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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Accelerated alloy discovery using synthetic data generation and data mining

The search for new alloys with improved properties is never ending with infinite combinations and amounts of alloying elements in the alloy. Advancements in machine learning have made navigating this enormous search space feasible. However, training the machine learning models and tuning their hyper-parameters to make accurate predictions can be time-consuming and often require high-performance computing resources. Furthermore, the quality of the predictions depend on the availability of sufficient training data. Here, we present a generic approach to accelerate alloy discovery by coupling high throughput CALPHAD calculations, synthetic data generation, and data mining. Finally, as a demonstration of the approach, we design super bainitic steels that form bainite at 200 C in lower transformation times.

36 MATERIALS SCIENCE↗
DOE OSTI2012

THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE

The Blanco Cosmology Survey (BCS) is a 60 night imaging survey of {approx}80 deg{sup 2} of the southern sky located in two fields: ({alpha}, {delta}) = (5 hr, -55 Degree-Sign ) and (23 hr, -55 Degree-Sign ). The survey was carried out between 2005 and 2008 in griz bands with the Mosaic2 imager on the Blanco 4 m telescope. The primary aim of the BCS survey is to provide the data required to optically confirm and measure photometric redshifts for Sunyaev-Zel'dovich effect selected galaxy clusters from the South Pole Telescope and the Atacama Cosmology Telescope. We process and calibrate the BCS data, carrying out point-spread function-corrected model-fitting photometry for all detected objects. The median 10{sigma} galaxy (point-source) depths over the survey in griz are approximately 23.3 (23.9), 23.4 (24.0), 23.0 (23.6), and 21.3 (22.1), respectively. The astrometric accuracy relative to the USNO-B survey is {approx}45 mas. We calibrate our absolute photometry using the stellar locus in grizJ bands, and thus our absolute photometric scale derives from the Two Micron All Sky Survey, which has {approx}2% accuracy. The scatter of stars about the stellar locus indicates a systematic floor in the relative stellar photometric scatter in griz that is {approx}1.9%, {approx}2.2%, {approx}2.7%, and {approx}2.7%, respectively. A simple cut in the AstrOmatic star-galaxy classifier spread{sub m}odel produces a star sample with good spatial uniformity. We use the resulting photometric catalogs to calibrate photometric redshifts for the survey and demonstrate scatter {delta}z/(1 + z) = 0.054 with an outlier fraction {eta} < 5% to z {approx} 1. We highlight some selected science results to date and provide a full description of the released data products.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

Ultrascale Visualization of Climate Data

To support interactive visualization and analysis of complex, large-scale climate data sets, UV-CDAT integrates a powerful set of scientific computing libraries and applications to foster more efficient knowledge discovery. Connected through a provenance framework, the UV-CDAT components can be loosely coupled for fast integration or tightly coupled for greater functionality and communication with other components. This framework addresses many challenges in the interactive visual analysis of distributed large-scale data for the climate community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Next Generation Hydrogen Station Composite Data Products: Retail Stations (Data through Quarter 3 of 2019)

This publication includes 98 composite data products (CDPs) produced for next generation hydrogen stations, with data through the third quarter of 2019. These CDPs include data from retail stations only and represent the following analysis categories: deployment, safety, maintenance and reliability, performance, cost, utilization, hydrogen quality, and component energy.

08 HYDROGEN↗
DOE OSTI2015

Transportation Secure Data Center: Real-world Data for Planning, Modeling, and Analysis (Fact Sheet)

The Transportation Secure Data Center (TSDC) at www.nrel.gov/tsdc provides free, web-based access to detailed transportation data from a variety of travel surveys conducted across the nation. While preserving the privacy of survey participants, this online repository makes vital transportation data broadly available to users from the comfort of their own desks via a secure online connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Open Energy Data Initiative: Advancing Analytics and Research Innovation through Improved Data Access

The Open Energy Data Initiative (OEDI) aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy's (DOE's) programs, offices, and national laboratories. Sponsored by DOE, this platform is being implemented by the National Renewable Energy Laboratory (NREL) to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. Partners at other national laboratories will help utilize the platform for analysis, can be directly involved in determining requirements, and will be resources for providing new datasets that can be shared with the public to expand innovation.

14 SOLAR ENERGY↗
DOE OSTI2019

FAST FY 2018 Federal Fleet Data Call: Looking at Data Quality

This presentation provides an overview of fiscal year 2018 federal motor vehicle fleet data at the individual vehicle level during the fall of 2018, a preliminary summary of the data collected, how the the collecting project has reviewed that information for potential quality issues, and an overview of two key areas within the dataset that represent data quality issues. This presentation will be given at the April 2019 FedFleet training event, hosted by the US General Services Administration in Washington, DC. The information is collected through the Federal Automotive Statistical Tool (FAST) project. FAST is a Web-based information system managed by the US Department of Energy, the US General Services Administration, and the Energy Information Administration. FAST is used to collect information about the fleet of motor vehicles used and managed by the Federal government. FAST is developed and maintained by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC

This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Nuclear Data Sheets for A = 112

Evaluated nuclear structure and decay data for all nuclei within the A = 112 mass chain are presented. The experimental data are evaluated and best values for level and gamma-ray energies, quantum numbers, lifetimes, gamma-ray intensities, and other nuclear properties are recommended. Inconsistencies and discrepancies that exist in the literature are noted. This work supersedes the earlier evaluation by D. De Frenne and E. Jacobs (1996De55), published in Nuclear Data Sheets79, 639 (1996)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Nuclear Data Sheets for A = 87

The evaluated experimental data are presented for 14 known nuclides of mass 87 (Ga, Ge, As, Se, Br, Kr, Rb, Sr, Y, Zr, Nb, Mo, Tc, Ru). New datasets have been added for {sup 87}Ga, {sup 87}Se, {sup 87}Kr, {sup 87}Rb, {sup 87}Sr, {sup 87}Nb, and {sup 87}Tc. The observation of and new lifetime measurements for {sup 87}As at the NSCL Coupled Cyclotron Facility and the Oak Ridge Isochronous Cyclotron at the HRIBF–ORNL Facility are included in the adopted levels for this isotope. The new evaluation also includes an isomeric level at unknown energy for {sup 87}Tc, changes to the adopted levels for {sup 87}Nb based on newly published data including an extension of the high spin levels and new lifetime measurements, and new heavy ion data for {sup 87}Kr. New high spin levels for {sup 87}Rb were observed, indicating possible neutron core excitations. In addition, the ground state lifetime of {sup 87}Rb has been revised and a new lifetime measurement for an excited level in {sup 87}Rb indicates a possible proton spin flip. New excitation levels have been observed for the first time in {sup 87}Se. New direct and precise measurement of atomic masses of {sup 87}Tc, {sup 87}Mo, {sup 87}Nb, {sup 87}Zr, {sup 87}Mo and {sup 87}Tc have greatly improved the Q value landscape in this mass region. This work supersedes the previous A=87 evaluation published in 2002He09, and Q values for all isotopes were updated to incorporate the mass evaluation in 2012Wa38.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2015

Nuclear Data Sheets for A = 224

The experimental nuclear spectroscopic data for ten experimentally known nuclides of mass number 224 (Bi, Po, At, Rn, Fr, Ra, Ac, Th, Pa, U) have been evaluated and presented together with the Adopted properties for levels and γ rays. For {sup 224}Bi, {sup 224}Po, {sup 224}At, {sup 224}Rn, {sup 224}Fr, {sup 224}Pa and {sup 224}U nuclei, only the ground–state information is known. Half–lives of ground states of 224Bi and 224Po have not yet been measured. The decay of {sup 224}Rn to {sup 224}Fr has been studied, but no decay scheme has been proposed. The decay of {sup 224}Ac to {sup 224}Ra is reported only in an abstract style conference proceedings. For a definite Jπ assignment for ground state of {sup 224}Ac, a detailed study of this decay may be helpful. In the decay of {sup 228}Pa to {sup 224}Ac, authors of 1994Ah03 state that the lowest state observed in their work may not be the ground state. Thus an extensive study of {sup 228}Pa α decay, and studies involving nuclear reactions are warranted since the level scheme of {sup 224}Ac presented in this evaluation is considered as incomplete due to the lack of confirmed information about multipolarities, mixing ratios and other spectroscopic details. Three new nuclides {sup 224}Bi, {sup 224}Po, and {sup 224}At are added in this revision, together with new in–beam γ–ray and Coulomb excitation data for {sup 224}Ra. Although, not much new experimental data have appeared since the previous evaluation (1997Ar05), extensive modifications have been made in almost all the datasets. This work supersedes the information presented in the previous NDS evaluations of A = 224: 1997Ar05, 1986Ma45 and 1976El05.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Nuclear Data Sheets for A = 79

Nuclear spectroscopic information for known nuclides of mass number 79 (Ni,Cu,Zn,Ga,Ge,As,Se,Br,Kr,Rb, Sr,Y,Zr) has been evaluated and presented together with adopted energies, Jπ, and decay modes of levels in these nuclei. No data are yet available for excited states in {sup 79}Ni, {sup 79}Cu and {sup 79}Zr. The half–life of {sup 79}Se, a nuclide of importance in reactor–irradiated fuel composition has been measured by several independent groups and reported in a number of publications after 1995, and seems to be converging to a narrow margin of experimental values. These measurements were likely prompted by a recommendation made in the 1993 update of A = 79 nuclides (1993Si28), that since a 1949PaZZ report, there had been no measurement until 1993, and that based on simulation studies by 1993HeZW, the value listed in 1949PaZZ, and cited in all data tables and charts for 44 years, was quite likely in error, being too low by a factor of 10. According to conclusions in recent papers such as 2014Do20, there is still room for improvement in the measurement of this half–life, and further experiments are expected. This evaluation supersedes earlier Nuclear Data Sheets for A = 79 (2002Si13,1993Si28, 1982Si21,1975Ur03).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Nuclear Data Sheets for A=40

The experimental nuclear structure data and decay data are evaluated for the known nuclides of mass 40 (Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti). Detailed evaluated nuclear structure information is presented with the best values recommended for level energies, half-lives, γ-ray energies and intensities, decay properties (energies, intensities and placement of radiations), and other spectroscopic data. The {sup 40}Ca and {sup 40}K nuclides remain as the most extensively studied from many different reactions and decays; no excited states are known in {sup 40}Mg, {sup 40}Al, {sup 40}P and {sup 40}Ti. This work supersedes the earlier full evaluation of A=40 by J. Cameron and B. Singh (2004Ca38).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Statistical data analysis of x-ray spectroscopy data enabled by neural network accelerated Bayesian inference

Bayesian inference applied to x-ray spectroscopy data analysis enables uncertainty quantification necessary to rigorously test theoretical models. However, when comparing to data, detailed atomic physics and radiation transfer calculations of x-ray emission from non-uniform plasma conditions are typically too slow to be performed in line with statistical sampling methods, such as Markov Chain Monte Carlo sampling. Furthermore, differences in transition energies and x-ray opacities often make direct comparisons between simulated and measured spectra unreliable. Here, we present a spectral decomposition method that allows for corrections to line positions and bound–bound opacities to best fit experimental data, with the goal of providing quantitative feedback to improve the underlying theoretical models and guide future experiments. In this work, we use a neural network (NN) surrogate model to replace spectral calculations of isobaric hot-spots created in Kr-doped implosions at the National Ignition Facility. The NN was trained on calculations of x-ray spectra using an isobaric hot-spot model post-processed with Cretin, a multi-species atomic kinetics and radiation code. The speedup provided by the NN model to generate x-ray emission spectra enables statistical analysis of parameterized models with sufficient detail to accurately represent the physical system and extract the plasma parameters of interest.

47 OTHER INSTRUMENTATION↗