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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Protocadherin cis -dimer architecture and recognition unit diversity

Clustered protocadherins (Pcdhs) mediate numerous neural patterning functions, including neuronal self-recognition and non–self-discrimination to direct self-avoidance among vertebrate neurons. Individual neurons stochastically express a subset of Pcdh isoforms, which assemble to form a stochastic repertoire of cis -dimers. We describe herein the structure of a PcdhγB7 cis -homodimer, which includes the membrane-proximal extracellular cadherin domains EC5 and EC6. The structure is asymmetric with one molecule contributing interface surface from both EC5 and EC6, and the other only from EC6. Structural and sequence analyses suggest that all Pcdh isoforms will dimerize through this interface. Site-directed mutants at this interface interfere with both Pcdh cis -dimerization and cell surface transport. The structure explains the known restrictions of cis -interactions of some Pcdh isoforms, including α-Pcdhs, which cannot form homodimers. The asymmetry of the interface approximately doubles the size of the recognition repertoire, and restrictions on cis -interactions among Pcdh isoforms define the limits of the Pcdh recognition unit repertoire.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Experimental observation of chimera and cluster states in a minimal globally coupled network

A “chimera state” is a dynamical pattern that occurs in a network of coupled identical oscillators when the symmetry of the oscillator population is broken into synchronous and asynchronous parts. We report the experimental observation of chimera and cluster states in a network of four globally coupled chaotic opto-electronic oscillators. This is the minimal network that can support chimera states, and our study provides new insight into the fundamental mechanisms underlying their formation. We use a unified approach to determine the stability of all the observed partially synchronous patterns, highlighting the close relationship between chimera and cluster states as belonging to the broader phenomenon of partial synchronization. Our approach is general in terms of network size and connectivity. We also find that chimera states often appear in regions of multistability between global, cluster, and desynchronized states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Combining Tensor Decompositions and Graph Analytics to Provide Cyber Situational Awareness at HPC Scale

This paper describes MADHAT (Multidimensional Anomaly Detection fusing HPC, Analytics, and Tensors), an integrated workflow that demonstrates the applicability of HPC resources to the problem of maintaining cyber situational awareness. MADHAT combines two high-performance packages: ENSIGN for large-scale sparse tensor decompositions and HAGGLE for graph analytics. Tensor decompositions isolate coherent patterns of network behavior in ways that common clustering methods based on distance metrics cannot. Parallelized graph analysis then uses directed queries on a representation that combines the elements of identified patterns with other available information (such as additional log fields, domain knowledge, network topology, whitelists and blacklists, prior feedback, and published alerts) to confirm or reject a threat hypothesis, collect context, and raise alerts. MADHAT was developed using the collaborative HPC Architecture for Cyber Situational Awareness (HACSAW) research environment and evaluated on structured network sensor logs collected from Defense Research and Engineering Network (DREN) sites using HPC resources at the U.S. Army Engineer Research and Development Center DoD Supercomputing Resource Center (ERDC DSRC). To date, MADHAT has analyzed logs with over 650 million entries.

Cyber security, Tensor decomposition, graph analyt↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Modeling and simulation of the data communication network at the ASRM Facility

This paper describes the modeling and simulation of the communication network for the NASA Advanced Solid Rocket Motor (ASRM) facility under construction at Yellow Creek near Luka, Mississippi. Manufacturing, testing, and operations at the ASRM site will be performed in different buildings scattered over an 1800 acre site. These buildings are interconnected through a local area network (LAN), which will contain one logical Fiber Distributed Data Interface (FDDI) ring acting as a backbone for the whole complex. The network contains approximately 700 multi-vendor workstations, 22 multi-vendor workcells, and 3 VAX clusters interconnected via Ethernet and FDDI. The different devices produce appreciably different traffic patterns, each pattern will be highly variable, and some patterns will be very bursty. Most traffic is between the VAX clusters and the other devices. Comdisco's Block Oriented Network Simulator (BONeS) has been used for network simulation. The two primary evaluation parameters used to judge the expected network performance are throughput and delay.

Nirgudkar, R. P.↗
DOE OSTI2018

NMR-based serum metabolomics study reveals a innovative diagnostic model for missed abortion

A missed abortion (MA) is an in-utero death of the embryo or fetus before the 20th week of gestation with retained products of conception. In order to discover novel biomarkers for MA, a {sup 1}H NMR spectroscopy-based metabolomics approach was applied to detect human MA serum metabolic profiles. Serum samples were obtained from patients with MA (n = 15) and healthy controls (n = 9) for study. The NOESYPR1D spectrum combined with multi-variate pattern recognition analysis was used to cluster the groups and establish a disease-specific metabolites phenotype. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) models were capable of distinguishing MA patients from healthy subjects. The results revealed that 24 metabolites altered in MA patients compared with the control population. Metabolomic pathway analysis demonstrated that alanine, aspartate and glutamate metabolism, citrate cycle (TCA cycle), taurine and hypotaurine metabolism were significantly altered in MA. The results indicated that serum NMR-based metabolomic profiling method is sensitive and specific enough to distinguish MA and from healthy controls, this method could be developed as a clinically useful diagnostic tool for MA. The finding from the MA serum metabolic profiling shed a new light on further understanding of MA disease mechanisms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

STAR CLUSTERS IN A NUCLEAR STAR FORMING RING: THE DISAPPEARING STRING OF PEARLS

An analysis of the star cluster population in a low-luminosity early-type galaxy, NGC 2328, is presented. The clusters are found in a tight star forming nuclear spiral/ring pattern and we also identify a bar from structural two-dimensional decomposition. These massive clusters are forming very efficiently in the circumnuclear environment and they are young, possibly all less than 30 Myr of age. The clusters indicate an azimuthal age gradient, consistent with a ''pearls-on-a-string'' formation scenario, suggesting bar-driven gas inflow. The cluster mass function has a robust down turn at low masses at all age bins. Assuming clusters are born with a power-law distribution, this indicates extremely rapid disruption at timescales of just several million years. If found to be typical, it means that clusters born in dense circumnuclear rings do not survive to become old globular clusters in non-interacting systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

APOGEE detection of N-rich stars in the tidal tails of Palomar 5

Recent results from chemical tagging studies using Apache Point Observatory Galactic Evolution Experiment data suggest a strong link between the chemical abundance patterns of stars found within globular clusters (GC), and chemically peculiar populations in the Galactic halo field. In this paper, we analyse the chemical compositions of stars within the cluster body and tidal streams of Palomar 5, a GC that is being tidally disrupted by interaction with the Galactic gravitational potential. Here we report the identification of nitrogen-rich (N-rich) stars both within and beyond the tidal radius of Palomar 5, with the latter being clearly aligned with the cluster tidal streams; this acts as confirmation that N-rich stars are lost to the Galactic halo from GCs, and provides support to the hypothesis that field N-rich stars identified by various groups have a GC origin.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Multispectroscopic Study of Single Xe Clusters Using XFEL Pulses

X-ray free-electron lasers (XFELs) deliver ultrashort coherent laser pulses in the X-ray spectral regime, enabling novel investigations into the structure of individual nanoscale samples. In this work, we demonstrate how single-shot small-angle X-ray scattering (SAXS) measurements combined with fluorescence and ion time-of-flight (TOF) spectroscopy can be used to obtain size- and structure-selective evaluation of the light-matter interaction processes on the nanoscale. We recorded the SAXS images of single xenon clusters using XFEL pulses provided by the SPring-8 Angstrom compact free-electron laser (SACLA). The XFEL fluences and the radii of the clusters at the reaction point were evaluated and the ion TOF spectra and fluorescence spectra were sorted accordingly. We found that the XFEL fluence and cluster size extracted from the diffraction patterns showed a clear correlation with the fluorescence and ion TOF spectra. Our results demonstrate the effectiveness of the multispectroscopic approach for exploring laser–matter interaction in the X-ray regime without the influence of the size distribution of samples and the fluence distribution of the incident XFEL pulses.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1985

Anatomic evidence for peripheral neural processing in mammalian graviceptors

Ultrastructural study of utricular and saccular maculas demonstrates that their innervation patterns are complex. There is a clustering of type I and type II hair cells based upon a sharing of afferents, a system of efferent-type beaded fibers that is of intramacular (mostly calyceal) origin, and a plexus-like arrangement of afferents and efferents at many sites in the neuroepithelium. Results suggest that information concerning linear acceleration is processed peripherally, beginning at the hair cell level, before being sent to the central nervous system. The findings may supply a structural basis for peripheral adaptation to a constant stimulus, and for lateral inhibition to improve signal relative to noise.

Ross, M. D.↗
NASA NTRS1989

Personality based clusters as predictors of aviator attitudes and performance

The feasibility of identification of personality-based population clusters was investigated along with the relationships of these subpopulations to relevant attitude and performance measures. The results of instrumental and expressive personality tests, using the Personal Characteristics Inventory (PCI) test battery and the Cockpit Management Attitudes Questionnaire, suggest that theoretically meaningful subpopulations exist among aviators, and that these groupings are useful in understanding of personality factors acting as moderator variables in the determination of aviator attitudes and performance. Out of the three clusters most easily described in terms of their relative elevations on the PCI subscales ('the right stuff', the 'wrong stuff', and the 'no stuff'), the members of the right stuff cluster tended to have more desirable patterns of responses along relevant attitudinal dimensions.

Gregorich, Steve↗
NASA NTRS1990

Predicting thunderstorm evolution using ground-based lightning detection networks

Lightning measurements acquired principally by a ground-based network of magnetic direction finders are used to diagnose and predict the existence, temporal evolution, and decay of thunderstorms over a wide range of space and time scales extending over four orders of magnitude. The non-linear growth and decay of thunderstorms and their accompanying cloud-to-ground lightning activity is described by the three parameter logistic growth model. The growth rate is shown to be a function of the storm size and duration, and the limiting value of the total lightning activity is related to the available energy in the environment. A new technique is described for removing systematic bearing errors from direction finder data where radar echoes are used to constrain site error correction and optimization (best point estimate) algorithms. A nearest neighbor pattern recognition algorithm is employed to cluster the discrete lightning discharges into storm cells and the advantages and limitations of different clustering strategies for storm identification and tracking are examined.

Goodman, Steven J.↗
NASA NTRS2021

Identifying Meteorological Influences on Marine Low Cloud Mesoscale Morphology Using Satellite Classifications

Marine low cloud mesoscale morphology in the southeastern Pacific Ocean is analyzed using a large dataset of machine-learning generated classifications spanning three years. Meteorological variables and cloud properties are composited 10by mesoscale cloud type, showing distinct meteorological regimes of marine low cloud organization from the tropics to the midlatitudes. The presentation of mesoscale cellular convection, with respect to geographic distribution, boundary layer structure, and large-scale environmental conditions, agrees with prior knowledge. Two tropical and subtropical cumuliform boundary layer regimes, suppressed cumulus and clustered cumulus, are studied in detail. The patterns in precipitation, circulation, column water vapor, and cloudiness are consistent with the representation of marine shallow mesoscale convective 15 self-aggregation by large eddy simulations of the boundary layer. Although they occur under similar large-scale conditions, the suppressed and clustered low cloud types are found to be well-separated by variables associated with low-level mesoscale circulation, with surface wind divergence being the clearest discriminator between them, whether reanalysis or satellite observations are used. Clustered regimes are associated with surface convergence and suppressed regimes are associated with surface divergence.

Johannes Mohrmann↗
DOE OSTI2025

Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning

Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.

58 GEOSCIENCES↗
DOE OSTI2015

FEEDBACK FROM MASSIVE STARS AND GAS EXPULSION FROM PROTO-GLOBULAR CLUSTERS

Globular clusters (GCs) are considerably more complex structures than previously thought, harboring at least two stellar generations that present clearly distinct chemical abundances. Scenarios explaining the abundance patterns in GCs mostly assume that originally the clusters had to be much more massive than today, and that the second generation of stars originates from the gas shed by stars of the first generation (FG). The lack of metallicity spread in most GCs further requires that the supernova-enriched gas ejected by the FG is completely lost within ∼30 Myr, a hypothesis never tested by means of three-dimensional hydrodynamic simulations. In this paper, we use 3D hydrodynamic simulations including stellar feedback from winds and supernovae, radiative cooling and self-gravity to study whether a realistic distribution of OB associations in a massive proto-GC of initial mass M{sub tot} ∼ 10{sup 7} M{sub ⊙} is sufficient to expel its entire gas content. Our numerical experiment shows that the coherence of different associations plays a fundamental role: as the bubbles interact, distort, and merge, they carve narrow tunnels that reach deeper and deeper toward the innermost cluster regions, and through which the gas is able to escape. Our results indicate that after 3 Myr, the feedback from stellar winds is responsible for the removal of ∼40% of the pristine gas, and that after 14 Myr, 99% of the initial gas mass has been removed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Modified microstructures in proton irradiated dual phase 308L weldment filler material

In this study, the effect of proton irradiation on the microstructure of δ ferrite—γ austenite mixed phase 308L filler material in a 508–304 dissimilar metal weldment was investigated over a depth of 0 to 10 µm. Ni–Si–Mn G-phase precipitates were observed with SEM and TEM in δ ferrite but not in γ austenite. Our density functional theory based calculations show that the G/Fe interface energy in δ-Fe is significantly lower than that in γ-Fe (0.35 versus 1.25 J/m 2 ), which provides a thermodynamics-based explanation for our experimental observations of preferential formation of G-phase in δ ferrite. STEM-EDS, TEM dark field imaging, and diffraction patterns confirmed the Ni–Si–Mn enriched precipitates were G-phase precipitates with a stoichiometry of Mn 6 Ni 16 Si 7 . Intragranular voids and Ni–Si enriched clusters were observed in irradiated γ austenite. Additionally, Ni and Si segregation was observed along the void interfaces. In both cases, Ni–Si clusters and segregation to voids, selected area diffraction patterns did not reveal the existence of a second phase. Proton irradiation induced Cr depletion and Si and Ni enrichment at γ-γ austenite grain boundaries that was characterized with STEM/EDS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Generation and structure of extremely large clusters in pulsed jets

Extremely large xenon clusters with sizes exceeding the predictions of the Hagena scaling law by several orders of magnitude are shown to be produced in pulsed gas jets. The cluster sizes are determined using single-shot single-particle imaging experiments with short-wavelength light pulses from the free-electron laser in Hamburg (FLASH). Scanning the time delay between the pulsed cluster source and the intense femtosecond x-ray pulses first shows a main plateau with size distributions in line with the scaling laws, which is followed by an after-pulse of giant clusters. For the extremely large clusters with radii of several hundred nanometers the x-ray scattering patterns indicate a grainy substructure of the particles, suggesting that they grow by cluster coagulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1980

Probabilistic cluster labeling of imagery data

The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.

Chittineni, C. B.↗