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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Constraints on the Origin of Mercury’s Large Core from Core-Mantle Differentiation Models

Mercury’s core is notoriously large when compared to other planets of our solar system. The origin of this large core is still uncertain. Available data on the surface composition and internal structure of Mercury from the past MESSENGER mission and future data collected by BepiColombo will continue to provide clues to Mercury’s formation. Here, we present results combining experimental data on elemental distribution between core and mantle with spacecraft data, to estimate bulk Mercury composition. We applied this strategy to major elements (Fe, Si, Mg, Al and O), as well as minor elements (Cr and Ti), and compared derived compositions to chondritic data and the chemical compositions of the other terrestrial planets. Our results show that Mercury has a chemical composition significantly different from all known materials of the solar system. In addition, numerous scenarios were proposed to explain Mercury’s structure, including “chaotic models” such as giant impacts and “orderly models” such as aerodynamic sorting. Here, we tested whether Mercury’s composition can be explained by mantle stripping by impacts. We will show how such a scenario reconciles several features of Mercury’s geochemistry with chondritic data. We will also discuss the outlook of additional constraints from supplementary data potentially collected by BepiColombo.

Mercury↗
NASA NTRSDate not supplied

Brillouin Asymmetric Spatial Heterodyne Oceanographic Lidar Receiver for Profiling Temperature, Salinity, and Sound Velocity

No sensor today is capable of remotely sensing temperature and salinity at depth in oceanic waters, yet the physics to do so exists. Blue-green light (450-550 nm) can penetrate 10’s of meters into the water and interacts with water by the Brillouin scatter process. Temperature and salinity can be determined by analyzing the spectrum of Brillouin scatter. A host of scientific and operational drivers exist for such a sensor, from improved hurricane and red tide forecasting to studies of ocean fronts, eddies, and freshwater lenses. A low flying airborne light detection and ranging (lidar) instrument concept that exploits this physics is presented, along with simulation tools that potential data users can use to model its measurement performance, determine suitability for their application, and assess its implications.

John Anthony Smith↗
DOE OSTI2025

Data from "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions"

Data and input files related to the paper "Deep Potential Molecular Dynamics Simulations of Low-Temperature Plasma-Surface Interactions" (https://doi.org/10.1116/6.0004027). This includes the final DP model used in all simulations, training data set, example input files to run DeepMD (with LAMMPS), and data tables summarizing the results obtained from the simulations.

machine learning models↗
DOE OSTI2023

Ultra-fast interpretable machine-learning potentials

Abstract All-atom dynamics simulations are an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. To address this challenge, we combine effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, as fast as the fastest traditional empirical potentials, and two to four orders of magnitude faster than state-of-the-art machine-learning potentials. For data from empirical potentials, we demonstrate the exact retrieval of the potential. For data from density functional theory, the predicted energies, forces, and derived properties, including phonon spectra, elastic constants, and melting points, closely match those of the reference method. The introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Identification of Cirrus over Wausau during the 1986 FIRE IFO from ground-based radiometer data

The potential of using irradiation data to indicate episodes of cirrus cloudiness during the daylight hours is explored. Thresholds separating cirrus from other clouds and clear skies are determined using four days of irradiation data, hourly weather observations, sky photographs, sky video, and occasional lidar observations. Data were gathered during the First International Satellite Cloud Climatology Program (ISCCP) Regional Experiment (FIRE) Intensive Field Observations (IFO) cirrus project. Thresholds are tested using data from the remaining 17 days of the IFO. Cirrus episodes are defined as intervals when the sky cover is primarily cirrus. Measurements of incoming shortwave and near infrared full hemispheric and diffuse irradiation, and atmospheric infrared irradiation were made at Wausau, Wisconsin Municipal Airport. Data were collected between October 13 and November 2, 1986 and are one minute averages of ten second samples. Data from October 23 and from October 27 to 29 were used for threshold determination. Using weather observations, lidar output, photographs and video, sky conditions were grouped into three categories: noncirrus cloudy, cirrus, and clear. Irradiation data from all periods falling under each of the sky categories were then examined and thresholds denoting a change from one sky category to another were determined. Variables with only a small amount of overlap between sky categories were selected as key indicators.

Robinson, David A.↗
DOE OSTI2020

Simple and efficient algorithms for training machine learning potentials to force data

Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of available training data, which can be expensive to obtain. A quantum simulation often provides all atomic forces, in addition to the total energy of the system. These forces provide much more information than the energy alone. It may appear that training a model to this large quantity of force data would introduce significant computational costs. Actually, training to all available force data should only be a few times more expensive than training to energies alone. Here, we present a new algorithm for efficient force training, and benchmark its accuracy by training to forces from real-world datasets for organic chemistry and bulk aluminum.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

Performance Potential of Mixed Data Management Modes for Heterogeneous Memory Systems

Many high-performance systems now include different types of memory devices within the same compute platform to meet strict performance and cost constraints. Such heterogeneous memory systems often include an upper-level tier with better performance, but limited capacity, and lower-level tiers with higher capacity, but less bandwidth and longer latencies for reads and writes. To utilize the different memory layers efficiently, current systems rely on hardware-directed, memory -side caching or they provide facilities in the operating system (OS) that allow applications to make their own data-tier assignments. Since these data management options each come with their own set of trade-offs, many systems also include mixed data management configurations that allow applications to employ hardware- and software-directed management simultaneously, but for different portions of their address space. Despite the opportunity to address limitations of stand-alone data management options, such mixed management modes are under-utilized in practice, and have not been evaluated in prior studies of complex memory hardware. In this work, we develop custom program profiling, configurations, and policies to study the potential of mixed data management modes to outperform hardware- or software-based management schemes alone. Our experiments, conducted on an Intel ® Knights Landing platform with high-bandwidth memory, demonstrate that the mixed data management mode achieves the same or better performance than the best stand-alone option for five memory intensive benchmark applications (run separately and in isolation), resulting in an average speedup compared to the best stand-alone policy of over 10 %, on average.

Effler, Chad↗
DOE OSTI2024

Geospatial characterization of low-temperature heating and cooling demand in residential, commercial, manufacturing, agricultural, and data center sectors for potential geothermal applications in the United States

Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. Here, this study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Self-potential tomography preconditioned by particle swarm optimization—Self-potential monitoring and streamflow data acquired March 26–September 14, 2023 at East Fork Poplar Creek near Oak Ridge Tennessee

This data release contains self-potential (SP) monitoring data measured on the flood plain of East Fork Poplar Creek (East Fork) in Oak Ridge, Tennessee and streamflow data measured at streamgage EFK5.4 about 310 meters upstream from the SP monitoring site. Additionally, forward and inverse numerical modeling scripts used to model the electrical-potential field on the East Fork flood plain are provided. SP monitoring data included in this data release were measured at 39 different data-collection points on the east flood plain; 30 points were spaced 3-m apart along an 87-m profile parallel to the edge of the streambank, and 9 points were spaced 5-m apart along a 40-m profile approximately perpendicular to the streambank. The two profiles of SP data-collection points intersected at the approximate midpoint of the profile parallel to the streambank. Transient voltages were measured at each data-collection point every 60 seconds between 16:13 Eastern Standard Time (EST) on March 26, 2023, and 11:41 EST on September 14, 2023. Streamflow data included in this data release overlap the time-period of self-potential monitoring and were measured every 900 seconds between 16:23 on March 26, 2023, and 23:53 on September 14, 2023.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

Potential of VIIRS Data for Regional Monitoring of Gypsy Moth Defoliation: Implications for Forest Threat Early Warning System

A NASA RPC (Rapid Prototyping Capability) experiment was conducted to assess the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) data for monitoring non-native gypsy moth (Lymantria dispar) defoliation of forests. This experiment compares defoliation detection products computed from simulated VIIRS and from MODIS (Moderate Resolution Imaging Spectroradiometer) time series products as potential inputs to a forest threat EWS (Early Warning System) being developed for the USFS (USDA Forest Service). Gypsy moth causes extensive defoliation of broadleaved forests in the United States and is specifically identified in the Healthy Forest Restoration Act (HFRA) of 2003. The HFRA mandates development of a national forest threat EWS. This system is being built by the USFS and NASA is aiding integration of needed satellite data products into this system, including MODIS products. This RPC experiment enabled the MODIS follow-on, VIIRS, to be evaluated as a data source for EWS forest monitoring products. The experiment included 1) assessment of MODIS-simulated VIIRS NDVI products, and 2) evaluation of gypsy moth defoliation mapping products from MODIS-simulated VIIRS and from MODIS NDVI time series data. This experiment employed MODIS data collected over the approximately 15 million acre mid-Appalachian Highlands during the annual peak defoliation time frame (approximately June 10 through July 27) during 2000-2006. NASA Stennis Application Research Toolbox software was used to produce MODIS-simulated VIIRS data and NASA Stennis Time Series Product Tool software was employed to process MODIS and MODIS-simulated VIIRS time series data scaled to planetary reflectance. MODIS-simulated VIIRS data was assessed through comparison to Hyperion-simulated VIIRS data using data collected during gypsy moth defoliation. Hyperion-simulated MODIS data showed a high correlation with actual MODIS data (NDVI R2 of 0.877 and RMSE of 0.023). MODIS-simulated VIIRS data for the same date showed moderately high correlation with Hyperion-simulated VIIRS data (NDVI R2 of 0.62 and RMSE of 0.035), even though the datasets were collected about a half an hour apart during changing weather conditions. MODIS products (MOD02, MOD09, and MOD13) and MOD02-simulated VIIRS time series data were used to generate defoliation mapping products based on image classification and image differencing change detection techniques. Accuracy of final defoliation mapping products was assessed by image interpreting over 170 randomly sampled locations found on Landsat and ASTER data in conjunction with defoliation map data from the USFS. The MOD02-simulated VIIRS 400-meter NDVI classification produced a similar overall accuracy (87.28 percent with 0.72 Kappa) to the MOD02 250-meter NDVI classification (86.71 percent with 0.71 Kappa). In addition, the VIIRS 400-meter NDVI, MOD02 250-meter NDVI, and MOD02 500-meter NDVI showed good user and producer accuracies for the defoliated forest class (70 percent) and acceptable Kappa values (0.66). MOD02 and MOD02-simulated VIIRS data both showed promise as data sources for regional monitoring of forest disturbance due to insect defoliation.

Spruce, Joseph P.↗
NASA NTRS1988

A relational data-knowledge base system and its potential in developing a distributed data-knowledge system

A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.

Rahimian, Eric N.↗
NASA NTRS1986

Analysis of forest structure using thematic mapper simulator data

The potential of Thematic Mapper Simulator (TMS) data for sensing forest structure information has been explored by principal components and feature selection techniques. In a survey of forest structural properties conducted for 123 field sites of the Sequoia National Park, the canopy closure could be well estimated (r = 0.62 to 0.69) by a variety of channel bands and band ratios, without reference to the forest type. Estimation of the basal area was less successful (r = 0.51 or less) on the average, but could be improved for certain forest types when data were stratified by floristic composition. To achieve such a stratification, individual sites were ordinated by a detrended correspondence analysis based on the canopy of dominant species. The analysis of forest structure in the Sequoia data suggests that total basal area can be best predicted in stands of lower density, and in younger even-aged managed stands.

Peterson, D. L.↗
NASA NTRS2007

Assessing Potential of VIIRS Data for Contribution to a Forest Threat Early Warning System

This viewgraph presentation reviews the contributions by the Rapid Prototyping Capability (RPC) towards using Visible Infrared Imager / Radiometer Suite (VIIRS) data in assessing the damage to forests. The Healthy Forest Restoration Act of 2003 mandates development of national Early Warning System (EWS) for forest threat monitoring and mitigation. NASA Stennis is working with the US Forest Service to develop needed components of this EWS. The use of MODIS data for monitoring forest disturbance at broad regional scales is a componet of this program. This RPC experiment was initiated to assess potential of the MODIS follow-on, VIIRS, for monitoring forest disturbance at broad scales and thereby contributing to the EWS. This presentation reviews the potential use of the VIIRS to examine the damage to forests caused by gyspy moths in the West Virginia and Virginia area.

Spruce, Joseph P.↗
DOE OSTI2019

Machine Learning Classical Interatomic Potentials for Molecular Dynamics from First-Principles Training Data

The ever-increasing power of modern super-computers, along with the availability of highly scalable atomistic simulation codes, has begun to revolutionize predictive modeling of materials. In particular, molecular dynamics (MD) has led to breakthrough advances in diverse fields, including tribology, catalysis, sensing, and nanoparticle self-assembly. Furthermore, recent integration of MD simulations with X-ray characterization has demonstrated promise in real-time 3-D characterization of materials on the atomic scale. The popularity of MD is driven by its applicability at disparate length/time scales, ranging from ab initio MD (hundreds of atoms and tens of picoseconds) to all-atom classical MD (millions of atoms over microseconds), and coarse-grained (CG) models (micrometers and tens of microseconds). Nevertheless, a substantial gap persists between AIMD, which is highly accurate but restricted to extremely small sizes, and those based on classical force fields (atomistic and CG) with limited accuracy but access to larger length/time scales. The accuracy and predictive power of classical MD simulations is dictated by the empirical force fields, and their capability to capture the relevant physics. Here, we discuss some of our recent work on the use of machine learning (ML) to combine the accuracy and flexibility of electronic structure calculations with the speed of classical potentials. Our ML framework attempts to bridge the significant gulf that exists between the handful of research groups that develop new interatomic potential models (often requiring several years of effort), and the increasingly large user community from academia and industry that applies these models. Our data-driven approach represents significant departure from the status quo and involves several steps including generation and manipulation of extensive training data sets through electronic structure calculations, defining novel potential functional forms, employing state-of-the-art ML algorithms to formulate highly optimized training procedures, and subsequently developing user-friendly workflow tools integrating these algorithms on high-performance computers (HPCs). Our ML approach shows marked success in developing force fields for a wide range of materials from metals, oxides, nitrides, and heterointerfaces to two-dimensional (2D) materials.

Chan, Henry↗
DOE OSTI2015

A measurement of the cosmic microwave background gravitational lensing potential from 100 square degrees of SPTPOL data

We present a measurement of the cosmic microwave background (CMB) gravitational lensing potential using data from the first two seasons of observations with SPTpol, the polarization-sensitive receiver currently installed on the South Pole Telescope. The observations used in this work cover 100 deg{sup 2} of sky with arcminute resolution at 150 GHz. Using a quadratic estimator, we make maps of the CMB lensing potential from combinations of CMB temperature and polarization maps. We combine these lensing potential maps to form a minimum-variance (MV) map. The lensing potential is measured with a signal-to-noise ratio of greater than one for angular multipoles between 100<L<250. This is the highest signal-to-noise mass map made from the CMB to date and will be powerful in cross-correlation with other tracers of large-scale structure. We calculate the power spectrum of the lensing potential for each estimator, and we report the value of the MV power spectrum between 100<L<2000 as our primary result. We constrain the ratio of the spectrum to a fiducial ΛCDM model to be A{sub MV} = 0.92 ± 0.14 (Stat.) ± 0.08 (Sys.). Restricting ourselves to polarized data only, we find A{sub POL} = 0.92 ± 0.24 (Stat.) ± 0.11 (Sys.). This measurement rejects the hypothesis of no lensing at 5.9σ using polarization data alone, and at 14σ using both temperature and polarization data.

79 ASTRONOMY AND ASTROPHYSICS↗