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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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A compilation of redshifts and velocity dispersions for Abell clusters (Struble and Rood 1987): Documentation for the machine-readable version

The machine readable version of the compilation, as it is currently being distributed from the Astronomical Data Center, is described. The catalog contains redshifts and velocity dispersions for all Abell clusters for which these data had been published up to 1986 July. Also included are 1950 equatorial coordinates for the centers of the listed clusters, numbers of observations used to determine the redshifts, and bibliographical references citing the data sources.

Warren, Wayne H., Jr.↗
DOE OSTI2013

ACTIVE GALACTIC NUCLEUS FEEDBACK AND ENTROPY INJECTION IN GALAXY CLUSTER CORES

We make the first estimate of non-gravitational energy profiles in galaxy cluster cores (and beyond) based on observational data. Comparing the observed entropy profiles within r{sub 500}, from the Representative XMM-Newton Cluster Structure Survey to simulated base entropy profiles without feedback from both adaptive mesh refinement (AMR) and smoothed particle hydrodynamic (SPH) non-radiative simulations, we estimate the amount of additional non-gravitational energy, E{sub ICM}, contained in the intracluster medium (ICM), as well as the total energy feedback, E{sub Feedback}, from active galactic nuclei (AGNs; the central AGNs in most cases) into the clusters. The total feedback energy scales with the mean spectroscopic temperature as E{sub Feedback}∝T{sub sp}{sup 2.52±0.08} and E{sub Feedback}∝T{sub sp}{sup 2.17±0.11} for the SPH and AMR baseline profiles. The mean non-gravitational energy per particle within r{sub 500} remaining in the ICM after energy lost during cooling is ε{sub ICM} = 2.8 ± 0.8 keV for the SPH theoretical relation and ε{sub ICM} = 1.7 ± 0.9 keV for the AMR theoretical relation. We use the NRAO/VLA Sky Survey source catalog to determine the radio luminosity, L{sub R} , at 1.4 GHz of the central source(s) of our sample. For T{sub sp} > 3 keV, the E{sub Feedback} correlates with L{sub R} , although with different normalization for cool-core and non-cool-core clusters. We show that AGNs could provide a significant portion of the feedback.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

A detailed X-ray study of the cooling intracluster gas in A496

X-ray observations of the galaxy cluster A496 are presented. Imaging and spectral data, together with the presence of optical filaments, show that radiative cooling is occurring in the cluster core and indicates a mass flow rate of about 200 solar masses per year on to the central galaxy. The gas temperature and density profiles within the core are similar to those in the Perseus cluster (A426) despite a six-fold difference in total X-ray luminosity. This is consistent with the model of Fabian and Nulsen (1977, 1979) for steady flows caused by cooling. The evolution of cooling flows is discussed and it is shown that thermal conduction is suppressed to well below the Spitzer rate.

Nulsen, P. E. J.↗
NASA NTRS1994

Imaging of 16 distant EMSS clusters with z greater than or equal to 0.2 and L(subx,44) greater than or equal to 4: New arcs and first consequences

Medium-deep V, R, and I observations of a subsample of 16 clusters of galaxies in the Experimental Mobile Satellite System (EMSS) sample defined from the Einstein satellite data are presented. Three new cases of clusters with giant luminous arcs (length/width greater than 10, V less than 22) have been discovered together with six new arcs (4 less than l/w less than 10, V less than 22), and we propose that these arcs are new examples of gravitational lensing of background galaxies by the rich clusters. Combined with giant luminous arcs already observed in the EMSS sample, there is a total of six giant luminous arcs observed in the sample of 16 clusters. This high rate of success in finding arcs added to their axis ratio distribution is consistent with very small core radii (less than 100 kpc) for the dark matter distribution in rich clusters, and rules out models in which dark matter follows the X-ray gas.

Le Fevre, O.↗
DOE OSTI2013

Stability of {sup 248–254}Cf isotopes against alpha and cluster radioactivity

Stability of {sup 248–254}Cf nuclei against alpha and cluster emissions is studied within our Coulomb and proximity potential model (CPPM). It is found that these nuclei are stable against light clusters (except alpha particles) and unstable against heavy cluster (A{sub 2}≥40) emissions. For heavy cluster emissions the daughter nuclei lead to doubly magic {sup 208}Pb or the neighbouring one. The effects of quadrupole and hexadecapole deformations of parent nuclei, daughter nuclei and emitted cluster on half lives are also studied. The computed alpha decay half life values (including quadrupole deformation β{sub 2}) are in close agreement with experimental data. Inclusion of quadrupole deformation reduces the height and width of the barrier (increases the barrier penetrability) and hence half life decreases. -- Highlights: •{sup 248–254}Cf parents are stable against light clusters (except alpha particles) and are unstable against heavy clusters ({sup 46}Ar, {sup 48,50}Ca etc.). •For the case of heavy cluster emissions the daughter nuclei are doubly magic {sup 208}Pb or neighbouring one. •The alpha decay half lives are in agreement with experimental data. •The cluster decay half lives decrease with the inclusion of quadrupole deformation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1986

An X-ray and optical study of the cluster of galaxies Abell 754

X-ray and optical data for A754 are used to study the relative distribution of the luminous and dark matter in this dense, rich cluster of galaxies with X-ray luminosity comparable to that of the Coma Cluster. A quantitative statistical comparison is made of the galaxy positions with the total mass responsible for maintaining the X-ray emitting gas in hydrostatic equilibrium. A simple bimodal model which fits both the X-ray and optical data suggests that the galaxies are distributed consistently with the projected matter distribution within the region covered by the X-ray map (0.5-1 Mpc). The X-ray and optical estimates of the mass in the central region of the cluster are 2.9 x 10 to the 14th and 3.6 + or - 0.5 x 10 to the 14th solar masses, respectively.

Fabricant, D.↗
DOE OSTI2020

Detecting Rain–Snow-Transition Elevations in Mountain Basins Using Wireless Sensor Networks

Here, to provide complementary information on the hydrologically important rain–snow-transition elevation in mountain basins, this study provides two estimation methods using ground measurements from basin-scale wireless sensor networks: one based on wet-bulb temperature T wet and the other based on snow-depth measurements of accumulation and ablation. With data from 17 spatially distributed clusters (178 nodes) from two networks, in the American and Feather River basins of California’s Sierra Nevada, we analyzed transition elevation during 76 storm events in 2014–18. A T wet threshold of 0.5°C best matched the transition elevation defined by snow depth. Transition elevations using T wet in upper elevations of the basins generally agreed with atmospheric snow level from radars located at lower elevations, while radar snow level was ~100 m higher due to snow-level lowering on windward mountainsides during orographic lifting. Diurnal patterns of the difference between transition elevation and radar snow level were observed in the American basin, related to diurnal ground-temperature variations. However, these patterns were not found in the Feather basin due to complex terrain and higher uncertainties in transition-elevation estimates. The American basin tends to exhibit 100-m-higher transition elevations than does the Feather basin, consistent with the Feather basin being about 1° latitude farther north. Transition elevation averaged 155 m higher in intense atmospheric river events than in other events; meanwhile, snow-level lowering was enhanced with a 90-m-larger difference between radar snow level and transition elevation. On-the-ground continuous observations from distributed sensor networks can complement radar data and provide important ground truth and spatially resolved information on transition elevations in mountain basins.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Statistical relationships across epigenomes using large-scale hierarchical clustering

Recent advances in genomics and sequencing platforms have revolutionized our ability to create immense data sets, particularly for studying epigenetic regulation of gene expression. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given nonlinear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3000 epigenomes of uninfected individuals and provide a framework to characterize the relationships among epigenetic modifiers, their modifiers, genetic loci, and specific immune cell types across all chromosomes using hierarchical clustering. Hierarchical clustering of epigenomic data revealed consistent epigenetic patterns across chromosomes, demonstrating that variation due to epigenetic modifiers is greater than variation between cell types. Gene Ontology and KEGG pathway analyses indicated significant enrichment of genes involved in chromatin remodeling, mRNA splicing, immune responses, and the regulation of microRNAs and snoRNAs. Epigenetic modifiers frequently formed biologically relevant clusters, including the cohesin complex, RNA Polymerase II transcription factors, and PRC2 complex members. These clustering behaviors remained consistent across all chromosomes, supported by entropy analysis and high Adjusted Rand Index scores, indicating robust cross-chromosomal similarity. Co-occurrence analysis further revealed specific sets of modifiers that consistently appeared together within clusters, reflecting shared biological functions and interactions. Validation using another dataset confirmed the reproducibility of these clustering patterns and modifier co-occurrence relationships, underscoring the reliability and generalizability of the methodology.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1973

An unsupervised classification technique for multispectral remote sensing data.

Description of a two-part clustering technique consisting of (a) a sequential statistical clustering, which is essentially a sequential variance analysis, and (b) a generalized K-means clustering. In this composite clustering technique, the output of (a) is a set of initial clusters which are input to (b) for further improvement by an iterative scheme. This unsupervised composite technique was employed for automatic classification of two sets of remote multispectral earth resource observations. The classification accuracy by the unsupervised technique is found to be comparable to that by traditional supervised maximum-likelihood classification techniques.

Su, M. Y.↗
DOE OSTI2017

Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants

Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we will need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2003

Metallicity Effects on Stellar Magnetic Activity: Blanco 1 as a Test Case

We present X-ray Luminosity Distributions (XLDs) of late-type members (dF, dG, dK, dM) of the Blanco 1 cluster, based on ROSAT-HRI data and new astrometric-photometric membership obtained from the GSC-II project. For the first time we present the XLD of dM stars of this cluster. The high metallicity of Blanco 1 allows us to investigate the role of chemical composition on the coronal emission of late-type stars. Comparison between X-ray Luminosity Distributions of Blanco 1 and Pleiades, NGC2516 and alpha Per suggests a possible metallicity effect in dM stars.

Harnden, F. R., Jr.↗
NASA NTRS2000

Sunyaev - Zel'dovich Effect Imaging of Massive Clusters of Galaxies at Redshift > 0.8

We present Sunyaev-Zel'dovich Effect (SZE) imaging observations of three distant (z greater than 0.8) and highly X-ray luminous clusters of galaxies, C1J1226+33, C1J0152-13 and MS1054-03. Two of the clusters, C1J1226+33 and C1J0152-13, were recently discovered in the WARPS X-ray survey. Their high X-ray luminosity suggests they are massive systems and, if confirmed, would provide strong constraints on the cosmological parameters of structure formation models. Our Sunyaev-Zel'dovich Effect data provide confirmation that they are massive clusters similar to the well studied cluster MS1054-03. Assuming the clusters have the same gas mass fraction derived from SZE measurements of eighteen known massive clusters, we are able to infer their mass and electron temperatures from the SZE data. The derived electron temperatures are 10.0, 8.5, and 10.3 KeV, respectively, and we infer total masses of approximately 2 x 10(circumflex) 14 h(circumflex) -l M - sun within a radius of 65" for all three clusters. For C1J0152-13 and MS1054-03 we find good agreement between our SZE derived temperatures and those derived from X-ray spectroscopic measurements. No X-ray derived temperatures are available for C1J1226+33, and thus the SZE data provide the first confirmation that it is indeed a massive system. The demonstrated ability to determine cluster temperatures and masses from SZE observations without access to X-ray data illustrates the power of using deep SZE surveys to probe the distant universe.

Joy, Marshall↗
NASA NTRS1999

Coronal Physics and the Chandra Emission Line Project

With the launch of the Chandra X-ray Observatory, high resolution X-ray spectroscopy of cosmic sources has begun. Early, deep observations of three stellar coronal sources will provide not only invaluable calibration data, but will also give us benchmarks for plasma spectral modeling codes. These codes are to interpret data from stellar coronae, galaxies and clusters of galaxies. supernova remnants and other astrophysical sources, but they have been called into question in recent years as problems with understanding moderate resolution ASCA and EUVE data have arisen. The Emission Line Project is a collaborative effort to improve the models, with Phase 1 being the comparison of models with observed spectra of Capella, Procyon, and HR, 1099. Goals of these comparisons are (1) to determine and verify accurate and robust diagnostics and (2) to identify and prioritize issues in fundamental spectroscopy which will require further theoretical and/or laboratory work. A critical issue in exploiting the coronal data for these purposes is to understand the extent to which common simplifying assumptions (coronal equilibrium, time-independence, negligible optical depth) apply. We will discuss recent advances in our understanding of stellar coronae in this context.

Brickhouse, Nancy↗
DOE OSTI2017

Cluster Mass Calibration at High Redshift: HST Weak Lensing Analysis of 13 Distant Galaxy Clusters from the South Pole Telescope Sunyaev-Zel’dovich Survey

We present an HST/Advanced Camera for Surveys (ACS) weak gravitational lensing analysis of 13 massive high-redshift (z(median) = 0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V - I colour. Our estimate of the source redshift distribution is based on Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalization of the mass-temperature scaling relation ln (E(z) M-500c/10(14)M(circle dot)) = A + 1.5ln (kT/7.2 keV) to A = 1.81(-0.14)(+0.24)(stat.)+/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c(200c) = 5.6(-1.8)(+3.7).

cosmology: observations↗
DOE OSTI2016

Cosmology and astrophysics from relaxed galaxy clusters – V. Consistency with cold dark matter structure formation

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. In addition, we present constraints on the concentration–mass relation for massive clusters, finding a power-law mass dependence with a slope of κ m = –0.16 ± 0.07, in agreement with CDM predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κ ζ = –0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ~50 kpc–1 Mpc), and test for departures from the simple Navarro–Frenk–White (NFW) form, for which the logarithmic slope of the density profile tends to –1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σ β = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σ α = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements

Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.

17 WIND ENERGY↗
DOE OSTI2023

Detecting low-mass perturbers in cluster lenses using curved arc bases

ABSTRACT Strong gravitationally lensed arcs produced by galaxy clusters have been observationally detected for several decades now. These strong lensing constraints provided high-fidelity mass models for cluster lenses that include substructure down to $10^{9{-}10}\, \mathrm{M}_\odot$. Optimizing lens models, where the cluster mass distribution is modelled by a smooth component and subhaloes associated with the locations of individual cluster galaxies, has enabled deriving the subhalo mass function, providing important constraints on the nature and granularity of dark matter. In this work, we explore and present a novel method to detect and measure individual perturbers (subhaloes, line-of-sight haloes, and wandering supermassive black holes) by exploiting their proximity to highly distorted lensed arcs in galaxy clusters, and by modelling the local lensing distortions with curved arc bases. This method offers the possibility of detecting individual low-mass perturber subhaloes in clusters and haloes along the line of sight down to a mass resolution of $10^8\, \mathrm{M}_\odot$. We quantify our sensitivity to low-mass perturbers ($M\sim 10^{7{-}9}\, \mathrm{M}_\odot$) in clusters (M ∼ 1014–15 M⊙), by creating realistic mock data. Using three lensed images of a background galaxy in the cluster SMACS J0723, taken by JWST, we study the retrieval of the properties of potential perturbers with masses $M=10^{7{-}9}\, \mathrm{M}_\odot$. From the derived posterior probability distributions for the perturber, we constrain its concentration, redshift, and ellipticity. By allowing us to probe lower mass substructures, the use of curved arc bases can lead to powerful constraints on the nature of dark matter as discrimination between dark matter models appears on smaller scales.

Astronomy & Astrophysics↗