Search NASA⌕ Search

SEARCH · Search NASA

Results for “Processing maps”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A RADIAL VELOCITY TEST FOR SUPERMASSIVE BLACK HOLE BINARIES AS AN EXPLANATION FOR BROAD, DOUBLE-PEAKED EMISSION LINES IN ACTIVE GALACTIC NUCLEI

One of the proposed explanations for the broad, double-peaked Balmer emission lines observed in the spectra of some active galactic nuclei (AGNs) is that they are associated with sub-parsec supermassive black hole (SMBH) binaries. Here, we test the binary broad-line region hypothesis through several decades of monitoring of the velocity structure of double-peaked Hα emission lines in 13 low-redshift, mostly radio-loud AGNs. This is a much larger set of objects compared to an earlier test by Eracleous et al. and we use much longer time series for the three objects studied in that paper. Although systematic changes in radial velocity can be traced in many of their lines, they are demonstrably not like those of a spectroscopic binary in a circular orbit. Any spectroscopic binary period must therefore be much longer than the span of the monitoring (assuming a circular orbit), which in turn would require black hole masses that exceed by 1–2 orders of magnitude the values obtained for these objects using techniques such as reverberation mapping and stellar velocity dispersion. Moreover, the response of the double-peaked Balmer line profiles to fluctuations of the ionizing continuum and the shape of the Lyα profiles are incompatible with an SMBH binary. The binary broad-line region hypothesis is therefore disfavored. Other processes evidently shape these line profiles and cause the long-term velocity variations of the double peaks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The separation of vibrational coherence from ground- and excited-electronic states in P3HT film

Concurrence of the vibrational coherence and ultrafast electron transfer has been observed in polymer/fullerene blends. However, it is difficult to experimentally investigate the role that the excited-state vibrational coherence plays during the electron transfer process since vibrational coherence from the ground- and excited-electronic states is usually temporally and spectrally overlapped. Here, we performed 2-dimensional electronic spectroscopy (2D ES) measurements on poly(3-hexylthiophene) (P3HT) films. By Fourier transforming the whole 2D ES datasets (S(λ{sub 1},T{sup ~}{sub 2},λ{sub 3})) along the population time (T{sup ~}{sub 2}) axis, we develop and propose a protocol capable of separating vibrational coherence from the ground- and excited-electronic states in 3D rephasing and nonrephasing beating maps (S(λ{sub 1},ν{sup ~}{sub 2},λ{sub 3})). We found that the vibrational coherence from pure excited electronic states appears at positive frequency (+ν{sup ~}{sub 2}) in the rephasing beating map and at negative frequency (−ν{sup ~}{sub 2}) in the nonrephasing beating map. Furthermore, we also found that vibrational coherence from excited electronic state had a long dephasing time of 244 fs. The long-lived excited-state vibrational coherence indicates that coherence may be involved in the electron transfer process. Our findings not only shed light on the mechanism of ultrafast electron transfer in organic photovoltaics but also are beneficial for the study of the coherence effect on photoexcited dynamics in other systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

PAndAS' PROGENY: EXTENDING THE M31 DWARF GALAXY CABAL

We present the discovery of five new dwarf galaxies, Andromeda XXIII-XXVII, located in the outer halo of M31. These galaxies were discovered during the second year of data from the Pan-Andromeda Archaeological Survey (PAndAS), a photometric survey of the M31/M33 subgroup conducted with the MegaPrime/MegaCam wide-field camera on the Canada-France-Hawaii Telescope. The current PAndAS survey now provides an almost complete panoramic view of the M31 halo out to an average projected radius of {approx}150 kpc. Here we present for the first time the metal-poor stellar density map for this whole region, not only as an illustration of the discovery space for satellite galaxies, but also as a birds-eye view of the ongoing assembly process of an L{sub *} disk galaxy. Four of the newly discovered satellites appear as well-defined spatial overdensities of stars lying on the expected locus of metal-poor (-2.5 < [Fe/H] < -1.3) red giant branch stars at the distance of M31. The fifth overdensity, And XXVII, is embedded in an extensive stream of such stars and is possibly the remnant of a strong tidal disruption event. Based on distance estimates from horizontal branch magnitudes, all five have metallicities typical of dwarf spheroidal galaxies ranging from [Fe/H] =-1.7 {+-} 0.2 to [Fe/H] =-1.9 {+-} 0.2 and absolute magnitudes ranging from M{sub V} = -7.1 {+-} 0.5 to M{sub V} = -10.2 {+-} 0.5. These five additional satellites bring the number of dwarf spheroidal galaxies in this region to 25 and continue the trend whereby the brighter dwarf spheroidal satellites of M31 generally have much larger half-light radii than their Milky Way counterparts. With an extended sample of M31 satellite galaxies, we also revisit the spatial distribution of this population and in particular we find that, within the current projected limits of the PAndAS survey, the surface density of satellites is essentially constant out to 150 kpc. This corresponds to a radial density distribution of satellites varying as r{sup -1}, a result seemingly in conflict with the predictions of cosmological simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Neutron Diffraction Residual Stress Study of a AA2219-T87 Self-Reacting Friction Stir Weld

2219-T87 is a precipitation hardenable aluminum-copper alloy which sees wide use in structural aerospace components. Thick panels of this alloy are joined via self-reacting friction welding (SRFSW); however, this thermomechanical process can result in significant loss of the alloy’s strengthening precipitates and large residual stresses which are detrimental to mechanical behavior and performance. High-resolution maps of the residual strain and stress states along the normal, transverse, and longitudinal directions of 2219-T87 SRFSW were obtained using neutron diffraction measurements. Residual stress had the highest tensile value in the heat-affected zone (HAZ) and the largest compressive stress in the base metal region of the normal and transverse directions. Line profile residual stress distributions displayed an “M”-shaped distribution in each direction with the longitudinal direction being the most pronounced while 2D residual stresses displayed an hourglass-shaped pattern. Further, average maximal longitudinal residual stress values ranged from 60.6 to 85.6% of yield. Microhardness testing across the transverse weld section produced a “V”-shaped curve with slight hardness recovery in the stir zone. Maximal and minimal microhardness values were observed in the base metal (154 HV) and thermomechanically affected zone (82 HV), respectively. Microstructural evolution was recorded using optical microscopy and showed decreasing grain size from the HAZ to the stir zone.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep-freeze graph training for latent learning

Scientific and engineering advances are primarily driven by multi-tier conceptual constructs and conditional theoretical frameworks. The theories allow predictions of hypothetical system responses, given a set of approximate conditions (ranges of applicability) imposed on latent parameters that cannot be measured directly. Learning to estimate the latent variables (Latent Learning) helps to pinpoint the anticipated range-edge anomalies and improves the confidence in interpretation, interpolation and extrapolation of limited experimental data. Due to high dimensionality and extreme non-linearity of the materials science problems, very large datasets are typically required for conventional data-driven model development. The vital experimental data collection, particularly on microstructural phases, is very challenging, which makes it difficult to compile a high-quality database. Incorporation of the domain knowledge into the computational graph structure, initialization and optimization processes presents a viable mechanism for developing accurate models, with limited datasets. Furthermore, this study successfully utilized the approach to build the Deep Freeze Graph (DeepFreG) by mapping known causality relationships and by digitizing empirical domain knowledge for Latent Learning (LL), with specific applications in materials science.

36 MATERIALS SCIENCE↗
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↗
DOE OSTI2025

Land-use analysis using infrastructure representations and high-resolution flood inundation mapping techniques

In the face of climate change and population growth in coastal regions, land-use analysis efforts are more challenging than ever. Land-use decision-makers in coastal communities are burdened with the difficult choices of where to place new homes versus other assets. While there has been an increased focus on hazard mitigation and disaster resilience in the field of planning, evidence points towards continued development in risk-prone areas including flood zones. Residential development within flood zones specifically continues to be a major issue. To help counter this trend, this study introduces a novel land-use analysis method, coupling topographic flood inundation mapping techniques with digital elevation model (DEM) adaptations. This Topographic Model Scenario Generation workflow can be used by planners early in the land-use decision making process and provides an alternative to high-computational hydraulic models. The analysis also includes the identification of strengths and weaknesses of topographic models' recognition of built infrastructure assets, adding to a limited body of knowledge addressing recommended uses of such models. Levees and canals prove particularly functional in this context while detention ponds less so, likely due to a lack of total water mass accountability. Lastly, we provide a functional demonstration in Southeast Texas to illustrate the workflow's ability to create multiple infrastructure scenarios and visualize their effects across different flood events.

42 ENGINEERING↗
DOE OSTI2021

Tuning Electrogenerated Chemiluminescence Intensity Enhancement Using Hexagonal Lattice Arrays of Gold Nanodisks

Electrogenerated chemiluminescence (ECL) microscopy shows promise as a technique for mapping chemical reactions on single nanoparticles. The technique’s spatial resolution is limited by the quantum yield of the emission and the diffusive nature of the ECL process. To improve signal intensity, ECL dyes have been coupled with plasmonic nanoparticles, which act as nanoantennas. Here, we characterize the optical properties of hexagonal arrays of gold nanodisks and how they impact the enhancement of ECL from the coreaction of tris(2,2'-bipyridyl)dichlororuthenium(II) hexahydrate and tripropylamine. Here, we find that varying the lattice spacing results in a 23-fold enhancement of ECL intensity because of increased dye-array near-field coupling as modeled using finite element method simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Small Reservoirs Offer a New Perspective on Flood Reduction in Large Basins

The flood reduction potential of individual reservoirs within a large river network continuum remains poorly understood due to the complex interplay between reservoir characteristics and network properties. Here we investigate whether a collection of relatively small reservoirs can play a significant role in mediating downstream floods and assess how that role may be influenced by reservoir network properties compared to traditionally known reservoir characteristics. Our unique contribution was the simulation of downstream flood inundation maps alongside peak flows for each of the 81 major reservoirs (6 × 104 to 8 × 109 m3) across 15,000 river reach segments, integrated into a process-based hydrologic model covering a 415,000 km2 region in the Texas Gulf Coast, United States. The three key takeaways from our study are as follows. (a) Smaller reservoirs can substantially reduce downstream flooding, suggesting that inclusion of large reservoirs—the traditional approach in flood risk management studies—may present only a partial picture. (b) Flood reduction by smaller reservoirs is more effective upstream, although this phenomenon may be linked with aridity and overall water availability. (c) While reservoir size matters, it is not the primary factor determining its downstream flood reduction potential; the influence of network properties, such as catchment area, the count of upstream reservoirs, the cumulative maximum storage capacity of upstream reservoirs, and Stream Order (i.e., location), is equally and often more important. The broader impact of our findings goes beyond just floods, providing foundational insights for addressing emerging challenges such as aging dams and river connectivity.

Patel, Krutikkumar [University of Texas at Arlingt↗
DOE OSTI2019

Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Abstract Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2014

Stability of polarized states for diamond valleytronics

The stability of valley polarized electron states is crucial for the development of valleytronics. A long relaxation time of the valley polarization is required to enable operations to be performed on the polarized states. Here, we investigate the stability of valley polarized states in diamond, expressed as relaxation time. We have found that the stability of the states can be extremely long when we consider the electron-phonon scattering processes allowed by symmetry considerations. We determine electron-phonon coupling constants by Time-of-Flight measurements and Monte Carlo simulations and use these data to map out the relaxation time temperature dependency. The relaxation time for diamond can be microseconds or longer below 100 K and 100 V/cm due to the strong covalent bond, which is highly encouraging for future use in valleytronic applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Vapor phase deposition of alumina on interior surfaces of tritium storage containers

Efficient tritium management and containment are critical challenges for nuclear fusion. To mitigate issues related to permeation into structural materials and isotopic exchange with protium, permeation barriers must be applied to the nonplanar internal surfaces of tritium handling components. In this study, we developed an atomic layer deposition (ALD) system to deposit films on canisters used to store and transport tritium. Growth conditions for deposition of Al 2 O 3 were mapped spatially along the length of a canister using silicon witness samples characterized by ellipsometry, atomic force microscopy, and x-ray reflectivity (XRR). We identified ALD process windows for growth at temperatures between 100 and 140 °C. Furthermore, we compared films grown on our Si witness samples to those grown on polished stainless steel 316 (SS316). X-ray photoelectron spectroscopy indicates two distinct chemical states for the alumina, while XRR confirms that the overall growth rate is similar to the 1.1 Å/cycle that we observed on the Si witness samples.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Multimodal x-ray and electron microscopy of the Allende meteorite

Multimodal microscopy that combines complementary nanoscale imaging techniques is critical for extracting comprehensive chemical, structural, and functional information, particularly for heterogeneous samples. X-ray microscopy can achieve high-resolution imaging of bulk materials with chemical, magnetic, electronic, and bond orientation contrast, while electron microscopy provides atomic-scale spatial resolution with quantitative elemental composition. In this work, we combine x-ray ptychography and scanning transmission x-ray spectromicroscopy with three-dimensional energy-dispersive spectroscopy and electron tomography to perform structural and chemical mapping of an Allende meteorite particle with 15-nm spatial resolution. Furthermore, we use textural and quantitative elemental information to infer the mineral composition and discuss potential processes that occurred before or after accretion. Additionally, we anticipate that correlative x-ray and electron microscopy overcome the limitations of individual imaging modalities and open up a route to future multiscale nondestructive microscopies of complex functional materials and biological systems.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

Systematic mapping of two component response regulators to gene targets in a model sulfate reducing bacterium

Background: Two component regulatory systems are the primary form of signal transduction in bacteria. Although genomic binding sites have been determined for several eukaryotic and bacterial transcription factors, comprehensive identification of gene targets of two component response regulators remains challenging due to the lack of knowledge of the signals required for their activation. We focused our study on Desulfovibrio vulgaris Hildenborough, a sulfate reducing bacterium that encodes unusually diverse and largely uncharacterized two component signal transduction systems. Results: We report the first systematic mapping of the genes regulated by all transcriptionally acting response regulators in a single bacterium. Our results enabled functional predictions for several response regulators and include key processes of carbon, nitrogen and energy metabolism, cell motility and biofilm formation, and responses to stresses such as nitrite, low potassium and phosphate starvation. Our study also led to the prediction of new genes and regulatory networks, which found corroboration in a compendium of transcriptome data available for D. vulgaris. For several regulators we predicted and experimentally verified the binding site motifs, most of which were discovered as part of this study. Conclusions: The gene targets identified for the response regulators allowed strong functional predictions to be made for the corresponding two component systems. By tracking the D. vulgaris regulators and their motifs outside the Desulfovibrio spp. we provide testable hypotheses regarding the functions of orthologous regulators in other organisms. The in vitro array based method optimized here is generally applicable for the study of such systems in all organisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

A Programming Framework for Scientific Applications on CPU-GPU Systems

At a high level, my research interests center around designing, programming, and evaluating computer systems that use new approaches to solve interesting problems. The rapid change of technology allows a variety of different architectural approaches to computationally difficult problems, and a constantly shifting set of constraints and trends makes the solutions to these problems both challenging and interesting. One of the most important recent trends in computing has been a move to commodity parallel architectures. This sea change is motivated by the industry’s inability to continue to profitably increase performance on a single processor and instead to move to multiple parallel processors. In the period of review, my most significant work has been leading a research group looking at the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver superior performance on a broad range of problems than their CPU counterparts, but effectively mapping complex applications to a parallel programming model with an emerging programming environment is a significant and important research problem.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Completed coarse-grained model for Pu hydriding that has quantum accuracy (Progress Summary)

Our team has developed a highly computationally efficient coarse-grained model for PuH 2 formation, based on high-level quantum mechanical calculations. Kohn-Sham Density Functional Theory (DFT) is a quantum mechanical approach that can be highly accurate for modeling condensed phases, including reactive materials that are subject to the breaking and forming of chemical bonds. DFT calculations, though, are extremely computationally intensive, and are generally limited to exceedingly small system sizes and time scales. This is particularly true for f-electron solids such as plutonium, where the high computational effort due to electron correlations and minimizing to specific electronic spin-states (e.g., anti-ferromagnetic vs. ferromagnetic) limits standard calculations to systems of tens of atoms and precludes the determination of time-dependent properties (e.g., chemical kinetic data). In this regard, we have created a simplified Pu + H 2 /PuH 2 equilibrium model based on a small number of interaction energies determined from DFT calculations. We have determined that Pu-H bond energetics in the bulk are largely defined by nearest-neighbor interactions, only. In other words, a hydrogen atom in a d-Pu tetrahedral interstitial site will only experience perturbations to its H-Pu bonded interactions when there is an additional hydrogen ion in a directly adjacent adsorption site. This is due to the strong electronic screening from the Pu atoms, which essentially limits the spatial extent of how hydrogen ions interact with each other within the Pu lattice. As a result, we are able to pre-tabulate all essential Pu-H interaction energies and model PuH 2 formation via a Monte Carlo simulation approach. This allows us to greatly expand the spatial and time scales of our calculations while maintaining most of the accuracy of quantum methods. Our model can thus be extended to system sizes if 100,000 atoms or greater and can be used to model hydride equilibrium processes, all of which are computationally cost prohibitive with any standard quantum code. We have used our approach to compute a two-dimensional free energy heat map and temperature dependence at sub-atmospheric pressures for PuH 2 formation. Our results compare well to the limited available experimental data, in particular for the enthalpy of formation (DH). Our goal is to extend these results to compute nucleation parameters such as the critical nucleus size and nucleation growth rates for continuum models.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Validation of Pin Span Superposition to Reconstruct Gridded Bundle Flows

This milestone shows a demonstration of the surface map model to map the surface temperature for single-phase computational fluid dynamics simulation data from STAR-CCM+ to the subchannel code CTF using a two-step process that captures global and local rod surface temperature behavior based on simulation boundary conditions and position. This model can be used to improve results from CTF by transforming surface temperatures obtained from CTF to model data that resembles data from STAR-CCM+. A summary of the current two-step model process and results of the initial investigations with a single subchannel and a 5 x 5 set of fuel rods are shown and discussed within, with suggestions for further improvements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗