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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Magnetism of Ir 5 + -based double perovskites: Unraveling its nature and the influence of structure

A combination of x-ray-diffraction, x-ray absorption, x-ray magnetic circular dichroism, macroscopic magnetization, and muon-spin relaxation measurements is used to investigate the interplay between structure (both crystallography and microstructure) and magnetism in Sr 2 YIrO 6 as a function of both chemical (Ca-doping) and physical (hydrostatic) pressure. X-ray absorption spectroscopy clearly shows that the physical pressure is more effective in modifying the structure. On the other hand, the dichroic measurements evidence a constant magnetic signal with physical pressure and strong differences with Ca-doping. Muon-spin relaxation reveals the presence of magnetic order, even when this is hidden in the magnetization data. From the combined analysis, the magnetic results are explained in terms of the presence of Ir 6+ or Ir 4+ ions in magnetic clusters, most likely located at and triggered by antisite disorder. The measurements under high physical pressure indicate that the magnetic state is independent of the crystallographic details such as Ir-O distances and Ir-O-Y angles.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Investigation into the transport properties of planetary interiors through inelastic X-ray scattering experiments and quantum molecular dynamics (Final Technical Report)

This was a two-year research project for the period 08/15/2018 - 08/14/2020 conducted at the University of Nevada, Reno using Pronghorn, a newly built high-performance cluster located at the University. The short-term project goal was to provide computational support for the experimental campaigns at LCLS through data analysis of previous and upcoming scheduled experiments, along with performing state-of-the-art atomistic simulations of dense plasmas. The longer-term goal was to create a new computational high energy density physics group located at the University of Nevada, Reno. Guided by results from past and future high-resolution scattering experiments, we aimed to research and develop non-equilibrium and non-adiabatic atomistic simulations of dense plasmas that went beyond the Born-Oppenheimer approximation

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

ADDGALS: Simulated Sky Catalogs for Wide Field Galaxy Surveys

Abstract We present a method for creating simulated galaxy catalogs with realistic galaxy luminosities, broadband colors, and projected clustering over large cosmic volumes. The technique, denoted Addgals (Adding Density Dependent GAlaxies to Lightcone Simulations), uses an empirical approach to place galaxies within lightcone outputs of cosmological simulations. It can be applied to significantly lower-resolution simulations than those required for commonly used methods such as halo occupation distributions, subhalo abundance matching, and semi-analytic models, while still accurately reproducing projected galaxy clustering statistics down to scales of r ∼ 100 h −1 kpc . We show that Addgals catalogs reproduce several statistical properties of the galaxy distribution as measured by the Sloan Digital Sky Survey (SDSS) main galaxy sample, including galaxy number densities, observed magnitude and color distributions, as well as luminosity- and color-dependent clustering. We also compare to cluster–galaxy cross correlations, where we find significant discrepancies with measurements from SDSS that are likely linked to artificial subhalo disruption in the simulations. Applications of this model to simulations of deep wide-area photometric surveys, including modeling weak-lensing statistics, photometric redshifts, and galaxy cluster finding, are presented in DeRose et al., and an application to a full cosmology analysis of Dark Energy Survey (DES) Year 3 like data is presented in DeRose et al. We plan to publicly release a 10,313 square degree catalog constructed using Addgals with magnitudes appropriate for several existing and planned surveys, including SDSS, DES, VISTA, Wide-field Infrared Survey Explorer, and Rubin Observatory’s Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1974

An automated and repeatable data analysis procedure for remote sensing applications

A new multispectral data analysis procedure, based on LARSYS, has been developed which substantially reduces the influence of the analyst. The analysis is automated, including the interpretation of clustering results. The classification results obtained are repeatable and not biased by analyst subjectivity during the analysis.

Davis, B. J.↗
NASA NTRS1989

Analysis of ISEE-3/ICE solar wind data

Under the grant that ended November 11, 1988 work was accomplished in a number of areas, as follows: (1) Analysis of solar wind data; (2) Analysis of Giacobini/Zinner encounter data; (3) Investigation of solar wind and magnetospheric electron velocity distributions; and (4) Experimental investigation of the electronic structure of clusters. Reprints and preprints of publications resulting from this work are included in the appendices.

Coplan, Michael A.↗
DOE OSTI2024

Automated pipeline processing X-ray diffraction data from dynamic compression experiments on the Extreme Conditions Beamline of PETRA III

Presented and discussed here is the implementation of a software solution that provides prompt X-ray diffraction data analysis during fast dynamic compression experiments conducted within the dynamic diamond anvil cell technique. It includes efficient data collection, streaming of data and metadata to a high-performance cluster (HPC), fast azimuthal data integration on the cluster, and tools for controlling the data processing steps and visualizing the data using the DIOPTAS software package. This data processing pipeline is invaluable for a great number of studies. The potential of the pipeline is illustrated with two examples of data collected on ammonia–water mixtures and multiphase mineral assemblies under high pressure. The pipeline is designed to be generic in nature and could be readily adapted to provide rapid feedback for many other X-ray diffraction techniques, e.g. large-volume press studies, in situ stress/strain studies, phase transformation studies, chemical reactions studied with high-resolution diffraction etc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Constraining Galaxy-Halo connection using machine learning

We investigate the potential of machine learning (ML) methods to model small-scale galaxy clustering for constraining Halo Occupation Distribution (HOD) parameters. Our analysis reveals that while many ML algorithms report good statistical fits, they often yield likelihood contours that are significantly biased in both mean values and variances relative to the true model parameters. This highlights the importance of careful data processing and algorithm selection in ML applications for galaxy clustering, as even seemingly robust methods can lead to biased results if not applied correctly. ML tools offer a promising approach to exploring the HOD parameter space with significantly reduced computational costs compared to traditional brute-force methods if their robustness is established. Using our ANN-based pipeline, we successfully recreate some standard results from recent literature. Properly restricting the HOD parameter space, transforming the training data, and carefully selecting ML algorithms are essential for achieving unbiased and robust predictions. Among the methods tested, artificial neural networks (ANNs) outperform random forests (RF) and ridge regression in predicting clustering statistics, when the HOD prior space is appropriately restricted. We demonstrate these findings using the projected two-point correlation function (w p (r p )), angular multipoles of the correlation function (ξ ℓ (r)), and the void probability function (VPF) of Luminous Red Galaxies from Dark Energy Spectroscopic Instrument mocks. Our results show that while combining w p (r p ) and VPF improves parameter constraints, adding the multipoles ξ 0 , ξ 2 , and ξ 4 to w p (r p ) does not significantly improve the constraints.

cosmology↗
NASA NTRS2016

A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce

Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.

big data↗
NASA NTRS1987

Vegetation spatial variability and its effect on vegetation indices

Landsat MSS data were used to simulate low resolution satellite data, such as NOAA AVHRR, to quantify the fractional vegetation cover within a pixel and relate the fractional cover to the normalized difference vegetation index (NDVI) and the simple ratio (SR). The MSS data were converted to radiances from which the NDVI and SR values for the simulated pixels were determined. Each simulated pixel was divided into clusters using an unsupervised classification program. Spatial and spectral analysis provided a means of combining clusters representing similar surface characteristics into vegetated and non-vegetated areas. Analysis showed an average error of 12.7 per cent in determining these areas. NDVI values less than 0.3 represented fractional vegetated areas of 5 per cent or less, while a value of 0.7 or higher represented fractional vegetated areas greater than 80 per cent. Regression analysis showed a strong linear relation between fractional vegetation area and the NDVI and SR values; correlation values were 0.89 and 0.95 respectively. The range of NDVI values calculated from the MSS data agrees well with field studies.

Ormsby, J. P.↗
NASA NTRSDate not supplied

ASCoT 3: Nonlinear Principal Components Analysis and Uncertainty Quantification in Early Concept Spacecraft Flight Software Cost Estimation

For mission planners and evaluators alike, value in cost models comes from a mean or median prediction, an understanding of the uncertainty on that prediction, and an understanding of model performance. Here we apply advanced statistical and machine learning methods to spacecraft flight software cost, effort, and SLOC estimation, and present the results in the latest version of the Analogy Software Cost Tool (ASCoT). We present in- and out-of-sample performance metrics for our models, each of which incorporate some amount of epistemic uncertainty. ASCoT, hosted on the One NASA Cost Engineering (ONCE) database via the Online NASA Space Estimation Tool (ONSET), was first showcased in 2016 as a number of analogy-based models and methods (kNN and Clustering) to support early project formulation. This ASCoT update improves upon the previous analogic methods by incorporating uncertainty in the data transformations. In particular, we use a Nonlinear Principal Components Analysis (NLPCA) to deal with ordinal data.

Robotic Spacecraft↗
DOE OSTI2021

Choice of 16S ribosomal RNA primers affects the microbiome analysis in chicken ceca

We evaluated the effect of applying different sets of 16S rRNA primers on bacterial composition, diversity, and predicted function in chicken ceca. Cecal contents from Ross 708 birds at 1, 3, and 5 weeks of age were collected for DNA isolation. Eight different primer pairs targeting different variable regions of the 16S rRNA gene were employed. DNA sequences were analyzed using open-source platform QIIME2 and the Greengenes database. PICRUSt2 was used to determine the predicted function of bacterial communities. Changes in bacterial relative abundance due to 16S primers were determined by GLMs. The average PCR amplicon size ranged from 315 bp (V3) to 769 bp (V4–V6). Alpha- and beta-diversity, taxonomic composition, and predicted functions were significantly affected by the primer choice. Beta diversity analysis based on Unweighted UniFrac distance matrix showed separation of microbiota with four different clusters of bacterial communities. Based on the alpha- and beta-diversity and taxonomic composition, variable regions V1–V3(1) and (2), and V3–V4 and V3–V5 were in most consensus. Our data strongly suggest that selection of particular sets of the 16S rRNA primers can impact microbiota analysis and interpretation of results in chicken as was shown previously for humans and other animal species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cool Cores in Clusters of Galaxies in the Dark Energy Survey

We search for the presence of cool cores in optically selected galaxy clusters from the Dark Energy Survey (DES) and investigate their prevalence as a function of redshift and cluster richness. Clusters were selected from the redMaPPer analysis of three years of DES observations that have archival Chandra X-ray observations, giving a sample of 99 clusters with a redshift range of 0.11 < z < 0.87 and a richness range of 25 < λ < 207. Using the X-ray data, the core temperature was compared to the outer temperature to identify clusters where the core temperature is a factor of 0.7 or less than the outer temperature. We found a cool core fraction of approximately 20% with no significant trend in the cool core fraction with either redshift or richness.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1998

A comparative bear model for immobility-induced osteopenia

The National Institutes of Health (NIH) and the National Aeronautics and Space Administration (NASA) are seeking solutions to the human problem of osteopenia, or immobility-induced bone loss. Bears, during winter dormancy, appear uniquely exempted from the debilitating effects of immobility osteopenia. NIH and ESA, Inc. are creating a large database of metabolic information on human ambulatory and bedrest plasma samples for comparison with metabolic data obtained from bear plasma samples collected in different seasons. The database generated from NASA's HR113 human bedrest study showed a clear difference between plasma samples of ambulatory and immobile subjects through cluster analysis using compounds determined by high performance liquid chromatography with coulometric electrochemical array detection (HPLC-EC). We collected plasma samples from black bears (Ursus americanus) across 4 seasons and from 3 areas and subjected them to similar analysis, with particular attention to compounds that changed significantly in the NASA human study. We found seasonal differences in 28 known compounds and 33 unknown compounds. A final database contained 40 known and 120 unknown peaks that were reliably assayed in all bear and human samples; these were the primary data set for interspecies comparison. Six unidentified compounds changed significantly but differentially in wintering bears and immobile humans. The data are discussed in light of current theories regarding dormancy, starvation, and anabolic metabolism. Work is in progress by ESA Laboratories on a larger database to confirm these findings prior to a chemical isolation and identification effort. This research could lead to new pharmaceuticals or dietary interventions for the treatment of immobility osteopenia.

NASA Discipline Musculoskeletal↗
NASA NTRS2004

The Soft Excess Emission in the Hercules Supercluster Observed with the ROSAT Position Sensitive Proportional Counter

Recent observations with the XMM-Newton satellite confirmed the existence of the soft excess phenomenon in galaxy clusters, earlier discovered in several EUVE, ROSAT and BeppoSAX observations. Excess of soft X-ray radiation in the spectra of several clusters -- above the contribution from the well-known hot intra-cluster gas -- is normally interpreted as emission from cooler gas (T~10^6 K) in the vicinity of galaxy clusters. In this paper we investigate the emission from three clusters in the Hercules concentration, MKW~S, A2052 and A2063. Available XMM observations for two of them (MKW~3s and A2052) indicate the presence of the excess emission, also associated with O VII emission lines. The clusters lie close to the North Polar Spur region, a portion of the sky which features strong soft X-ray emission. We analyze the ROSAT PSPC data of the three clusters and of eight other neighboring locations, in order to study the level of the local soft X-ray background and its influence on the determination of soft excess fluxes. Our analysis reveals that the North Polar Spur region has strong gradients in the soft X-ray emission, which render the background subtraction process quite cumbersome. Despite these complications, we are able to provide evidence for the presence of soft excess emission in the PSPC data of MKW~S, A2052 and A2063.

Bonamente, Massimiliana↗
DOE OSTI2025

Probing substrate water access through the O1 channel of Photosystem II by single site mutations and membrane inlet mass spectrometry

Abstract Light-driven water oxidation by photosystem II sustains life on Earth by providing the electrons and protons for the reduction of CO 2 to carbohydrates and the molecular oxygen we breathe. The inorganic core of the oxygen evolving complex is made of the earth-abundant elements manganese, calcium and oxygen (Mn 4 CaO 5 cluster), and is situated in a binding pocket that is connected to the aqueous surrounding via water-filled channels that allow water intake and proton egress. Recent serial crystallography and infrared spectroscopy studies performed with PSII isolated fromThermosynechococcus vestitus(T. vestitus) support that one of these channels, the O1 channel, facilitates water access to the Mn 4 CaO 5 cluster during its S 2 →S 3 and S 3 →S 4 →S 0 state transitions, while a subsequent CryoEM study concluded that this channel is blocked in the cyanobacteriumSynechocystis sp.PCC 6803, questioning the role of the O1 channel in water delivery. Employing site-directed mutagenesis we modified the two O1 channel bottleneck residues D1-E329 and CP43-V410 (T. vestitusnumbering) and probed water access and substrate exchange via time resolved membrane inlet mass spectrometry. Our data demonstrates that water reaches the Mn 4 CaO 5 cluster via the O1 channel in both wildtype and mutant PSII. In addition, the detailed analysis provides functional insight into the intricate protein-water-cofactor network near the Mn 4 CaO 5 cluster that includes the pentameric, near planar ‘water wheel’ of the O1 channel.

Plant Sciences↗
DOE OSTI2025

Interacting dark sector within ETHOS: Cosmological constraints from SPT cluster abundance with DES and HST weak lensing data

We use galaxy cluster abundance measurements from the South Pole Telescope enhanced by multicomponent matched filter confirmation and complemented with mass information obtained using weak-lensing data from Dark Energy Survey Year 3 (DES Y3) and targeted Hubble Space Telescope observations for probing deviations from the cold dark matter paradigm. Concretely, we consider a class of dark sector models featuring interactions between dark matter (DM) and a dark radiation (DR) component within the framework of the effective theory of structure formation (ETHOS). We focus on scenarios that lead to power suppression over a wide range of scales, and thus can be tested with data sensitive to large scales, as realized, for example, for DM–DR interactions following from an unbroken non-Abelian 𝑆⁢𝑈⁡(𝑁) gauge theory (interaction rate with power-law index 𝑛 = 0 within the ETHOS parametrization). Cluster abundance measurements are mostly sensitive to the amount of DR interacting with DM, parametrized by the ratio of DR temperature to the cosmic microwave background (CMB) temperature, 𝜉 DR = 𝑇 DR /𝑇 CMB . We find an upper limit 𝜉 DR < 17% at 95% credibility. When the cluster data are combined with Planck 2018 CMB data along with baryon acoustic oscillation (BAO) measurements we find 𝜉 DR < 10%, corresponding to a limit on the abundance of interacting DR that is around 3 times tighter than that from CMB + BAO data alone. We also discuss the complementarity of weak lensing informed cluster abundance studies with probes sensitive to smaller scales, explore the impact on our analysis of massive neutrinos, and comment on a slight preference for the presence of a nonzero interacting DR abundance, which enables a physical solution to the 𝑆 8 tension.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1982

The violet interstellar medium associated with the Carina Nebula

The interstellar spectrum of HD 93205, an 03V star in the cluster Tr16, located inside the most active part of the Great Carina Nebula was analyzed using data extracted directly from raw IUE images. From the analysis of 67 lines of 25 atoms, ions and molecules, up to six discrete absorption components spanning a maximum velocity range of approximately 375 km 5/1 were identified. The component structure for different species is tabulated and sample absorption lines, indicating at the top the positions of the six components, labelled A to F are shown. Ion column densities appropriate to each component which can be resolved in at least one line were derived by constructing empirical curves of growth and by fitting the observations with corresponding theoretical absorption line profiles. The most important results concerning each component are discussed.

Pettini, M.↗