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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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Ab initio molecular dynamics simulations of liquid water using high quality meta-GGA functionals

We have used ab initio molecular dynamics (AIMD) to characterize water properties using two meta-generalized gradient approximation (meta-GGA) functionals, M06-L-D3 and B97M-rV, and compared their performance against a standard GGA corrected for dispersion, revPBE-D3, at ambient conditions (298 K, and 1 g cm –3 or 1 atm). Simulations of the equilibrium density, radial distribution functions, self-diffusivity, the infrared spectrum, liquid dipole moments, and characterizations of the hydrogen bond network show that all three functionals have overcome the problem of the early AIMD simulations that erroneously found ambient water to be highly structured, but they differ substantially among themselves in agreement with experiment on this range of water properties. We show directly using water cluster data up through the pentamer that revPBE-D3 benefits from a cancellation of its intrinsic functional error by running classical trajectories, whereas the meta-GGA functionals are demonstrably more accurate and would require the simulation of nuclear quantum effects to realize better agreement with all cluster and condensed phase properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Simulation of convective transport during frequency chirping of a TAE using the MEGA code

Here, we present a procedure to examine energetic particle phase-space during long range frequency chirping phenomena in tokamak plasmas. To apply the proposed method, we have performed self-consistent simulations using the MEGA code and analyzed the simulation data. We demonstrate a traveling wave in phase-space and that there exist specific slices of phase-space on which the resonant particles lie throughout the wave evolution. For non-linear evolution of an n = 6 toroidicity-induced Alfvén eigenmode (TAE), our results reveal the formation of coherent phase-space structures (holes/clumps) after coarse-graining of the distribution function. These structures cause a convective transport in phase-space which implies a radial drift of the resonant particles. We also demonstrate that the rate of frequency chirping increases with the TAE damping rate. Our observations of the TAE behavior and the corresponding phase-space dynamics are consistent with the Berk–Breizman theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

The hybrid mesons quest: the MesonEx experiment at Jefferson Laboratory

The meson spectroscopy plays nowadays a central role in the investigation of hadron structure thanks to the possible existence of exotic hybrid mesons, quark-antiquark-gluon bound states. Their explicit gluonic degrees of freedom which should clearly emerge from a Partial Wave Analysis (PWA) of the corresponding Dalitz plot of the exotic particle decay, may result in final JPC configurations not allowed in the constituent quark model. Besides this clear signature, hybrid mesons are also expected to have a large particle multiplicity decays, requiring for their search an experimental apparatus with high performances in terms of rate capability, resolution and almost a full acceptance to apply PWA methods. New-generation experiments are planned at Thomas Jefferson National Laboratory (VA, USA) for which an unprecedented statistics of large multiplicity decay events with fully reconstructed kinematics will be available. In particular for the MesonEx (CLAS12) experiment in Hall B, a wide scientific program that will start in 2016 has been deployed to study the meson spectrum at energies up to 11 GeV. A key role in such program is played by the Forward Tagger apparatus of the experiment, which will allow to extend the study of meson electro-production to very low Q2 values, in a quasi-real photo production kinematical region, where the production of hybrid mesons is expected to be favorite. Currently a new analysis framework for the search of the hybrid mesons is being set up by the HASPECT network, an international structure which gather people involved into theoretical and experimental hadronic physics all over the world. The goals of the network is to develop new analysis models and statistical techniques to unfold the signal and background distributions in high-statistics datasets. In this work are briefly presented the first preliminary results from the application of a statistical technique, namely the sPlot, to the data already acquired by the CLAS experiment for the decay f 1 (1285) --> (eta)pi+pi-.

Rizzo, Alessandro↗
DOE OSTI2019

Galaxy–Galaxy lensing in HSC: Validation tests and the impact of heterogeneous spectroscopic training sets

Although photometric redshifts (photo-z’s) are crucial ingredients for current and upcoming large-scale surveys, the high-quality spectroscopic redshifts currently available to train, validate, and test them are substantially non-representative in both magnitude and colour. We investigate the nature and structure of this bias by tracking how objects from a heterogeneous training sample contribute to photo-z predictions as a function of magnitude and colour, and illustrate that the underlying redshift distribution at fixed colour can evolve strongly as a function of magnitude. We then test the robustness of the galaxy–galaxy lensing signal in 120 deg 2 of HSC–SSP DR1 data to spectroscopic completeness and photo-z biases, and find that their impacts are sub-dominant to current statistical uncertainties. Our methodology provides a framework to investigate how spectroscopic incompleteness can impact photo-z-based weak lensing predictions in future surveys such as LSST and WFIRST .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Direct 400 Vdc to 1 Vdc power conversion with input series output parallel connection for data center power supplies

Data centers consume an ever-increasing amount of electricity because of the rapid growth of cloud computing and digital information storage. A high voltage point of load (HV POL) converter is proposed to convert the 400-VDC distribution voltage to 1-VDC within a single stage to increase the power conversion efficiency. A six-phase input series output parallel (ISOP) connected structure is implemented for the HV POL. The symmetrical controlled half bridge current doubler is selected as the converter topology in the ISOP structure. The full load efficiency is improved by 4% points compared with state of the art products. A voltage compensator has been designed in order to meet the strict dynamic voltage regulation requirement. A laboratory prototype has been built, and experimental results have been provided to verify the proposed HV POL with a single power conversion stage can meet the dynamic voltage regulation requirement for an on-board power supply with higher efficiency compared to the conventional architecture.

Cui, Yutian↗
DOE OSTI2017

Differences in soil biological activity by terrain types at the sub-field scale in central Iowa US

Soil microbial communities are structured by biogeochemical processes that occur at many different spatial scales, which makes soil sampling difficult. Because soil microbial communities are important in nutrient cycling and soil fertility, it is important to understand how microbial communities function within the heterogeneous soil landscape. In this study, a self-organizing map was used to determine whether landscape data can be used to characterize the distribution of microbial biomass and activity in order to provide an improved understanding of soil microbial community function. Points within a row crop field in south-central Iowa were clustered via a self-organizing map using six landscape properties into three separate landscape clusters. Twelve sampling locations per cluster were chosen for a total of 36 locations. After the soil samples were collected, the samples were then analysed for various metabolic indicators, such as nitrogen and carbon mineralization, extractable organic carbon, microbial biomass, etc. It was found that sampling locations located in the potholes and toe slope positions had significantly greater microbial biomass nitrogen and carbon, total carbon, total nitrogen and extractable organic carbon than the other two landscape position clusters, while locations located on the upslope did not differ significantly from the other landscape clusters. However, factors such as nitrate, ammonia, and nitrogen and carbon mineralization did not differ significantly across the landscape. Altogether, this research demonstrates the effectiveness of a terrain-based clustering method for guiding soil sampling of microbial communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Joint emulation of Earth System Model temperature-precipitation realizations with internal variability and space-time and cross-variable correlation: fldgen v2.0 software description

Earth System Models (ESMs) are excellent tools for quantifying many aspects of future climate dynamics but are too computationally expensive to produce large collections of scenarios for downstream users of ESM data. In particular, many researchers focused on the impacts of climate change require large collections of ESM runs to rigorously study the impacts to both human and natural systems of low-frequency high-importance events, such as multi-year droughts. Climate model emulators provide an effective mechanism for filling this gap, reproducing many aspects of ESMs rapidly but with lower precision. The fldgen v1.0 R package quickly generates thousands of realizations of gridded temperature fields by randomizing the residuals of pattern scaling temperature output from any single ESM, retaining the spatial and temporal variance and covariance structures of the input data at a low computational cost. The fldgen v2.0 R package described here extends this capability to produce joint realizations of multiple variables, with a focus on temperature and precipitation in an open source software package available for community use (https://github.com/jgcri/fldgen). This substantially improves the fldgen package by removing the requirement that the ESM variables be normally distributed, and will enable researchers to quickly generate covarying temperature and precipitation data that are synthetic but faithful to the characteristics of the original ESM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Studies of Quark Momentum in the Proton by Use of the SIDIS Process

The three dimensional structure of protons and neutrons has been a subject of great interest since the early 1990s when advancements in theory produced the Generalized Parton Distribution (GPD) framework for understanding spatial distributions, and Transverse Momentum Dependent Parton Distribution Functions (TMD PDFs) for understanding quark momentum. In this thesis, I present two analyses of the SIDIS process which can be related to TMDs. I have used CLAS data from the E1-F run period, which recorded over 1 billion events of 5.498 GeV electrons colliding with a liquid hydrogen ( LH 2 ) target. First, I discuss analysis of the SIDIS cross section (fully differential in 5 variables) for both charged pions over a wide kinematic range. This analysis observes a non-zero cos(ϕ h ) and cos(2ϕ h ) modulations, and is useful for phenomenological extractions of the Boer-Mulders and Cahn effect terms. I then present results for the beam spin asymmetry (BSA), which is only sensitive to terms containing twist-three distribution functions. The analysis, performed for K + mesons, observes a non-zero BSA and therefore implies that twist-three effects are not negligably small in the kinematics used for the study. Unlike the large magnitude of the Sivers asymmetries observed at HERMES for K + mesons, the BSA reported here is of the same magnitude as the lighter π + meson.

Riser, David↗
DOE OSTI2017

Fluorescent H{sub 2} Emission Lines from the Reflection Nebula NGC 7023 Observed with IGRINS

We have analyzed the temperature, velocity, and density of H{sub 2} gas in NGC 7023 with a high-resolution near-infrared spectrum of the northwestern filament of the reflection nebula. By observing NGC 7023 in the H and K bands at R ≃ 45,000 with the Immersion GRating INfrared Spectrograph, we detected 68 H{sub 2} emission lines within the 1″ × 15″ slit. The diagnostic ratio of 2-1 S(1)/1-0 S(1) is 0.41−0.56. In addition, the estimated ortho-to-para ratio (OPR) is 1.63−1.82, indicating that the H{sub 2} emission transitions in the observed region arise mostly from gas excited by UV fluorescence. Gradients in the temperature, velocity, and OPR within the observed area imply motion of the photodissociation region (PDR) relative to the molecular cloud. In addition, we derive the column density of H{sub 2} from the observed emission lines and compare these results with PDR models in the literature covering a range of densities and incident UV field intensities. The notable difference between PDR model predictions and the observed data, in high rotational J levels of ν = 1, is that the predicted formation temperature for newly formed H{sub 2} should be lower than that of the model predictions. To investigate the density distribution, we combine pixels in 1″ × 1″ areas and derive the density distribution at the 0.002 pc scale. The derived gradient of density suggests that NGC 7023 has a clumpy structure, including a high clump density of ∼10{sup 5} cm{sup −3} with a size smaller than ∼5 × 10{sup −3} pc embedded in lower-density regions of 10{sup 3}–10{sup 4} cm{sup −3}.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

High-resolution Statistics of Solar Wind Turbulence at Kinetic Scales Using the Magnetospheric Multiscale Mission

Using data from the Magnetospheric Multiscale (MMS) and Cluster missions obtained in the solar wind, we examine second-order and fourth-order structure functions at varying spatial lags normalized to ion inertial scales. The analysis includes direct two-spacecraft results and single-spacecraft results employing the familiar Taylor frozen-in flow approximation. Several familiar statistical results, including the spectral distribution of energy, and the sale-dependent kurtosis, are extended down to unprecedented spatial scales of ∼6 km, approaching electron scales. The Taylor approximation is also confirmed at those small scales, although small deviations are present in the kinetic range. The kurtosis is seen to attain very high values at sub-proton scales, supporting the previously reported suggestion that monofractal behavior may be due to high-frequency plasma waves at kinetic scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2017

Determination of the proton spin structure functions for 0.05 < Q 2 < 5 GeV 2 using CLAS

In this work, we present the results of our final analysis of the full data set of g$p\atop{1}$ (Q 2 ), the spin structure function of the proton, collected using CLAS at Jefferson Laboratory in 2000–2001. Polarized electrons with energies of 1.6, 2.5, 4.2, and 5.7 GeV were scattered from proton targets ( 15 NH 3 dynamically polarized along the beam direction) and detected with CLAS. From the measured double spin asymmetries, we extracted virtual photon asymmetries A$p\atop{1}$ and A$p\atop{2}$ and spin structure functions g$p\atop{1}$ and g$p\atop{2}$ over a wide kinematic range (0.05 GeV 2 < Q 2 < 5 GeV 2 and 1.08 GeV < W < 3 GeV) and calculated moments of g$p\atop{1}$. We compare our final results with various theoretical models and expectations, as well as with parametrizations of the world data. Lastly, our data, with their precision and dense kinematic coverage, are able to constrain fits of polarized parton distributions, test pQCD predictions for quark polarizations at large x, offer a better understanding of quark-hadron duality, and provide more precise values of higher twist matrix elements in the framework of the operator product expansion.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Using a machine learning approach to determine the space group of a structure from the atomic pair distribution function

Here a method is presented for predicting the space group of a structure given a calculated or measured atomic pair distribution function (PDF) from that structure. The method utilizes machine learning models trained on more than 100 000 PDFs calculated from structures in the 45 most heavily represented space groups. In particular, a convolutional neural network (CNN) model is presented which yields a promising result in that it correctly identifies the space group among the top-6 estimates 91.9% of the time. The CNN model also successfully identifies space groups for 12 out of 15 experimental PDFs. Interesting aspects of the failed estimates are discussed, which indicate that the CNN is failing in similar ways as conventional indexing algorithms applied to conventional powder diffraction data. This preliminary success of the CNN model shows the possibility of model-independent assessment of PDF data on a wide class of materials.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

A METHOD TO SEARCH FOR BULK MOTIONS IN THE ICM WITH CHANDRA CCD SPECTRA: APPLICATION TO THE BULLET CLUSTER

We propose a strategy to search for bulk motions in the intracluster medium (ICM) of merging clusters based on Chandra CCD data. Our goal is to derive robust measurements of the average redshift of projected ICM regions obtained from the centroid of the K{sub α} line emission. We thoroughly explore the effect of the unknown temperature structure along the line of sight to accurately evaluate the systematic uncertainties on the ICM redshift. We apply our method to the “Bullet cluster” (1E 0657-56). We directly identify 23 independent regions on the basis of the surface brightness contours, and measure the redshift of the ICM averaged along the line of sight in each. We find that the redshift distribution across these regions is marginally inconsistent with the null hypothesis of a constant redshift or no bulk motion in the ICM, at a confidence level of about 2σ. We tentatively identify the regions most likely affected by bulk motions and find a maximum velocity gradient of about (46 ± 13) km s{sup −1} kpc{sup −1} along the line of sight on a scale of ∼260 kpc along the path of the “bullet.” We interpret this as the possible signature of a significant mass of ICM pushed away along a direction perpendicular to the merging. This preliminary result is promising for a systematic search for bulk motions in bright, moderate-redshift clusters based on spatially resolved spectral analysis of Chandra CCD data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Viewing is understanding: Graphite microstructure effects on infiltrated molten salt distribution revealed by 3D neutron tomography

Molten salt infiltration in the pore network of nuclear graphite may cause unwanted changes to graphite's local structure and mechanical and thermal properties. A detailed and comprehensive understanding of molten salt intrusion (distribution across sample cross section and penetration depth) is needed to assess its effects. Here, in this work, we report on an improved methodology for the use of neutron imaging (computed tomography) to evaluate salt penetration and distribution of a wide range of graphite grades with diverse microstructures. Neutron tomography data were acquired on the same graphite sample before and after salt intrusion; the 3D reconstructed volumes were digitally co-registered and subtracted. The difference in neutron attenuation coefficient represents direct visualization of FLiNaK (LiF–NaF–KF) salt distribution in the salt-impregnated graphite samples. This improved methodology was applied to investigate the effect of exposure times (12 h and 336 h) and of graphite microstructure when exposed to FLiNaK at 750 °C and 3 bar (gauge) pressure, starting from flowing argon at near atmospheric pressure. The results show that medium-grained and fine-grained graphites evolve to equilibrium at significantly different rates: fast salt uptake in medium-grained graphites produces salt deposits throughout the volume of graphite specimens, whereas salt infiltration in fine-grained graphites is much slower and limited to exposed surfaces.

FLiNaK infiltration↗
DOE OSTI2019

X-ray Scattering and Coarse-Grained Simulations for Clustering and Interactions of Monoclonal Antibodies at High Concentrations

Attractive protein-protein interactions (PPI) in concentrated monoclonal antibody (mAb) solutions may lead to reversible oligomers (clusters) that impact colloidal stability and viscosity. Herein, the PPI are tuned for two mAbs via the addition of arginine (Arg), NaCl, or ZnSO 4 as characterized by the structure factor ( S eff (q)) with small angle x-ray scattering (SAXS). The SAXS data are fit with molecular dynamics simulations by placing a physically relevant short-ranged attractive interaction on selected beads in coarse-grained 12 bead models of the mAb shape. The optimized 12-bead models are then used to differentiate key microstructural properties, including center of mass radial distribution functions ( g COM (r)), coordination numbers and cluster size distributions (CSD). The addition of co-solutes results in more attractive Seff(q) relative to the no co-solute control for all systems tested, with the most attractive systems showing an upturn at low q . Only the All1 model with an attractive site in each Fab and Fc region (possessing Fab-Fab, Fab-Fc and Fc-Fc interactions) can reproduce this upturn, and the corresponding CSDs show the presence of larger clusters compared to the control. In general, for models with similar net attractions, i.e., second osmotic virial coefficients, the size of the clusters increases as the attraction is concentrated on a smaller number of evenly distributed beads. Here, the cluster size distributions from simulations are used to improve the understanding and prediction of experimental viscosities. The ability to discriminate between models with bead interactions at particular Fab and Fc bead sites from SAXS simulations, and to provide real space properties (CSD and g COM (r)), will be of interest in engineering protein sequence and formulating protein solutions for weak PPI to minimize aggregation and viscosities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Event‐Based Training in Label‐Limited Regimes

Abstract The distribution of attributes assigned using data on independent sensors for a specific source, for example, magnitude, can be richly descriptive for final event characterization and associated uncertainty. Attribute distributions can also provide powerful context for event characterization in the absence of comprehensive annotation. This work develops a way to leverage distributional information across a set of sensors in the absence of comprehensive annotation as a domain‐informed regularization term applied during gradient‐based learning. The regularization term is the basis of event‐based training which I show can be a powerful semi‐supervised learning (SSL) approach. I first use a simple feed forward neural network and a toy data set to outline how data set structure interacts with the assumptions inherent to many semi‐supervised learning approaches. I then demonstrate the effectiveness of event‐based training using a deep convolutional neural network for seismic event classification in Utah, which increases SSL accuracy from 92% to 97% on event classification with a limited number of training labels.

Linville, Lisa M.↗