Search NASA⌕ Search

SEARCH · Search NASA

Results for “source selection”

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.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Ecological Benchmark for Chemicals

The Ecological Benchmark Tool dataset serves as a comprehensive repository of benchmarks designed to assess ecological risks at contaminated sites. This tool facilitates the evaluation of various environmental media and contaminants, supporting regulatory compliance and ecological protection. Benchmarks are available for air, biota, sediment, soil, and surface water. The dataset also provides species-specific benchmarks for fish, plants, birds, mammals, and invertebrates. Users can select benchmark sources, media, individual chemicals, and retrieve results in tabular or spreadsheet formats for analysis. Benchmarks are derived from authoritative sources, including government agencies, scientific councils, and academic publications. The dataset supports ecological risk assessments, regulatory decision-making, and environmental planning, with tools for benchmarking against chemical thresholds, sensitive species protection, and habitat impact evaluations. This structured approach ensures a robust evaluation of ecological risks tailored to site-specific and regulatory needs.

Stewart, Debra [Oak Ridge National Laboratory (ORN↗
DOE OSTI2024

Ecological Benchmark for Radionuclides

The Ecological Benchmark Tool for radionuclides dataset serves as a comprehensive repository of benchmarks designed to assess ecological risks at contaminated sites. This tool facilitates the evaluation of various environmental media and contaminants, supporting regulatory compliance and ecological protection. Benchmarks are available for sediment, soil, and surface water. The dataset also provides species-specific benchmarks for fish, plants, birds, mammals, and invertebrates. Users can select benchmark sources, media, individual radionuclides, and retrieve results in tabular or spreadsheet formats for analysis. Benchmarks are derived from authoritative sources, including government agencies, scientific councils, and academic publications. The dataset supports ecological risk assessments, regulatory decision-making, and environmental planning, with tools for benchmarking against radiological thresholds, sensitive species protection, and habitat impact evaluations. This structured approach ensures a robust evaluation of ecological risks tailored to site-specific and regulatory needs.

Stewart, Debra [Oak Ridge National Laboratory (ORN↗
DOE OSTI2017

Ultra-Low Noise Germanium Neutrino Detection system (ULGeN).

Monitoring nuclear power plant operation by measuring the antineutrino flux has become an active research field for safeguards and non-proliferation. We describe various efforts to demonstrate the feasibility of reactor monitoring based on the detection of the Coherent Neutrino Nucleus Scattering (CNNS) process with High Purity Germanium (HPGe) technology. CNNS detection for reactor antineutrino energies requires lowering the electronic noise in low-capacitance kg-scale HPGe detectors below 100 eV as well as stringent reduction in other particle backgrounds. Existing state- of-the-art detectors are limited to an electronic noise of 95 eV-FWHM. In this work, we employed an ultra-low capacitance point-contact detector with a commercial integrated circuit preamplifier- on-a-chip in an ultra-low vibration mechanically cooled cryostat to achieve an electronic noise of 39 eV-FWHM at 43 K. We also present the results of a background measurement campaign at the Spallation Neutron Source to select the area with sufficient low background to allow a successful first-time measurement of the CNNS process.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Hydrogen Risk Assessment Models (HyRAM) (V.3.1) (Technical Reference Manual)

The HyRAM software toolkit provides a basis for conducting quantitative risk assessment and consequence modeling for hydrogen infrastructure and transportation systems. HyRAM is designed to facilitate the use of state-of-the-art science and engineering models to conduct robust, repeatable assessments of hydrogen safety, hazards, and risk. HyRAM includes generic probabilities for hydrogen equipment failures, probabilistic models for the impact of heat flux on humans and structures, and experimentally validated first-order models of hydrogen release and flame physics. HyRAM integrates deterministic and probabilistic models for quantifying accident scenarios, predicting physical effects, and characterizing hydrogen hazards (thermal effects from jet res, overpressure effects from deflagrations), and assessing impact on people and structures. HyRAM is developed at Sandia National Laboratories for the U.S. Department of Energy to increase access to technical data about hydrogen safety and to enable the use of that data to support development and revision of national and international codes and standards. HyRAM is a research software in active development and thus the models and data may change. This report will be updated at appropriate developmental intervals. This document provides a description of the methodology and models contained in HyRAM version 3.1. There have been several impactful updates since version 3.0. HyRAM 3.1 contains a correction to use the volume fraction for two-phase speed of sound calculations; this only affects cryogenic releases in which two-phase ow (vapor and liquid) is predicted in the orifice. Other changes include clarifications that inputs for tank pressure should be given in absolute pressure, not gauge pressure. Additionally, the interface now rejects invalid inputs to probability distributions, and the less accurate single-point radiative source model selection was removed from the interface.

08 HYDROGEN↗
DOE OSTI2023

The Prospects for Pumped Storage Hydropower in Alaska

Key Takeaways: The resource mapping analysis confirmed that numerous locations in Alaska are suitable for the development of pumped storage hydropower (PSH) projects, both larger grid scale projects and smaller projects that could be suitable for remote communities; The resource assessment for larger, grid-scale projects showed the potential for more than 1,800 closed-loop systems in Alaska, with a total energy storage capacity of about 4 terawatt hours (TWh); Because of their small reservoir sizes and dam heights, many locations were identified as potentially suitable for small-scale PSH systems. Nearly 50% of the identified potentially suitable small-scale PSH sites are in Southeast Alaska; PSH candidate sites were part of the optimal capacity expansion solution in all scenarios analyzed for the Railbelt system. Depending on the scenario, the new PSH capacity that the model selected for the analysis period until 2046 ranged from 300 MW to 600 MW. The locations and timing of new PSH investments vary in different scenarios; Lithium-ion batteries were also selected a source of new generating capacity in all analyzed scenarios for the Railbelt system, indicating that the system will need a mix of short- and long-duration energy storage to support variable renewable energy sources and provide system reliability in the future; For rural communities, analysis results showed that PSH suitability is very site-specific; in addition to diesel fuel costs and PSH capital costs, suitability depends heavily on available renewable resources and existing infrastructure (e.g., reservoirs, transmission access and construction road access); The analysis for rural communities also showed that PSH projects with 10-hour energy storage are likely to be more economical for remote community applications in Alaska than those with larger reservoirs that could provide 10 days of energy storage; Lithium-ion batteries seem to be an economically more viable energy storage option for small, remote communities in Alaska.

13 HYDRO ENERGY↗
DOE OSTI2020

Microanalytical Approaches to Characterizing REE in Appalachian Basin Underclays

The search for a reliable U.S. domestic source of rare earth elements (REE) is necessary to support the demand of advanced energy applications (e.g., catalysts, electronics, magnets). Sedimentary deposits may be sources for selectively recovering REE and critical metals—specifically the interbedded seat rock, or underclay, that underlies or forms the floor of a coal seam. This material is often a major component of coal waste fines and refuse and thus readily available. This study examines several Appalachian Basin underclays associated with actively mined coal seams as potential feedstocks for the REE. Multimodal microanalytical electron microscopy (SEM, FIB-SEM, EMPA) synchrotron-based µXRF, and image processing techniques are coupled with detailed elemental and mineral data to classify the 2D and 3D petrophysical properties of the materials. The REE contents of Appalachian Basin underclays were measured from 235–399 ppm and predominantly observed as discrete REE-bearing minerals such as monazite and xenotime on the order of 10–100 µm in size. These REE-bearing minerals typically accounted for less than 1% of the scanned areas and volumes under SEM and FIB-SEM analysis, with the exception of regions enriched in crandallite. Synchrotron-based µXRF elemental maps further identified several REE deposition environments in different underclays, including micro-scale (10–100 µm) light REEs co-localizing with Ca and P, micro-scale heavy REEs with Fe, and large-scale light REEs (>200 µm) co-localizing with Sr, Ba, Ca and P.

36 MATERIALS SCIENCE↗
DOE OSTI2016

STAR FORMATION RELATIONS IN THE MILKY WAY

The relations between star formation and properties of molecular clouds (MCs) are studied based on a sample of star-forming regions in the Galactic Plane. Sources were selected by having radio recombination lines to provide identification of associated MCs and dense clumps. Radio continuum emission and mid-infrared emission were used to determine star formation rates (SFRs), while {sup 13}CO and submillimeter dust continuum emission were used to obtain the masses of molecular and dense gas, respectively. We test whether total molecular gas or dense gas provides the best predictor of SFR. We also test two specific theoretical models, one relying on the molecular mass divided by the free-fall time, the other using the free-fall time divided by the crossing time. Neither is supported by the data. The data are also compared to those from nearby star-forming regions and extragalactic data. The star formation “efficiency,” defined as SFR divided by mass, spreads over a large range when the mass refers to molecular gas; the standard deviation of the log of the efficiency decreases by a factor of three when the mass of relatively dense molecular gas is used rather than the mass of all of the molecular gas.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

The SCUBA-2 Cosmology Legacy Survey: Multi-wavelength Properties of ALMA-identified Submillimeter Galaxies in UKIDSS UDS

We present a multi-wavelength analysis of 52 submillimeter galaxies (SMGs), identified using ALMA 870 μm continuum imaging in a pilot program to precisely locate bright SCUBA-2-selected submillimeter sources in the UKIDSS Ultra Deep Survey (UDS) field. Using the available deep (especially near-infrared) panoramic imaging of the UDS field at optical-to-radio wavelengths we characterize key properties of the SMG population. The median photometric redshift of the bright ALMA/SCUBA-2 UDS (AS2UDS) SMGs that are detected in a sufficient number of wavebands to derive a robust photometric redshift is z = 2.65 ± 0.13. However, similar to previous studies, 27% of the SMGs are too faint at optical-to-near-infrared wavelengths to derive a reliable photometric redshift. Assuming that these SMGs lie at z ≳ 3 raises the median redshift of the full sample to z = 2.9 ± 0.2. A subset of 23 unlensed, bright AS2UDS SMGs have sizes measured from resolved imaging of their rest-frame far-infrared emission. We show that the extent and luminosity of the far-infrared emission are consistent with the dust emission arising from regions that are, on average, optically thick at a wavelength of λ{sub 0}⩾75 μm (1σ dispersion of 55–90 μm). Using the dust masses derived from our optically thick spectral energy distribution models, we determine that these galaxies have a median hydrogen column density of N {sub H} = 9.8{sub −0.7}{sup +1.4} × 10{sup 23} cm{sup −2}, or a corresponding median V-band obscuration of A {sub v} = 540{sub −40}{sup +80} mag, averaged along the line of sight to the source of their rest-frame ∼200 μm emission. We discuss the implications of this extreme attenuation by dust for the multi-wavelength study of dusty starbursts and reddening-sensitive tracers of star formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright ( m peak ≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We present BTSbot , a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features. BTSbot is able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates. BTSbot recovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find that BTSbot is not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates. BTSbot has been integrated into Fritz and Kowalski , ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May, BTSbot selected 609 sources in real time, 96% of which were real extragalactic transients. With BTSbot and other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan.

Rehemtulla, Nabeel (ORCID:0000000256832389)↗
DOE OSTI2018

Measurements of Scatter Peaks in 137 Cs and 60 Co Sources

Results from tests of radiation detection instruments with radionuclide identification capabilities will depend on the sources used for the tests. Radionuclide identification detectors are designed to measure photons and provide an identification of the source being measured. High-resolution spectra need to be acquired to determine all the observable peaks in the source spectra before testing these types of instruments. These peaks may be due to impurities and/or scatter peaks in the sources. This paper discusses the issues encountered with the response of a radioisotope identification device due to scatter peaks in one type of source used for testing. In addition, it provides spectra for different source types and source constructions to compare the differences in scatter, allowing for a better source type selection for instrument testing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Electroactive materials for rechargeable batteries

A secondary battery including a cathode having a primary cathode active material and an alkaline source material selected from the group consisting of Li.sub.2O, Li.sub.2O.sub.2, Li.sub.2S, LiF, LiCl, Li.sub.2Br, Na.sub.2O, Na.sub.2O.sub.2, Na.sub.2S, NaF, NaCl, and a mixture of any two or more thereof; an anode having an anode active material; an electrolyte; and a separator.

25 ENERGY STORAGE↗
DOE OSTI2018

Quantum random number generators

Random number generators include a thermal optical source and detector configured to produce random numbers based on quantum-optical intensity fluctuations. An optical flux is detected, and signals proportional to optical intensity and a delayed optical intensity are combined. The combined signals can be electrical signals or optical signals, and the optical source is selected so as to have low coherence over a predetermined range of delay times. Balanced optical detectors can be used to reduce common mode noise, and in some examples, the optical flux is directed to only one of a pair of balanced detectors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum random number generators

Random number generators include a thermal optical source and detector configured to produce random numbers based on quantum-optical intensity fluctuations. An optical flux is detected, and signals proportional to optical intensity and a delayed optical intensity are combined. The combined signals can be electrical signals or optical signals, and the optical source is selected so as to have low coherence over a predetermined range of delay times. Balanced optical detectors can be used to reduce common mode noise, and in some examples, the optical flux is directed to only one of a pair of balanced detectors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Electroactive materials for rechargeable batteries

A secondary battery including a cathode having a primary cathode active material and an alkaline source material selected from the group consisting of Na2O, Na2O2, Na2S, NaF, NaCl, NaBr, Li2O, Li2O2, Li2S, LiF, LiCl, LiBr, Na2O, Na2O2, Na2S, NaF, NaCl, and a mixture of any two or more thereof; an anode having an anode active material; an electrolyte; and a separator.

Amine, Khalil↗
DOE OSTI2019

Comprehensive Evaluation and Application of GIS Insulation Condition Part 1: Selection and Optimization of Insulation Condition Comprehensive Evaluation Index Based on Multi-Source Information Fusion

How to select and optimize the evaluation indicators from many characteristic information to characterize the internal insulation condition of GIS equipment, and establish a scientific technical and economic evaluation model of insulation condition is a huge challenge needs to be addressed urgently. Motived by this purpose, this study simulates the deterioration process of four typical insulation defects in GIS to obtain the information of UHF, ultrasound, and SF 6 decomposition components excited by different degradation stages of each insulating defect simultaneously. Moreover, the operating environment conditions, running time, overhaul times, and the residual value of equipment are taken into account. Using the improved minimum-redundancy-maximum-relevance (mRMR) criterion and genetic algorithm, a comprehensive evaluation index system including the 23 evaluation indicators in three categories is established, which covers the PD hazard, SF 6 insulation performance, equipment running time and environment, and residual value. The research lays a foundation for the later construction and application of multi-source information fusion evaluation model of GIS equipment insulation condition considering economic value.

42 ENGINEERING↗
DOE OSTI2023

Selection of Training Sets for 235U Source Detection Classifiers Using Gamma Signatures

The machine learning methods for classifiers to detect low level radiation sources are of interest when suitable training data sets are available. Their application and performance assessment, however, involves the aspects of over-fitting and training data selection that are somewhat uncommon in other existing methods for this task.We study U-235 gamma signatures using data sets collected by 21 NaI detectors under controlled conditions. The gamma spectra are collected by the detectors located at different distances from the source, and we study their choice as training sets for classifiers to detect a source. The detectors form the near, middle and outer groups based on the distance to source. The classifiers based on the outer group are susceptible to over-fitting, that is, they achieve low training error but incur much higher testing error in independent tests. The other two groups achieve lower training error and comparable testing error, and the near group achieves the overall lowest error. In detecting a source at an unknown distance, the farther detectors in the middle group achieve the overall lowest testing error with limited over-fitting, thereby indicating the complex dependencies between the training and classifier performance.

Rao, Nageswara↗
DOE OSTI2017

Estimating methane emissions from biological and fossil‐fuel sources in the San Francisco Bay Area

Abstract We present the first sector‐specific analysis of methane (CH 4 ) emissions from the San Francisco Bay Area (SFBA) using CH 4 and volatile organic compound (VOC) measurements from six sites during September – December 2015. We apply a hierarchical Bayesian inversion to separate the biological from fossil‐fuel (natural gas and petroleum) sources using the measurements of CH 4 and selected VOCs, a source‐specific 1 km CH 4 emission model, and an atmospheric transport model. We estimate that SFBA CH 4 emissions are 166–289 Gg CH 4 /yr (at 95% confidence), 1.3–2.3 times higher than a recent inventory with much of the underestimation from landfill. Including the VOCs, 82 ± 27% of total posterior median CH 4 emissions are biological and 17 ± 3% fossil fuel, where landfill and natural gas dominate the biological and fossil‐fuel CH 4 of prior emissions, respectively.

Jeong, Seongeun↗