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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS2016

Interannual Similarity in the Martian Atmosphere During the Dust Storm Season

We find that during the dusty season on Mars (southern spring and summer) of years without a global dust storm there are three large regional-scale dust storms. The storms are labeled A, B, and C in seasonal order. This classification is based on examining the zonal mean 50 Pa (approximately 25 km) daytime temperature retrievals from TES/MGS and MCS/MRO over 6 Mars Years. Regional-scale storms are defined as events where the temperature exceeds 200 K. Examining the MCS dust field at 50 Pa indicates that warming in the Southern Hemisphere is dominated by direct heating, while northern high latitude warming is a dynamical response. A storms are springtime planet encircling Southern Hemisphere events. B storms are southern polar events that begin near perihelion and last through the solstice. C storms are southern summertime events starting well after the end of the B storm. C storms show the most interannual variability.

Kass, D. M.↗
DOE OSTI2012

Cognitive decision errors and organization vulnerabilities in nuclear power plant safety management: Modeling using the TOGA meta-theory framework

In the field of nuclear power plant (NPP) safety modeling, the perception of the role of socio-cognitive engineering (SCE) is continuously increasing. Today, the focus is especially on the identification of human and organization decisional errors caused by operators and managers under high-risk conditions, as evident by analyzing reports on nuclear incidents occurred in the past. At present, the engineering and social safety requirements need to enlarge their domain of interest in such a way to include all possible losses generating events that could be the consequences of an abnormal state of a NPP. Socio-cognitive modeling of Integrated Nuclear Safety Management (INSM) using the TOGA meta-theory has been discussed during the ICCAP 2011 Conference. In this paper, more detailed aspects of the cognitive decision-making and its possible human errors and organizational vulnerability are presented. The formal TOGA-based network model for cognitive decision-making enables to indicate and analyze nodes and arcs in which plant operators and managers errors may appear. The TOGA's multi-level IPK (Information, Preferences, Knowledge) model of abstract intelligent agents (AIAs) is applied. In the NPP context, super-safety approach is also discussed, by taking under consideration unexpected events and managing them from a systemic perspective. As the nature of human errors depends on the specific properties of the decision-maker and the decisional context of operation, a classification of decision-making using IPK is suggested. Several types of initial situations of decision-making useful for the diagnosis of NPP operators and managers errors are considered. The developed models can be used as a basis for applications to NPP educational or engineering simulators to be used for training the NPP executive staff. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2014

Application of Hybrid Geo-Spatially Granular Fragility Curves to Improve Power Outage Predictions

Fragility curves depict the relationship between a weather variable (wind speed, gust speed, ice accumulation, precipitation rate) and the observed outages for a targeted infrastructure network. This paper describes an empirical study of the county by county distribution of power outages and one minute weather variables during Hurricane Irene with the objective of comparing 1) as built fragility curves (statistical approach) to engineering as designed (bottom up) fragility curves for skill in forecasting outages during future hurricanes; 2) county specific fragility curves to find examples of significant deviation from average behavior; and 3) the engineering practices of outlier counties to suggest future engineering studies of robustness. Outages in more than 90% of the impacted counties could be anticipated through an average or generic fragility curve. The remaining counties could be identified and handled as exceptions through geographic data sets. The counties with increased or decreased robustness were characterized by terrain more or less susceptible to persistent flooding in areas where above ground poles located their foundations. Land use characteristics of the area served by the power distribution system can suggest trends in the as built power grid vulnerabilities to extreme weather events that would be subjects for site specific studies.

classification of impacted areas↗
DOE OSTI2011

A PHOTOMETRIC VARIABILITY SURVEY OF FIELD K AND M DWARF STARS WITH HATNet

Using light curves from the HATNet survey for transiting extrasolar planets we investigate the optical broadband photometric variability of a sample of 27, 560 field K and M dwarfs selected by color and proper motion (V - K {approx}> 3.0, {mu} > 30 mas yr{sup -1}, plus additional cuts in J - H versus H - K{sub S} and on the reduced proper motion). We search the light curves for periodic variations and for large-amplitude, long-duration flare events. A total of 2120 stars exhibit potential variability, including 95 stars with eclipses and 60 stars with flares. Based on a visual inspection of these light curves and an automated blending classification, we select 1568 stars, including 78 eclipsing binaries (EBs), as secure variable star detections that are not obvious blends. We estimate that a further {approx}26% of these stars may be blends with fainter variables, though most of these blends are likely to be among the hotter stars in our sample. We find that only 38 of the 1568 stars, including five of the EBs, have previously been identified as variables or are blended with previously identified variables. One of the newly identified EBs is 1RXS J154727.5+450803, a known P = 3.55 day, late M-dwarf SB2 system, for which we derive preliminary estimates for the component masses and radii of M{sub 1} = M{sub 2} = 0.258 {+-} 0.008 M{sub sun} and R{sub 1} = R{sub 2} = 0.289 {+-} 0.007 R{sub sun}. The radii of the component stars are larger than theoretical expectations if the system is older than {approx}200 Myr. The majority of the variables are heavily spotted BY Dra-type stars for which we determine rotation periods. Using this sample, we investigate the relations between period, color, age, and activity measures, including optical flaring, for K and M dwarfs, finding that many of the well-established relations for F, G, and K dwarfs continue into the M dwarf regime. We find that the fraction of stars that is variable with peak-to-peak amplitudes greater than 0.01 mag increases exponentially with the V - K{sub S} color such that approximately half of field dwarfs in the solar neighborhood with M {approx}< 0.2 M{sub sun} are variable at this level. Our data hint at a change in the rotation-activity-age connection for stars with M {approx}< 0.25 M{sub sun}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Evaluation of Multi-Fidelity Soil Moisture Products Across the Continental United States

We have aggregated the most recent soil moisture datasets from a diverse range of sources, encompassing the Continental United States (CONUS). These sources encompass gridded data from remote sensing products, reanalysis products, machine learning-based projects, and land surface modeling products. Additionally, we have obtained and processed in-situ soil moisture observations from the International Soil Moisture Network. The collected datasets exhibit variations in both temporal and spatial resolutions. Among the 20 datasets, six are available at a spatial resolution of 0.25 degrees, while three are at a coarser spatial resolution of 25 km. To minimize spatial interpolation, we conducted data uncertainty evaluations at the 0.25-degree spatial resolution. For our data evaluations, we maintained a monthly temporal resolution, which effectively captures soil moisture seasonality and interannual variability. Our data processing strategy preserves the raw data and interpolated data at their original temporal resolutions. Datasets with higher temporal resolutions, including daily, three-hourly, and hourly datasets, are set aside for subsequent analyses. These analyses will delve into topics such as soil moisture changes and recovery during extreme weather events. Furthermore, we have processed auxiliary data to enhance our evaluation, leveraging tools such as Google Earth Engine. This includes incorporating topography data, land use land cover data, Köppen-Geiger climate classification, and more to provide a comprehensive assessment from multiple sources.

Li, Lingcheng↗
NASA NTRS1984

A 154-day periodicity in the occurrence of hard solar flares?

An analysis of the temporal distribution of 139 solar flares monitored by the Gamma Ray Spectrometer aboard the Solar Maximum Mission is reported. It is found that, instead of being randomly distributed in time, these events have a tendency to occur in groups with a mean spacing of about 154 days (75 nHz) over the observing interval. A larger sample of flares with an X-ray classification of M 2.5 or larger recorded by the GOES satellite showed a similar regularity.

Rieger, E.↗
NASA NTRS1992

Research Instruments

The GENETI-SCANNER, newest product of Perceptive Scientific Instruments, Inc. (PSI), rapidly scans slides, locates, digitizes, measures and classifies specific objects and events in research and diagnostic applications. Founded by former NASA employees, PSI's primary product line is based on NASA image processing technology. The instruments karyotype - a process employed in analysis and classification of chromosomes - using a video camera mounted on a microscope. Images are digitized, enabling chromosome image enhancement. The system enables karyotyping to be done significantly faster, increasing productivity and lowering costs. Product is no longer being manufactured.

Source record↗
DOE OSTI2017

Automated Proton Track Identification in MicroBooNE Using Gradient Boosted Decision Trees

MicroBooNE is a liquid argon time projection chamber (LArTPC) neutrino experiment that is currently running in the Booster Neutrino Beam at Fermilab. LArTPC technology allows for high-resolution, three-dimensional representations of neutrino interactions. A wide variety of software tools for automated reconstruction and selection of particle tracks in LArTPCs are actively being developed. Short, isolated proton tracks, the signal for low- momentum-transfer neutral current (NC) elastic events, are easily hidden in a large cosmic background. Detecting these low-energy tracks will allow us to probe interesting regions of the proton's spin structure. An effective method for selecting NC elastic events is to combine a highly efficient track reconstruction algorithm to find all candidate tracks with highly accurate particle identification using a machine learning algorithm. We present our work on particle track classification using gradient tree boosting software (XGBoost) and the performance on simulated neutrino data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

CryoEM reveals the stochastic nature of individual ATP binding events in a group II chaperonin

Chaperonins are homo- or hetero-oligomeric complexes that use ATP binding and hydrolysis to facilitate protein folding. ATP hydrolysis exhibits both positive and negative cooperativity. The mechanism by which chaperonins coordinate ATP utilization in their multiple subunits remains unclear. Here we use cryoEM to study ATP binding in the homo-oligomeric archaeal chaperonin from Methanococcus maripaludis (MmCpn), consisting of two stacked rings composed of eight identical subunits each. Using a series of image classification steps, we obtained different structural snapshots of individual chaperonins undergoing the nucleotide binding process. We identified nucleotide-bound and free states of individual subunits in each chaperonin, allowing us to determine the ATP occupancy state of each MmCpn particle. We observe distinctive tertiary and quaternary structures reflecting variations in nucleotide occupancy and subunit conformations in each chaperonin complex. Detailed analysis of the nucleotide distribution in each MmCpn complex indicates that individual ATP binding events occur in a statistically random manner for MmCpn, both within and across the rings. Our findings illustrate the power of cryoEM to characterize a biochemical property of multi-subunit ligand binding cooperativity at the individual particle level.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2007

Application of Multi-Satellite Precipitation Analysis to Floods and Landslides

Satellite data acquired and processed in real time now have the potential to provide the spacetime information on rainfall needed to monitor flood and landslide events around the world. This can be achieved by integrating the satellite-derived forcing data with hydrological models and landslide algorithms. Progress in using the TRMM Multi-satellite Precipitation Analysis (TMPA) as input to flood and landslide forecasts is outlined, with a focus on understanding limitations of the rainfall data and impacts of those limitations on flood/landslide analyses. Case studies of both successes and failures will be shown, as well as comparison with ground comparison data sets both in terms of rainfall and in terms of flood/landslide events. In addition to potential uses in real-time, the nearly ten years of TMPA data allow retrospective running of the models to examine variations in extreme events. The flood determination algorithm consists of four major components: 1) multi-satellite precipitation estimation; 2) characterization of land surface including digital elevation from NASA SRTM (Shuttle Radar Terrain Mission), topography-derived hydrologic parameters such as flow direction, flow accumulation, basin, and river network etc.; 3) a hydrological model to infiltrate rainfall and route overland runoff; and 4) an implementation interface to relay the input data to the models and display the flood inundation results to potential users and decision-makers. In terms of landslides, the satellite rainfall information is combined with a global landslide susceptibility map, derived from a combination of global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a weighted linear combination approach. In those areas identified as "susceptible" (based on the surface characteristics), landslides are forecast where and when a rainfall intensity/duration threshold is exceeded. Results are described indicating general agreement with landslide occurrences. However, difficulties in comparing landslide event information (mostly from news reports) with the satellite-based forecasts are analyzed.

Adler, Robert↗
NASA NTRS2000

Gamma-Ray Burst Class Properties

Guided by the supervised pattern recognition algorithm C4.5 developed by Quinlan in 1986, we examine the three gamma-ray burst classes identified by Mukherjee et al. in 1998. C4.5 provides strong statistical support for this classification. However, with C4.5 and our knowledge of the Burst and Transient Source Experiment (BATSE) instrument, we demonstrate that class 3 (intermediate fluence, intermediate duration, soft) does not have to be a distinct source population: statistical/systematic errors in measuring burst attributes combined with the well-known hardness/intensity correlation can cause low peak flux class 1 (high fluence, long, intermediate hardness) bursts to take on class 3 characteristics naturally. Based on our hypothesis that the third class is not a distinct one, we provide rules so that future events can be placed in either class 1 or class 2 (low fluence, short, hard). We find that the two classes are relatively distinct on the basis of Band's work in 1993 on spectral parameters alpha, beta, and E (sub peak) alone. Although this does not indicate a better basis for classification, it does suggest that different physical conditions exist for class 1 and class 2 bursts. In the process of studying burst class characteristics, we identify a new bias affecting burst fluence and duration measurements. Using a simple model of how burst duration can be underestimated, we show how this fluence duration bias can affect BATSE measurements and demonstrate the type of effect it can have on the BATSE fluence versus peak flux diagram.

Hakkila, Jon↗
DOE OSTI2020

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Benchmark Dose Analysis of DNA Damage Biomarker Responses Provides Compound Potency and Adverse Outcome Pathway Information for the Topoisomerase II Inhibitor Class of Compounds

Genetic toxicology data have traditionally been utilized for hazard identification to provide a binary call for a compound's risk. Recent advances in the scientific field, especially with the development of high‐throughput methods to quantify DNA damage, have influenced a change of approach in genotoxicity assessment. The in vitro MultiFlow® DNA Damage Assay is one such method which multiplexes γH2AX, p53, phospho‐histone H3 biomarkers into a single‐flow cytometric analysis (Bryce et al., [2016]: Environ Mol Mutagen 57:546–558). This assay was used to study human TK6 cells exposed to each of eight topoisomerase II poisons for 4 and 24 hr. Using PROAST v65.5, the Benchmark Dose approach was applied to the resulting flow cytometric datasets. With “compound” serving as covariate, all eight compounds were combined into a single analysis, per time point and endpoint. The resulting 90% confidence intervals, plotted in Log scale, were considered as the potency rank for the eight compounds. The in vitro MultiFlow data showed a maximum confidence interval span of 1Log, which indicates data of good quality. Patterns observed in the compound potency rank were scrutinized by using the expert rule‐based software program Derek Nexus, developed by Lhasa Limited. Compound sub‐classification and structural alerts were considered contributory to the potencies observed for the topoisomerase II poisons studied herein. The Topo II poison Adverse Outcome Pathway was evaluated with MultiFlow endpoints serving as Key Events. The step‐wise approach described herein can be considered as a foundation for risk assessment of compounds within a specific mode of action of interest. Environ. Mol. Mutagen. 2020. © 2020 Wiley Periodicals, Inc.

Wheeldon, Ryan P.↗
DOE OSTI2018

(Machine) learning to do more with less

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard “fully supervised” approach (which relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called “weakly supervised” technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail — both analytically and numerically — with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to a class of systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC. Example code is provided on GitHub.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2011

QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies {approx}80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

PlasmoData.jl — A Julia framework for modeling and analyzing complex data as graphs

Datasets encountered in scientific and engineering applications appear in complex formats (e.g., images, multivariate time series, molecules, video, text strings, networks). Graph theory provides a unifying framework to model such datasets and enables the use of powerful tools that can help analyze, visualize, and extract value from data. In this work, we present PlasmoData.jl, an open-source, Julia framework that uses concepts of graph theory to facilitate the modeling and analysis of complex datasets. The core of our framework is a general data modeling abstraction, which we call a DataGraph. We show how the abstraction and software implementation can be used to represent diverse data objects as graphs and to enable the use of tools from topology, graph theory, and machine learning (e.g., graph neural networks) to conduct a variety of tasks. We illustrate the versatility of the framework by using real datasets: (i) an image classification problem using topological data analysis to extract features from the graph model to train machine learning models; (ii) a disease outbreak problem where we model multivariate time series as graphs to detect abnormal events; and (iii) a technology pathway analysis problem where we highlight how we can use graphs to navigate connectivity. Further, our discussion also highlights how PlasmoData.jl leverages native Julia capabilities to enable compact syntax, scalable computations, and interfaces with diverse packages. Overall, we show that the DataGraph abstraction and PlasmoData.jl Julia package are able to model data within graphs and enable useful analysis.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1986

The Maryville meteorite - A 1983 fall of an L6 chondrite

The Maryville chondrite fell on January 28, 1983 in eastern Tennessee. Compositions of olivine (Fa 24.5), orthopyroxene (Fs 20.8), plagioclase (An 10.6), along with the bulk composition and siderophile concentrations, indicate L-group classification. The presence of highly equilibrated minerals, strongly recrystallized matrix, and the development of large, clear plagioclase grains suggest petrologic type 6 classification. Subsequent to metamorphism the meteorite was subjected to high transient pressures that converted some feldspar to glass, deformed the silicates, and caused small amounts of melting to occur. The effects of this shock event correspond to shock facies 'd' or 'e'.

Shervais, J. W.↗
DOE OSTI2014

Host galaxy spectra and consequences for supernova typing from the SDSS SN survey

We present the spectroscopy from 5254 galaxies that hosted supernovae (SNe) or other transient events in the Sloan Digital Sky Survey II (SDSS-II). Obtained during SDSS-I, SDSS-II, and the Baryon Oscillation Spectroscopic Survey, this sample represents the largest systematic, unbiased, magnitude limited spectroscopic survey of SN host galaxies. Using the host galaxy redshifts, we test the impact of photometric SN classification based on SDSS imaging data with and without using spectroscopic redshifts of the host galaxies. Following our suggested scheme, there are a total of 1166 photometrically classified SNe Ia when using a flat redshift prior and 1126 SNe Ia when the host spectroscopic redshift is assumed. For 1024 (87.8%) candidates classified as likely SNe Ia without redshift information, we find that the classification is unchanged when adding the host galaxy redshift. Using photometry from SDSS imaging data and the host galaxy spectra, we also report host galaxy properties for use in future analysis of SN astrophysics. Finally, we investigate the differences in the interpretation of the light curve properties with and without knowledge of the redshift. Without host galaxy redshifts, we find that SALT2 light curve fits are systematically biased toward lower photometric redshift estimates and redder colors in the limit of low signal-to-noise data. The general improvements in performance of the light curve fitter and the increased diversity of the host galaxy sample highlights the importance of host galaxy spectroscopy for current photometric SN surveys such as the Dark Energy Survey and future surveys such as the Large Synoptic Survey Telescope.

79 ASTRONOMY AND ASTROPHYSICS↗