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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

R-Value Measurements Performed on Uranium Targets Irradiated with Fission Spectrum Neutrons FY 2021 for F2019 Project

The separation and characterization of two irradiated uranium targets, a depleted uranium (DU) and a highly enriched uranium (HEU) target, was conducted in April of 2021. The two targets were assembled at Los Alamos National Laboratory (LANL) and irradiated using the critical assembly at the National Criticality Experiments Research Center (NCERC). Splits of the dissolved targets were received by Pacific Northwest National Laboratory (PNNL) after which the PNNL and LANL teams chemically separated the solutions using independent separation schemes and analyzed the separated fractions for short lived actinides and fission products. Chemical separations at PNNL were traced with stable or radioactive tracers to allow for the determination of chemical yields, analyzing using either inductively coupled plasma optical emission spectroscopy (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS) or gamma emission analysis (GEA) depending on the nature of the tracer. Several other analytical techniques were used by PNNL including kinetic phosphorescence analysis (KPA) and thermal ionization mass spectrometry (TIMS) depending on the analyte’s need. Comparisons were made between current and historical PNNL and LANL, as well as literature values. Overall, there was agreement between the two laboratories for the bulk of analytes, with some notable exceptions such as 111 Ag, and 141,143,144 Ce. Included in these comparisons were the short-lived actinides 237 U, 239 Np, the fission products 89 Sr, 91 Y, 95/97 Zr, 99 Mo, 111 Ag, 115/115 mCd, 136/137 Cs, 140 Ba, 141/143/144 Ce, 147 Nd, 153 Sm, 156 Eu, and 161 Tb, providing both total atoms as well as the R-values. The data presented in this report represents the sixth NCERC irradiation of HEU and DU (FY13, FY14, FY15, FY17, FY18, FY21), and their subsequent separation and analysis.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1986

Biological consequences of a recurrent eddy off Point Conception, California

The biological effects on three different time scales (100-day mesoscale, annual, and several-year) of a mesoscale anticyclonic eddy consistently found in shipboard surveys and satellite-sensed data several hundred kilometers southwest of Point Conception, CA, are described. A detailed shipboard study of the eddy in January 1981 found a complex system of fronts in surface chlorophyll at the northern edge of the eddy; microplankton and zooplankton distributions were strongly affected by entrainment processes at the surface and, apparently, at depth. Concurrent satellite coastal zone color scanner ocean color images show agreement with the general surface characteristics of the eddy chlorophyll field but do not reflect features deeper than about 25 m, including the contribution of the deep chlorophyll maximum to the integrated chlorophyll values. Satellite data for the period October 1980 through October 1981 and shipboard data from California Cooperative Oceanic Fisheries Investigations (CalCOFI) for December 1980 to July 1981 show the continued presence of the eddy in the sea surface temperature and color field and in the distributions of surface chlorophyll and zooplankton displacement volume. A review of the CalCOFI survey results from 1949 to the present time demonstrates the recurrent nature of the eddy system on a year-to-year basis. The eddy system appears to have a significant effect on the distribution of both oceanic and nearshore organisms. Offshore transport of coastal species occurs in the form of large entrained plumes or filaments.

Haury, Loren R.↗
DOE OSTI2022

Nuclear Structure and Decay Data for A=31 Isobars

Here, experimental nuclear spectroscopic data are evaluated for 11 known nuclides of mass 31 (F, Ne, Na, Mg, Al, Si, P, S, Cl, Ar, K). Detailed evaluated information is presented for each reaction and decay experiment. Combining all the available data, recommended values are provided for energies, spins and parities, and half-lives of levels, with energies, branching ratios and multipolarities of γ radiations, and characteristics of β and α radiations in radioactive decays. 31 P is the most extensively studied one among all nuclides via various reactions and decays, followed by 31 S, 31 Si, 31 Mg and 31 Al. Information for excited states in 31 Ne, 31 Na, 31 Cl, 31 Ar and 31 K are limited; no excited states have yet been identified in 31 F, even its ground-state half-life is unknown. Significant new data have become available for the structure of 31 S, with its relevance to astrophysical applications. This work supersedes the earlier evaluation of A=31 by 2013Ou01, with literature cutoff date of Feb 15, 2013.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

pnnl/ZebrafishBMD

ZebrafishBMD: A software tool to perform benchmark dose calculations using toxicity screening data of high-throughput zebrafish assays. High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based “hits” and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. ZebrafishBMD implements the BMD workflow and provides Matlab scripts to read the data (stored in a specific structure) from a .mat file, to calculate the behavioral endpoint values from movement versus time data, to construct concentrationresponse curves, to perform curve-fitting, and to calculate BMD10 values.

Thomas, Dennis↗
DOE OSTI2018

Shallow Cumulus (SHALLOWCUMULUS) Value-Added Product Report

Periods of shallow cumulus clouds are identified automatically at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory in order to help determine times of interest for Large-Eddy Simulation (LES) and Observation (LASSO) modeling efforts. This value-added data product (VAP) uses input from the Cloud Type (CLDTYPE) VAP (with cloud types determined from Active Remotely Sensed Cloud Locations [ARSCL] VAP cloud boundaries), the ceilometer, and the total sky imager (TSI). The data set uses an hourly temporal resolution and only classifies events as shallow cumulus when low clouds are detected for at least 1.5 hours and meet specified criteria for cloud occurrence and cloud fraction. In addition to time periods that contain just shallow cumulus, cases with overlying cirrus and a number of transitional cases (transitions to/from cirrus, stratus, and altocumulus/altostratus) are also identified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

SAM-CAAM: A Concept for Acquiring Systematic Aircraft Measurements to Characterize Aerosol Air Masses

A modest operational program of systematic aircraft measurements can resolve key satellite aerosol data record limitations. Satellite observations provide frequent global aerosol amount maps but offer only loose aerosol property constraints needed for climate and air quality applications. In this paper, we define and illustrate the feasibility of flying an aircraft payload to measure key aerosol optical, microphysical, and chemical properties in situ. The flight program could characterize major aerosol airmass types statistically, at a level of detail unobtainable from space. It would 1) enhance satellite aerosol retrieval products with better climatology assumptions and 2) improve translation between satellite-retrieved optical properties and species-specific aerosol mass and size simulated in climate models to assess aerosol forcing, its anthropogenic components, and other environmental impacts. As such, Systematic Aircraft Measurements to Characterize Aerosol Air Masses (SAM-CAAM) could add value to data records representing several decades of aerosol observations from space; improve aerosol constraints on climate modeling; help interrelate remote sensing, in situ, and modeling aerosol-type definitions; and contribute to future satellite aerosol missions. Fifteen required variables are identified and four payload options of increasing ambition are defined to constrain these quantities. “Option C” could meet all the SAM-CAAM objectives with about 20 instruments, most of which have flown before, but never routinely several times per week, and never as a group. Aircraft integration and approaches to data handling, payload support, and logistical considerations for a long-term, operational mission are discussed. Finally, SAM-CAAM is feasible because, for most aerosol sources and specified seasons, particle properties tend to be repeatable, even if aerosol loading varies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, and lack of privacy could discourage widespread adoption of many exciting applications. We present a privacy-preserving participatory sensing scheme for multidimensional data which uses negative surveys. Multidimensional data, such as vectors of attributes that include location and environment fields, pose a particular challenge for privacy protection and are common in participatory sensing applications. When reporting data in a negative survey, an individual participant randomly selects a value from the set complement of the sensed data value, once for each dimension, and returns the negative values to a central collection server. Using algorithms described in this paper, the server can reconstruct the probability density functions of the original distributions of sensed values, without knowing the participants actual data. As a consequence, complicated encryption and key management schemes are avoided, conserving energy. We study trade-offs between accuracy and privacy, and their relationships to the number of dimensions, categories, and participants. We introduce dimensional adjustment, a method that reduces the magnification of error associated with earlier work. Two simulation scenarios illustrate how the approach can protect the privacy of a participant's multidimensional data while allowing useful population information to be aggregated.

Groat, Michael↗
DOE OSTI2024

Radioisotope Identification with List-Mode Gamma Ray Data: A rigorous assessment on the value of temporal information applied to radioisotope identification.

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with “confuser” sources, or spectra with similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research rigorously examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and information theory. We further propose a basic classification model that can utilize spectral or temporal data (or both) to determine if the incorporation of temporal information can improve radioisotope identification. The findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Oncology↗
NASA NTRS1995

The carbon-isotopic composition of Proterozoic carbonates: Riphean successions from northwestern Siberia (Anabar Massif, Turukhansk Uplift)

Thick carbonate-dominated successions in northwestern Siberia document secular variations in the C-isotopic composition of seawater through Mesoproterozoic and early Neoproterozoic (Early to early Late Riphean) time. Mesoproterozoic dolomites of the Billyakh Group, Anabar Massif, have delta 13C values that fall between 0 and -1.9 permil versus PDB, with values in the upper part of the succession (Yusmastakh Formation) consistently higher than those of the lower (Ust'-Il'ya and Kotuikan formations). Consistent with available biostratigraphic and radiometric data, delta 13C values for Billyakh carbonates compare closely with those characterizing early Mesoproterozoic carbonates (about 1600-1200 Ma) worldwide. In contrast, late Mesoproterozoic to early Neoproterozoic limestones and dolomites in the Turukhansk Uplift exhibit moderate levels of secular variation. Only the lowermost carbonates in the Turukhansk succession (Linok Formation) have delta 13C values that approximate Billyakh values. Higher in the Turukhansk succession, delta 13C values vary from -2.7 to +4.6 permil (with outliers as low as -5.0 permil interpreted as diagentically altered). Again, consistent with paleontological and radiometric data, these values compare well with isotopic values from 1200 to 850 Ma successions elsewhere. Five sections measured in different parts of the Turukhansk basin show nearly identical patterns of variation, confirming that carbonate delta 13C correlates primarily with time and not facies. The Siberian sections illustrate the potential of integrated biostratigraphic and chemostratigraphic data in the intra- and interbasinal correlation of Mesoproterozoic and early Neoproterozoic rocks.

NASA Discipline Exobiology↗
DOE OSTI2011

Generalized blockmodeling of multiple valued networks.

This paper presents an extension to generalized blockmodeling where there are more than two types of objects to be clustered based on valued network data. We use the ideas in homogeneity block modeling to develop an optimization model to perform the clustering of the objects and the resulting partitioning of the ties so as to minimize the inconsistency of an empirical block with an ideal block. The ideal block types used in this modeling are null, complete and a new type that is related to that developed in Ziberna (2007). Three case studies are presented, two based on the Southern Women dataset (Davis et al. 1941) and a third based on passenger air travel in the Continental United States.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1994

Calibration of TOPEX/POSEIDON at Platform Harvest

We present estimates for the mean bias of the TOPEX/POSEIDON NASA altimeter (ALT) and the Centre National d'Etudes Spatiales altimeter (SSALT) using in-situ data gathered at Platform Harvest during the first 36 cycles of the mission. Data for 21 overflights of the ALT and six overflights of the SSALT have been analyzed. The analysis includes an independent assessment of in-situ measurements of sea level, the radial component of the orbit, wet tropospheric path delay, and ionospheric path delay. (The sign convention used is such that, to correct the geophysical data record values for sea level, add the bias algebraically. Unless otherwise stated, the uncertainty in a given parameter is depicted by +/- sigma(sub x), where sigma(sub x) is the sample standard deviation of x about the mean.) Tide gauges at Harvest provide estimates of sea level with an uncertainty of +/- 1.5 cm. The uncertainty in the radial component of the orbit is estimated to be +/- 1.3 cm. In-situ measurements of tropopsheric path delay at Harvest compare to within +/- 1.3 cm of the TOPEX/POSEIDON microwave radiometer, and in-situ measurements of the ionospheric path delay compare to within -0.4 +/- 0.7 cm of the dual-frequency ALT and 1.1 +/- 0.6 cm of Doppler orbitography and radiopositioning integrated by satellite. We obtain mean bias estimates of -14.5 +/- 2.9 cm for the ALT and +0.9 +/- 3.1 cm for the SSALT (where the uncertainties are based on the standard deviation of the estimated mean (sigma(sub bar x/y), which is derived from sample statistics and estimates for errors that cannot be observed). These results are consistent with independent estimates for the relative bias between the two altimeters. A linear regression applied to the complete set of data shows that there is a discernable secular trend in the time series for the ALT bias estimates. A preliminary analysis of data obtained through cycle 48 suggests that the apparent secular drift may be the result of a poorly sampled annual signal.

Christensen, E. J.↗
DOE OSTI2020

Powering the Blue Economy: Progress Exploring Marine Renewable Energy Integration With Ocean Observations

The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.

marine renewable energy, powering the blue economy↗
DOE OSTI2020

Measurement of the cross section for electroweak production of a Z boson, a photon and two jets in proton-proton collisions at $\sqrt{s} =$ 13 TeV and constraints on anomalous quartic couplings

A measurement is presented of the cross section for electroweak production of a Z boson and a photon in association with two jets (Zγjj) in proton-proton collisions. The Z boson candidates are selected through their decay into a pair of electrons or muons. The process of interest, electroweak Zγjj production, is isolated by selecting events with a large dijet mass and a large pseudorapidity gap between the two jets. The measurement is based on data collected at the CMS experiment at $ \sqrt{s} $ = 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{−1}$. The observed significance of the signal is 3.9 standard deviations, where a significance of 5.2 standard deviations is expected in the standard model. These results are combined with published results by CMS at $ \sqrt{s} $ = 8 TeV, which leads to observed and expected respective significances of 4.7 and 5.5 standard deviations. From the 13 TeV data, a value is obtained for the signal strength of electroweak Zγjj production and bounds are given on quartic vector boson interactions in the framework of dimension-eight effective field theory operators.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Spectroscopic Evidence of Phosphorous Heterocycle–DNA Interaction and its Verification by Docking Approach

In the present work, the interaction of phosphorous heterocycle (PH) with calf thymus DNA (CTDNA) has been studied using spectroscopy and verified by molecular modeling which is found to be in consonance with each other. Apparent association constant (K{sub app} = 4.77 × 10{sup 3} M{sup − 1}), calculated using UV–Vis spectra indicating an adequate complex formation between CTDNA and PH. A dynamic mode of the fluorescence quenching mechanism in case of ethidium bromide (EB) + CTDNA by PH has been observed confirming formation of DNA-PH complex. A moderate binding constants of PH with CTDNA + EB has been observed (2.74 × 10{sup 4} M{sup − 1} at 293 K) by means of fluorescence data. Calculated values of thermodynamic parameters enthalpy change (ΔH) and entropy change (ΔS), suggests weak (van der Walls like) force and hydrogen bonds playing the main role in the binding of PH to CTDNA. Furthermore, the results of circular dichroism (CD) reveal that PH does not disturb native conformation of CTDNA. As observed from absorption and fluorescence spectroscopy the binding mode of PH with DNA was indicative of a non-intercalative binding, which was supposed to be a groove binding. The molecular modeling results show that PH is capable of binding DNA having docking binding energy = -7.26 kcal × mol{sup − 1}. Above mentioned experimental results are found to be in consonance with molecular docking simulations and supports the CTDNA-PH binding. .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Lagrangian dual method for two-stage robust optimization with binary uncertainties

This report presents a new exact method to calculate worst-case parameter realizations in two-stage robust optimization problems with categorical or binary-valued uncertain data. Traditional exact algorithms for these problems, notably Benders decomposition and column-and-constraint generation, compute worst-case parameter realizations by solving mixed-integer bilinear optimization subproblems. However, their numerical solution can be computationally expensive not only due to their resulting large size after reformulating the bilinear terms, but also because decision-independent bounds on their variables are typically unknown. We propose an alternative Lagrangian dual method that circumvents these difficulties and is readily integrated in either algorithm. We specialize the method to problems where the binary parameters switch on or off constraints as these are commonly encountered in applications, and discuss extensions to problems that lack relatively complete recourse and to those with integer recourse. Numerical experiments provide evidence of significant computational improvements over existing methods.

42 ENGINEERING↗
DOE OSTI2021

Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of methane fluxes measured by eddy-covariance require gap-filling to estimate annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for methane, with regards both to the best model algorithms and predictors. In this study, we address the need for standardization by synthesizing results of gap-filling methods applied at 17 wetland sites spanning boreal to tropical regions including all major wetlands classes and two rice paddies. We introduce new procedures for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with robust uncertainty estimates. We tested a conventional method (marginal distribution sampling) and four machine learning algorithms - penalized linear regression, artificial neural networks, random forests, and boosted decision trees - and four predictor sets, including temporal, meteorological, ecosystem carbon and energy flux, and soil predictors. We find that the conventional method can achieve similar median performance to the machine learning models but is worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. Finally, we gap-fill and provide summary evaluation metrics for all 81 sites in the FLUXNET-CH4 community dataset and publicly release the python code for model development, evaluation, and uncertainty estimation.

42 ENGINEERING↗
DOE OSTI2020

Combustion of metal powder with dinitrogen tetroxide

In this work, we present analysis of a novel reactive material system that employs dinitrogen tetroxide (N 2 O 4 ) as a liquid oxidizer with metal powder fuels. The oxidizer was added to micron scale aluminum and zirconium powders by a remote injection system. When ignited with a high voltage spark, the mixtures were observed to possess reactivity comparable to nanocomposite reactive materials, with open-tube flame expansion velocities from 500 to 1400 m/s depending on fuel/oxidizer ratio and tube diameter. Temperatures were observed to range from 3000 to 3500 K as measured with gray body fits to 16-channel time-resolved pyrometer and time-integrated spectrometer data. These values were significantly below calculated adiabatic flame temperatures, which we attribute to local deviations from stoichiometry and kinetic/energetic limitations similar to those observed in studies of particles burning in high pressures gaseous oxidizers. Al/N 2 O 4 reactivity was found to be most likely limited by the vaporization of the metal from the particle surface and Zr/N 2 O 4 was limited by slower burning and complex interactions involving the solubility of nitrogen and oxygen in the molten Zr. We also discuss the potential for these materials to be used to create an “on/off” reactive material, since N2O4 can be added remotely and driven to evaporate via vacuum or purge of inert gases to return it to a safe condition.

36 MATERIALS SCIENCE↗