and Naturita Formations, Jurassic Navajo Sandstone and Entrada Sandstone, and Permian Weber Sandstone reservoir units that lie beneath robust sealing units like the ~5000-ft-thick Mancos Shale and Carmel Formation. Reservoirs such as the Navajo and Weber Sandstones have been demonstrated to be suitable reservoirs through a long history of oil and gas exploration in Utah. New areas of interest include the southern Basin and Range in southwest Utah, where the Jurassic Navajo Sandstone is overlain by the sealing Carmel Formation at suitable depths (>3000 ft), and has good porosities based on outcrop analogue data. Just to the north (e.g., central Basin and Range), legacy wells and 2D seismic data show possible salt and subsurface basalt flows that may provide additional possible CS reservoirs and seals. This geo-region also has the advantage of being coupled with geothermal energy resources that may be used to power burgeoning direct air capture technologies. In the Paradox Basin of southeastern Utah, the Leadville Limestone is a potential storage reservoir beneath the thick (4000–5000 ft), salt-bearing Pennsylvanian Paradox Formation. Although the northern and western parts of Utah offer CS potential, these areas typically contain less infrastructure and subsurface penetrations, creating geologic uncertainty associated with subsurface seals and reservoirs due to a lack of data. Overall, this statewide assessment and ranking is the first step to aid in evaluating CS potential across Utah and provides a foundation for future research in the most favorable locations.
The experiment from which benchmark specifications of "Lady Godiva" were derived consisted of nested hemispheres forming a bare sphere of highly enriched uranium (HEU). That experiment was performed in the early 1950s and the critical configuration was later (1995) evaluated and published in the International Handbook of Evaluated Criticality Safety Benchmark Experiments . The benchmark eigenvalue is reported as 1.000 ± 0.001, which is representative of a high-quality benchmark experiment. Our current neutronic codes and cross section data are tailored to provide qualitative results that concur with the GODIVA I benchmark. Since 1995, additional high-fidelity HEU metal benchmark data have been evaluated and published. Calculated results from those benchmarks are consistently low. Those findings are investigated further in this paper.
Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.
The considerable body of data available for evaluating biometric recognition systems in Research and Development (R&D) environments has contributed to the increasingly common problem of target performance mismatch. Biometric algorithms are frequently tested against data that may not reflect the real world applications they target. From a Testing and Evaluation (T&E) standpoint, this domain mismatch causes difficulty assessing when improvements in State-of-the-Art (SOTA) research actually translate to improved applied outcomes. This problem can be addressed with thoughtful preparation of data and experimental methods to reflect specific use-cases and scenarios.To that end, this paper evaluates research solutions for identifying individuals at ranges and altitudes, which could support various application areas such as counterterrorism, protection of critical infrastructure facilities, military force protection, and border security. We address challenges including image quality issues and reliance on face recognition as the sole biometric modality. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification from both the ground and steep pitch angles. Preliminary results show promising progress in whole-body recognition. This paper presents these early findings and discusses potential future directions for advancing long-range biometric identification systems based on mission-driven metrics.
The experimental vapor-liquid equilibrium (VLE) data for the binaries relevant to the catalytic fast pyrolysis of biomass have been collected and analyzed using the NIST-COSMO-SAC and NIST-modified UNIFAC models. The existing inconsistencies in the experimental data and the predicted values are discussed. For VLE with furan derivatives, PTxy data are normally reported, and the predicted values are in good agreement with them. For phenolic compounds, the gas phase compositions are available for only 36 % of the points, most results originate from a few laboratories, and each system has been typically studied in a single laboratory. The binaries are identified where more experimental VLE data are required to evaluate quality of the existing experimental and predicted results.
The findings and recommendations of the Earth and Ocean Physics working group of the space shuttle payload planning activity are presented. The requirements for the space shuttle mission are defined as: (1) precision measurement for earth and ocean physics experiments, (2) development and demonstration of new and improved sensors and analytical techniques, (3) acquisition of surface truth data for evaluation of new measurement techniques, (4) conduct of critical experiments to validate geophysical phenomena and instrumental results, and (5) development and validation of analytical/experimental models for global ocean dynamics and solid earth dynamics/earthquake prediction. Tables of data are presented to show the flight schedule estimated costs, and the mission model.
Acoustic data inside the payload bay and over the exterior of the orbiter were collected. The data were used to evaluate the Payload Acoustic Environment for Shuttle (PACES) computer program.
Synthetic aperture radar (SAR) is a coherent imaging device which uses the Doppler-induced phase changes of the received signals to locate scatterers in the along-track direction. In principle, the recorded data can be analyzed to obtain the radial velocity of moving objects in the scene relative to the flight path of the SAR platform. The application of this concept for measuring ocean surface currents has been evaluated using data from SAR systems mounted in aircraft and spacecraft platforms. This paper describes the results of these experiments and summarizes the limitations of the technique using existing SAR systems. Considerations involved in the design of optimum systems for this application are also discussed.
Ozone data from the solar occultation Polar Ozone and Aerosol Measurement (POAM) III instrument are included in the ozone assimilation system at NASA's Global Modeling and Assimilation Office, which uses Solar Backscatter UItraViolet/2 (SBUV/2) instrument data. Even though POAM data are available at only one latitude in the southern hemisphere on each day, their assimilation leads to more realistic ozone distribution throughout the Antarctic region, especially inside the polar vortex. Impacts of POAM data were evaluated by comparisons of assimilated ozone profiles with independent ozone sondes. Major improvements in ozone representation are seen in the Antarctic lower stratosphere during austral Winter and spring in 1998. Limitations of assimilation of sparse occultation data are illustrated by an example.
Nuclear data (ND) libraries are the backbone of the nuclear industry, as they are the collections of tabulated probabilities that define sub-atomic particle interactions with matter. In the areas of criticality safety and experiments, no evaluated nuclear data files (ENDF) are more important than those containing neutron cross section data. It is upon these files, and accompanying radiation transport codes, that practitioners are enabled to safely design both subcritical and critical systems. Likewise, in a symbiotic fashion, it is the same critical assemblies which are used primarily to validate that the cross sections are correct. The ENDF/B library, the United States’ national library maintained by the National Nuclear Data Center (NNDC) at Brookhaven National Laboratory (BNL), is soon releasing a new version, ENDF/B-VIII.1. Prior to the official release, several beta versions of the library were prepared and tested in simulation suites. The work presented here are results comparing the newest ENDF/B beta release (ENDF/B-VIII.1b3) and ENDF/B-VIII.0 with recent experiments done at the National Criticality Experiments Research Center (NCERC) as well as correlated experiments from the Los Alamos Critical Experiments Facility (LACEF). These NCERC and LACEF experiments were performed in part to provide validation for various cross sections that were identified as insufficient in the ENDF/B-VIII.0. In particular, lead, copper, fluorine, and chlorine, as well as the major actinides, were targeted from the last decade of critical experiments.
Introducing risk-informed safety analyses for external events can bring sensible benefits to the nuclear power industry easing the regulatory burden introduced in the aftermath of the Fukushima accident. In the framework of the US-DOE Light-Water Reactor Sustainability program, Risk-Informed Margin Characterization project (LWRS-RISMC), research activities were conducted at INL for developing and applying a toolkit for performing risk-informed external hazards analysis. The “EEVE” (External EVEnts) toolkit, a chain of INL and state-of-the-art codes, was set-up and applied for simulating earthquake-induced transients with internal flooding in a PWR. This was done using methodology set-up for evaluating new data and determining critical areas that would benefit most from advanced analysis methods. The methodology is agnostic and it can be applied to other external hazards than just seismic and flooding (e.g., high winds, intense precipitation, etc.). The first step was the deterministic calculations for evaluating the propagation of a set of earthquakes and for determining the effects on a fire suppression system in an auxiliary building. These deterministic calculations were performed using the LS-DYNA and the OPENSEE structural mechanics codes. Probability failures and event trees of a generic 3-loops Westinghouse PWR were derived from a generic SAPHIRE model. Seismically induced Loss of Offsite Power, Station Blackout, and fire suppression piping failure events were considered. SAPHIRE code allowed a pre-screening of all possible branches of the event tree and a selection of those with the largest Core Damage Frequency increase for further analysis using dynamic PRA methods. INL's EMRALD code performed dynamic PRA calculations of the identified sequences, integrating risk analysis with on-line deterministic safety analysis results (seismically-induced internal flooding calculations by NEUTRINO code and system analysis by RELAP5-3D code). RELAP5-3D Best-Estimate calculations coupled with RAVEN statistical code were used for deriving limit surfaces as function of relevant transient parameters. This data was used by EMRALD for determining the final core status (core damage/core safe). Finally, the coupling of seismic, flooding, system thermal-hydraulic, uncertainty quantification and probabilistic risk analysis for an advanced risk-informed external hazards analysis was demonstrated. Results showed the sensible decrease in the level of conservatism achieved using the developed risk-informed methodology, which allowed also improving the evaluation of the failure modes and failure probabilities of different components.
Simulations were conducted using the BISON fuel performance code on an automated process to read initial and operating conditions from two databases—the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor Materials Information System (IMIS) database. These databases contain metallic fuel data from the Experimental Breeder Reactor-II (EBR-II) and the Fast Flux Test Facility (FFTF). The work demonstrates use of an integrated framework to access EBR-II fuel pin data for evaluating fuel performance models contained within BISON to predict fuel performance of next-generation metallic fuel systems. Between IMIS and FIPD, there is enough information to conduct 1,977 unique EBR-II metallic fuel pin histories from 29 different experiments, and 338 pins from FFTF MFF-3 and MFF-5 with varying levels of details between the two databases. Each of these fuel performance histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures, and new model developments in BISON since the initial demonstration of this integrated framework. Fission gas release (FGR), cumulative damage fraction, fuel axial swelling, FCCI wastage thickness, cladding profilometry, and burnup were all simulated in BISON and compared to post-irradiation examination (PIE) results to evaluate BISON fuel performance modeling. Implementation of new fuel performance models into a generic BISON input file coupled with IMIS and FIPD yielded results with a better representation of physics than the initial evaluation of the integrated framework. Cladding profilometry, FGR, and fuel axial swelling were found to be in good agreement with PIE measurements for most of the pins simulated. The chosen mechanical contact solver was found to significantly impact the axial fuel swelling and cladding strain predictions when used in conjunction with the U-Pu-Zr hot-pressing model since it bound the fuel to prevent further swelling and increased hydrostatic stresses. This work suggests that fuel performance modeling in BISON under steady-state conditions represents the PIE data well and should be reassessed when new PIE data become available in IMIS and FIPD databases and when improved physical models to better capture fuel performance are added to BISON.
A study which may guide the operations of existing computer installations, as well as the design of future networks, is described. Performance data and evaluations are considered with reference to interarrival time, users' habits, waiting time for execution, time spent in a partition, figures of merit, and states of the system. The analysis of the variables proceeds from examination of typical data with appropriate statistical tests to conclusions about the possible state of nature.
The usefulness of LANDSAT in classifying land cover and in identifying and classifying land use change was investigated using an urbanizing area as the study area. The question of what was the best technique for classification was the primary focus of the study. The many computer-assisted techniques available to analyze LANDSAT data were evaluated. Techniques of statistical training (polygons from CRT, unsupervised clustering, polygons from digitizer and binary masks) were tested with minimum distance to the mean, maximum likelihood and canonical analysis with minimum distance to the mean classifiers. The twelve output images were compared to photointerpreted samples, ground verified samples and a current land use data base. Results indicate that for a reconnaissance inventory, the unsupervised training with canonical analysis-minimum distance classifier is the most efficient. If more detailed ground truth and ground verification is available, the polygons from the digitizer training with the canonical analysis minimum distance is more accurate.
Inland open water bodies often pose a systematic error source in the passive remote sensing retrievals of soil moisture. Water temperature is a necessary variable used to compute water emissions that is required to be subtracted from satellite observation to yield actual emissions from the land portion, which in turn generates accurate soil moisture retrievals. Therefore, overestimation of soil moisture can often be corrected using concurrent water temperature data in the overall mitigation procedure. In recent years, several data sets of lake water temperature have become available, but their specifications and accuracy have rarely been investigated in the context of passive soil moisture remote sensing on a global scale. For this reason, three lake temperature products were evaluated against in-situ measurements from 2007 to 2011. The data sets include the lake surface water temperature (LSWT) from Global Observatory of Lake Responses to Environmental Change (GloboLakes), the Copernicus Global Land Operations Cryosphere and Water (C-GLOPS), as well as the lake mix-layer temperature (LMLT) from the European Centers for Medium-Range Weather Forecast (ECMWF) ERA5 Land Reanalysis. GloboLakes, C-GLOPS, and ERA5 Land have overall comparable performance with Pearson correlations (R) of 0.87, 0.92 and 0.88 in comparison with in-situ measurements. LSWT products exhibit negative median biases of −0.27 K (GloboLakes) and −0.31 K (C-GLOPS), whereas the median bias of LMLT is 1.56 K. When mapped from their respective native resolutions to a common 9 km Equal-Area Scalable Earth (EASE) Grid 2.0 projection, similar relative performance was observed. LMLT and LSWT data are closer in performance over the 9 km grid cells that exhibit a small range of lake cover fractions (0.05–0.5). Despite comparable relative performance, ERA5 Land shows great advantages in spatial coverage and temporal resolution. In summary, an integrated evaluation on data accuracy, long-term availability, global coverage, temporal resolution, and regular forward processing with modest data latency led us to conclude that LMLT from the ERA5 Land Reanalysis product represents the most optimal path for use in the development of a long-term soil moisture product.
Even with the advent of high throughput methods to detect modified ribonucleic acids (RNAs), mass spectrometry remains a reliable method to detect, characterize, and place post-transcriptional modifications within an RNA sequence. Here we have developed a stable isotope labeling comparative analysis of RNA digests (SIL-CARD) approach, which improves upon the original {sup 18}O/{sup 16}O labeling CARD method. Like the original, SIL-CARD allows sequence or modification information from a previously uncharacterized in vivo RNA sample to be obtained by direct comparison with a reference RNA, the sequence of which is known. This reference is in vitro transcribed using a {sup 13}C/{sup 15}N isotopically enriched nucleoside triphosphate (NTP). The two RNAs are digested with an endonuclease, the specificity of which matches the labeled NTP used for transcription. As proof of concept, several transfer RNAs (tRNAs) were characterized by SIL-CARD, where labeled guanosine triphosphate was used for the reference in vitro transcription. RNase T1 digestion products from the in vitro transcript will be 15 Da higher in mass than the same digestion products from the in vivo tRNA that are unmodified, leading to a doublet in the mass spectrum. Singlets, rather than doublets, arise if a sequence variation or a post-transcriptional modification is present that results in a relative mass shift different from 15 Da. Moreover, the use of the in vitro synthesized tRNA transcript allows for quantitative measurement of RNA abundance. Overall, SIL-CARD simplifies data analysis and enhances quantitative RNA modification mapping by mass spectrometry. .