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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Data automation at light sources

Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. We present here the case for automating and outsourcing light source science using cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. We discuss three specific services that accomplish these goals for data distribution, automation, and transformation. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. We draw conclusions about best practices for building next-generation data automation systems for future light sources.

Blaiszik, Ben↗
DOE OSTI2010

Azimuthal asymmetries in unpolarized Drell-Yan processes and the Boer-Mulders distributions of antiquarks

Using a previous extraction of the quark Boer-Mulders distributions from semiinclusive deep inelastic scattering data, we fit the unpolarized Drell-Yan data on the $\cos 2 \phi$ asymmetry, determining the antiquark Boer-Mulders distributions. A good agreement with the data is found in the region of low $q_T$, where the transverse-momentum factorization approach applies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

The Effect of Flocculent, Dispersants, and Binder on Wet-laid Process for Recycled Glass Fiber/PA6 Composite

The papermaking industry has been using the wet-laid process to suspend paper pulp-derived fibers in water and drain the solution through a forming mesh. This process has recently been adopted to produce non-woven, wet-laid fiber-reinforced polymer matrix composite mats. The mats can be postmolded into different complex shapes using compression molding or related processes. The objective of this study was to produce composite panels from wet-laid mats and observe the effect of chemicals used during the process on the mechanical and thermal characteristics of the resulting composite. Two sets of mats were processed using recycled glass fiber with Polyamide 6 (PA6). Flocculent, dispersant and binder (poly(vinyl alcohol) (PVOH)) were added to one of the mats, and the second mat was processed without these chemicals. The addition of these chemicals enhanced the fiber distribution and reduced processing defects in the mats. This was reflected in the mechanical properties of the final product. It was noticed that the flocculent, dispersant and binder volatilized during the compression molding step. Furthermore, the additives were found not to affect the thermal properties of the consolidated part.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structural and Proteomic Studies of the Aureococcus anophagefferens Virus Demonstrate a Global Distribution of Virus-Encoded Carbohydrate Processing

Viruses modulate the function(s) of environmentally relevant microbial populations, yet considerations of the metabolic capabilities of individual virus particles themselves are rare. We used shotgun proteomics to quantitatively identify 43 virus-encoded proteins packaged within purified Aureococcus anophagefferens Virus (AaV) particles, normalizing data to the per-virion level using a 9.5-Å-resolution molecular reconstruction of the 1900-Å (AaV) particle that we generated with cryogenic electron microscopy. This packaged proteome was used to determine similarities and differences between members of different giant virus families. We noted that proteins involved in sugar degradation and binding (e.g., carbohydrate lyases) were unique to AaV among characterized giant viruses. To determine the extent to which this virally encoded metabolic capability was ecologically relevant, we examined the TARA Oceans dataset and identified genes and transcripts of viral origin. Our analyses demonstrated that putative giant virus carbohydrate lyases represented up to 17% of the marine pool for this function. In total, our observations suggest that the AaV particle has potential prepackaged metabolic capabilities and that these may be found in other giant viruses that are widespread and abundant in global oceans.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Using a spatially-distributed hydrologic biogeochemistry model with a nitrogen transport module to study the spatial variation of carbon processes in a Critical Zone Observatory

Terrestrial carbon processes are affected by soil moisture, soil temperature, nitrogen availability and solar radiation, among other factors. Most of the current ecosystem biogeochemistry models represent one point in space, and have limited characterization of hydrologic processes. Therefore these models can neither resolve the topographically driven spatial variability of water, energy, and nutrient, nor their effects on carbon processes. A spatially-distributed land surface hydrologic biogeochemistry model, Flux-PIHM-BGC, is developed by coupling the Biome-BGC model with a physically-based land surface hydrologic model, Flux-PIHM. In the coupled system, each Flux-PIHM model grid couples a 1-D Biome-BGC model. In addition, a topographic solar radiation module and an advection-driven nitrogen transport module are added to represent the impact of topography on nutrient transport and solar energy distribution. Because Flux-PIHM is able to simulate lateral groundwater flow and represent the land surface heterogeneities caused by topography, Flux-PIHM-BGC is capable of simulating the complex interaction among water, energy, nutrient, and carbon in time and space. The Flux-PIHM-BGC model is tested at the Susquehanna/Shale Hills Critical Zone Observatory. Model results show that distributions of carbon and nitrogen stocks and fluxes are strongly affected by topography and landscape position, and tree growth is nitrogen limited. The predicted aboveground and soil carbon distributions generally agree with the macro patterns observed. Although the model underestimates the spatial variation, the predicted watershed average values are close to the observations. Lastly, the coupled Flux-PIHM-BGC model provides an important tool to study spatial variations in terrestrial carbon and nitrogen processes and their interactions with environmental factors, and to predict the spatial structure of the responses of ecosystems to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Theoretical Analysis of the Entrainment–Mixing Process at Cloud Boundaries. Part I: Droplet Size Distributions and Humidity within the Interface Zone

Abstract The problem of a complex entrainment–mixing process is analyzed by solving a diffusion–evaporation equation for an open region in the vicinity of the cloud–dry air interface. Upon normalization the problem is reduced to a one-parametric one, the governing parameter being the potential evaporation parameter R proportional to the ratio of saturation deficit in the dry air to the available liquid water content in the cloud air. As distinct from previous multiple studies analyzing mixing within closed adiabatic volumes, we consider a principally nonstationary problem that never leads to a homogeneous equilibrium state. It is shown that at R < −1 the cloud edge shifts toward the cloud; that is, the cloud dissipates due to mixing with dry air, and the cloud volume decreases. If R > −1, the cloud edge shifts outside; that is, the mixing leads to an increase in the cloud volume. The time evolution of droplet size distribution and its moments, as well as the relative humidity within the expanding cloud–dry air interface, are calculated and analyzed. It is shown that the values of the mean volume radii rapidly decrease within the interface zone in the direction away from the cloud, indicating significant changes in the cloud edge microstructure. Scattering diagrams plotted for the cloud edge agree well with high-frequency in situ measurements, corroborating the reliability of the proposed approach. It is shown that the humidity front moves toward dry air faster than the front of liquid water content. As a result, the mixing leads to formation of a humid air shell around the cloud. The widths of the interface zone and humid shell are evaluated.

Pinsky, Mark↗
DOE OSTI2015

The climatological distribution of extreme Arctic winds and implications for ocean and sea ice processes

Abstract Some of the strongest near‐surface winds on Earth form in the Arctic and sub‐Arctic due to intense midlatitude cyclones and mesoscale processes, and these strong surface winds have important impacts on ocean and sea ice processes. We examine the climatological distribution of over‐ocean, near‐surface wind speeds within a Pan‐Arctic domain for 18 years (1990–2007) in four gridded data sets: the European Centre for Medium‐Range Weather Forecasts Interim reanalysis (ERA‐I), the Climate Forecast System Reanalysis, version 2 of the Common Ocean‐Ice Reference Experiment data set, and a regional climate simulation generated using the Weather Research and Forecasting (WRF) model run at 50 km (WRF50) horizontal resolution with ERA Interim as lateral boundary conditions. We estimate probability density functions, the annual cycle, and map the 50th and 99th percentile winds. We then perform the same statistical analysis of winds for 2 years when 10 km WRF data are available (June 2005 to May 2007); despite the much shorter time period, the Pan‐Arctic statistics are very similar to those from the 18 year analysis. We repeat the wind speed statistical analysis within a subdomain surrounding Greenland and find that WRF10 has consistently larger maximum wind speeds, but this difference only appears at wind speed percentiles higher than 99%. Differences in the 99th percentile wind speeds are spatially heterogeneous. An investigation of surface fluxes within WRF50 and WRF10 reveals unrealistically large sensible heat fluxes along the sea ice edge, and the geographic distribution and magnitude of these fluxes is shown to be sensitive to sea ice representation in WRF.

Hughes, Mimi↗
DOE OSTI2025

Scalable low-loss cryogenic packaging of quantum memories in CMOS-foundry processed photonic chips

Optically linked solid-state quantum memories such as color centers in diamond are a promising platform for distributed quantum information processing and networking. Photonic integrated circuits (PICs) have emerged as a crucial enabling technology for these systems, integrating quantum memories with efficient electrical and optical interfaces in a compact and scalable platform. Packaging these hybrid chips into deployable modules while maintaining low optical loss and resiliency to temperature cycling is a central challenge to their practical use. We demonstrate a packaging method for PICs using surface grating couplers and angle-polished fiber arrays that is robust to temperature cycling, offers scalable channel count, applies to a wide variety of PIC platforms and wavelengths, and offers pathways to automated high-throughput packaging. Using this method, we show optically and electrically packaged quantum memory modules integrating all required qubit controls on chip, operating at millikelvin temperatures with <3 dB losses achievable from fiber to quantum memory for the TE 0 mode at a wavelength of 737 nm.

Bernson, Robert [Tyndall National Institute, Cork ↗
DOE OSTI2013

Self-consistent mean-field theory of size distribution narrowing during ramped temperature ion beam synthesis

A simple mathematical argument explains a recently identified route for the ion beam synthesis of nanoclusters with a narrowed size distribution. The key idea is that growth conditions for which the average nanocluster size is increasing rapidly can lead to narrowed size distributions. Modeling candidate processes using a self-consistent, mean-field theory shows that normalized nanocluster size distributions with full-width at half-maximum of 17% of the average can be attained.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Efficient Delaunay Tessellation through K-D Tree Decomposition

Delaunay tessellations are fundamental data structures in computational geometry. They are important in data analysis, where they can represent the geometry of a point set or approximate its density. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We evaluate the new algorithm using two late-stage cosmology datasets. The new running times are up to 50 times faster using k-d tree compared with regular grid decomposition. Moreover, in the unbalanced data sets, decomposing the domain into a k-d tree is up to five times faster than decomposing it into a regular grid.

Morozov, Dmitriy↗
DOE OSTI2024

Analytic Neural Network Gaussian Process Enabled Chance-Constrained Voltage Regulation for Active Distribution Systems with PVs, Batteries and EVs

This paper proposes an analytic neural network Gaussian process (NNGP)-based chance-constrained real-time voltage regulation method for active distribution systems with photovoltaics (PVs), batteries, and electric vehicles (EVs). NNGP can utilize historical measurement data to achieve real-time probabilistic node voltage estimation through Bayesian inference. Then, NNGP is fully analytically embedded into the optimal power flow model to perform voltage regulation and adapt to various topological changes. The uncertainties of voltage estimations are easily considered via the chance constraint, and it has been shown that the adoption of this chance constraint can significantly improve the reliability of voltage regulation under various scenarios. The comparison results with other methods, carried out on a real 759-node distribution system located in western Colorado, U.S., show that the proposed method can achieve accurate voltage estimation across different topologies and reliably perform voltage regulation considering PVs, batteries, and EVs.

active distribution systems↗