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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Molecular dynamics simulation of effects of solutes on dislocation propagation in Ni-based superalloys

Ni-based superalloys are used in the hot sections of jet turbine engines because of their high strength, stability and resistance to oxidation. The properties of these alloys can be further optimized by adding alloying elements. Therefore, a fundamental knowledge on the effect of different elements on properties of Ni-based superalloys is required. Molecular dynamics simulation could shed light here but its application is hindered by the absence of reliable and computationally cheap semi-empirical potential of the interatomic interaction for 4 and more element alloys. We will present a new Ni-Al-Cr-Nb Finnis-Sinclair (FS) potential specially designed to simulate the dislocation propagation from to  phase. In order to construct this potential, we designed a special algorithm to incorporate the data on element partitioning in the potential development procedure. For example, it is known from experiment, that Cr is mostly present in the gamma phase. Figure 1 shows a snapshot obtained after equilibration of the model of the Ni68Al17Cr15 alloy at T=1000 K using the hybrid Monte-Carlo (MC)/molecular dynamics (MD) simulation with the developed semi-empirical potential. One can clearly see that the Cr partitioning is in agreement with the experimental data. We will discuss the developed algorithm to incorporate the solute partition data in details. Using the developed semi-empirical potential, we first investigated the effect of anti-site defects in the  phase on the single dislocation propagation. It was found that the dislocation velocity increases with the increasing of the anti-site defect concentration. This effect was attributed to smaller number of Al-Al pairs forming during the dislocation migration in the presence of the anti-site defects. Next, we investigated the effect of Nb on the dislocation pair propagation in the Kolbe mechanism. It was found that the addition of Nb leads to considerable decrease in the dislocation propagation rate. This is in agreement with the experimental data on the effect of Nb on the creep resistance of the Ni-based superalloys. We will discuss the origin of this effect.

Mikhail I. Mendelev↗
NASA NTRS1988

Assessing forest damage in high-elevation coniferous forests in Vermont and New Hampshire using Thematic Mapper data

This study evaluates the potential of measuring/mapping forest damage in spruce-fir forests in the Green Mountains of Vermont and White Mountains of New Hampshire using Landsat Thematic Mapper (TM) data. The TM 1.65/0.83-micron (TM5/4) and 2.22/0.83-micron (TM7/4) band ratios were found to correlate well with ground-based measurements of forest damage (a measure of percentage foliar loss) at 11 spruce-fir stands located on Camels Hump, a mountain in northern Vermont. Images using 0.56 and 1.65-micron bands with 1.65/0.83-micron band ratios indicated locations of heavy conifer forest damage. Both 1.65/0.83 and 2.22/0.83-micron band ratios were used to quantify levels of conifer forest damage among individual mountains throughout many of the Green and White Mountains. Damage was found to be consistently higher for the Green than the White Mountains.

Vogelmann, James E.↗
NASA NTRS1996

Venusian k(sub 2) Tidal Love Number from Magellan and PVO Tracking Data

The k(sub 2) potential ove number which scales the tidal deformation of Venus by the Sun has been estimated from Doppler tracking of Magellan and Pioneer Venus Orbiter (PVO) spacecraft data. The nominal range for k(sub 2) from theoretical models is 0.23(less than or equal to)k(sub 2)(less than or equal to)0.29 for a liquid iron core and about 0.17 if the iron core has solidified. Our best estimate of this parameter is k(sub 2) = 0.295 +/- 0.662 (2X formal {delta}) and supports the hypothesis that Venus core is solid.

orbiter pvo↗
DOE OSTI2016

Full empirical potential curves for the X{sup 1}Σ{sup +} and A{sup 1}Π states of CH{sup +} from a direct-potential-fit analysis

All available “conventional” absorption/emission spectroscopic data have been combined with photodissociation data and translational spectroscopy data in a global analysis that yields analytic potential energy and Born-Oppenheimer breakdown functions for the X{sup 1}Σ{sup +} and A{sup 1}Π states of CH{sup +} and its isotopologues that reproduce all of the data (on average) within their assigned uncertainties. For the ground X{sup 1}Σ{sup +} state, this fully quantum mechanical “Direct-Potential-Fit” analysis yielded an improved empirical well depth of D{sub e} = 34 362.8(3) cm{sup −1} and equilibrium bond length of r{sub e} = 1.128 462 5 (58) Å. For the A{sup 1}Π state, the resulting well depth and equilibrium bond length are D{sub e} = 10 303.7(3) cm{sup −1} and r{sub e} = 1.235 896 (14) Å, while the electronic isotope shift from the hydride to the deuteride is ΔT{sub e} = − 5.99(±0.08) cm{sup −1}.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
DOE OSTI2016

A global reaction route mapping-based kinetic Monte Carlo algorithm

We propose a new on-the-fly kinetic Monte Carlo (KMC) method that is based on exhaustive potential energy surface searching carried out with the global reaction route mapping (GRRM) algorithm. Starting from any given equilibrium state, this GRRM-KMC algorithm performs a one-step GRRM search to identify all surrounding transition states. Intrinsic reaction coordinate pathways are then calculated to identify potential subsequent equilibrium states. Harmonic transition state theory is used to calculate rate constants for all potential pathways, before a standard KMC accept/reject selection is performed. The selected pathway is then used to propagate the system forward in time, which is calculated on the basis of 1st order kinetics. The GRRM-KMC algorithm is validated here in two challenging contexts: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle. We demonstrate that in both cases the GRRM-KMC method is capable of reproducing the 1st order kinetics observed during independent quantum chemical molecular dynamics simulations using the density-functional tight-binding potential.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Predicting weather impacts on corn production in a data-limited region using a transfer learning approach

The stability of food supply and prices may depend more on annual changes in yields from year-to-year variability in weather than on longer-term average changes from changing climatic conditions. However, the absence of high-quality data on crop yields at fine spatial resolutions in many regions of the world makes it challenging to statistically model their response to interannual variability in weather patterns. Therefore, there is a need for empirical methods that can project annual crop yield changes even in limited data regions. Here, we propose a transfer learning algorithm that uses high spatial resolution data from one region to project yields in another region with more limited data. The goal of our work is to understand what data types can be beneficial for transferring learning from a source region to a very different target region with more limited data. We utilize Long Short-Term Memory to develop a transfer learning model that is trained on historical county-level corn yield in the United States and predicts district-level corn yield variations in India. Even using smaller amounts of data in India, simulating a data-scarce region, we achieve an average root mean square error of 0.48 bu acre−1 in predicting interannual yield variations. Using Shapley values to interpret results, we explore the contribution of the different weather parameters to interannual yield variability and find a larger influence of precipitation-related variables. Our study demonstrates the usefulness of this method for transferring models of weather impacts on crop yields trained on a data-rich country to one with more limited data. It suggests the potential of applying the transfer learning model to mitigate the need for extensive raw data globally.

Vishwakarma, Srishti [ORNL] (ORCID:000000031674419↗
DOE OSTI2023

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗
DOE OSTI2015

Statistical Analysis of Demographic and Temporal Differences in LANL's 2014 Voluntary Protection Program Survey

Voluntary Protection Program (VPP) surveys were conducted in 2013 and 2014 to assess the degree to which workers at Los Alamos National Laboratory feel that their safety is valued by their management and peers. The goal of this analysis is to determine whether the difference between the VPP survey scores in 2013 and 2014 is significant, and to present the data in a way such that it can help identify either positive changes or potential opportunities for improvement. Data for several questions intended to identify the demographic groups of the respondent are included in both the 2013 and 2014 VPP survey results. These can be used to identify any significant differences among groups of employees as well as to identify any temporal trends in these cohorts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

​Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset

Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. Additionally, we tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2012

Synegies Between Visible/Near-Infrared Imaging Spectrometry and the Thermal Infrared in an Urban Environment: An Evaluation of the Hyperspectral Infrared Imager (HYSPIRI) Mission

A majority of the human population lives in urban areas and as such, the quality of urban environments is becoming increasingly important to the human population. Furthermore, these areas are major sources of environmental contaminants and sinks of energy and materials. Remote sensing provides an improved understanding of urban areas and their impacts by mapping urban extent, urban composition (vegetation and impervious cover fractions), and urban radiation balance through measures of albedo, emissivity and land surface temperature (LST). Recently, the National Research Council (NRC) completed an assessment of remote sensing needs for the next decade (NRC, 2007), proposing several missions suitable for urban studies, including a visible, near-infrared and shortwave infrared (VSWIR) imaging spectrometer and a multispectral thermal infrared (TIR) instrument called the Hyperspectral Infrared Imagery (HyspIRI). In this talk, we introduce the HyspIRI mission, focusing on potential synergies between VSWIR and TIR data in an urban area. We evaluate potential synergies using an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and MODIS-ASTER (MASTER) image pair acquired over Santa Barbara, United States. AVIRIS data were analyzed at their native spatial resolutions (7.5m VSWIR and 15m TIR), and aggregated 60 m spatial resolution similar to HyspIRI. Surface reflectance was calculated using ACORN and a ground reflectance target to remove atmospheric and sensor artifacts. MASTER data were processed to generate estimates of spectral emissivity and LST using Modtran radiative transfer code and the ASTER Temperature Emissivity Separation algorithm. A spectral library of common urban materials, including urban vegetation, roofs and roads was assembled from combined AVIRIS and field-measured reflectance spectra. LST and emissivity were also retrieved from MASTER and reflectance/emissivity spectra for a subset of urban materials were retrieved from co-located MASTER and AVIRIS pixels. Fractions of Impervious, Soil, Green Vegetation (GV) and Non-photosynthetic Vegetation (NPV), were estimated using Multiple Endmember Spectral Mixture Analysis (MESMA) applied to AVIRIS data at 7.5, 15 and 60 m spatial scales. Surface energy parameters, including albedo, vegetation cover fraction, broadband emissivity and LST were also determined for urban and natural land-cover classes in the region. Fractions were validated using 1m digital photography.

Roberts, Dar A.↗
NASA NTRS1980

Shuttle/payload communications and data systems interface analysis: Executive summary

Potential interface compatibility problems associated with interfacing OFT payload elements with orbiter communications and data handling elements that would result from payload or orbit failure to meet interface requirements contained in the orbiter/cargo standard interface specification are identified. The potential interface compatibility problems can be associated with either attached payloads in the payload bay or detached payloads communicating with the orbiter via an RF link. Evaluation of the attached payload orbiter interfaces was made by investigating interface problem areas experienced between the IUS and the orbiter since similar problems could be expected for other payloads.

Source record↗
NASA NTRS1991

Neuro-classification of multi-type Landsat Thematic Mapper data

Neural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.

Zhuang, Xin↗
DOE OSTI2018

City Energy: From Data to Decisions. Hutchinson, Minnesota: Evaluating Local Solar Energy Generation Potential

The City of Hutchinson, Minnesota, partnered with the Energy Department and the National Renewable Energy Laboratory (NREL) to demonstrate how data and analysis can inform more strategic energy decisions. NREL based its analysis in part on the City Energy Profiles on the State and Local Energy Data (SLED) website (eere.energy.gov/sled). The profiles contain data compiled by SLED and the Cities Leading through Energy Analysis and Planning (Cities-LEAP) program. Cities across the country can follow the same approach and use data-driven analysis in their own energy planning.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Experimental evidence for the formation of CoFe{sub 2}C phase with colossal magnetocrystalline-anisotropy

Attainment of magnetic order in nanoparticles at room temperature is an issue of critical importance for many different technologies. For ordinary ferromagnetic materials, a reduction in size leads to decreased magnetic anisotropy and results in superparamagnetic relaxations. If, instead, anisotropy could be enhanced at reduced particle sizes, then it would be possible to attain stable magnetic order at room temperature. Herein, we provide experimental evidence substantiating the synthesis of a cobalt iron carbide phase (CoFe{sub 2}C) of nanoparticles. Structural characterization of the CoFe{sub 2}C carbide phase was performed by transmission electron microscopy, electron diffraction and energy electron spectroscopy. X-ray diffraction was also performed as a complimentary analysis. Magnetic characterization of the carbide phase revealed a blocking temperature, T{sub B}, of 790 K for particles with a domain size as small as 5 ± 1 nm. The particles have magnetocrystalline anisotropy of 4.6 ± 2 × 10{sup 6 }J/m{sup 3}, which is ten times larger than that of Co nanoparticles. Such colossal anisotropy leads to thermally stable long range magnetic order. Moreover, the thermal stability constant is much larger than that of the commonly used FePt nanoparticles. With thermal stability and colossal anisotropy, the CoFe{sub 2}C nanoparticles have huge potential for enhanced magnetic data storage devices.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Sap Velocity, Leaf-level Measurements, and Meteorological Data in Manaus and Santarem, Plant Science, Brazil: 2015-2017

These datasets were used in the paper Gimenez et al., 2019 (Species-Specific Shifts in Diurnal Sap Velocity Dynamics and Hysteretic Behavior of Ecophysiological Variables During the 2015–2016 El Niño Event in the Amazon Forest) published in Frontiers in Plant Science, special issue: Water-Use Efficiency: Advances and Challenges in a Changing Climate. This data package contains data from ICT sap flow sensors, leaf temperature (Infrared Sensors), leaf gas exchange, leaf water potential and leaf thermocouple data used for some experiments of the NGEE-Tropics project in the Amazon Forest (sites: BR-Ma2 and BR-Sa1). The data package also includes environmental data (Relative humidity, air temperature, and VPD) collected using thermo-hygrometers installed in K-34 tower vertical profile (Manaus - BR-Ma2).

54 ENVIRONMENTAL SCIENCES↗