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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Crystal and magnetic structures of Cr{sub 1∕3}NbSe{sub 2} from neutron diffraction

Neutron diffraction measurements of the Cr intercalated niobium diselenide Cr{sub 1∕3}NbSe{sub 2} together with magnetization measurements have revealed that this compound exhibits ferromagnetic ordering below T{sub C} = 96 K unlike a chiral helimagnetic order observed in the sulfide compound Cr{sub 1∕3}NbS{sub 2}. As derived from neutron diffraction data, the Cr magnetic moments μ{sub Cr} = 2.83 ± 0.03 μ{sub B} in Cr{sub 1∕3}NbSe{sub 2} are aligned within basal plane. The discrepancy in the magnetic states of Cr{sub 1∕3}NbS{sub 2} and Cr{sub 1∕3}NbSe{sub 2} is ascribed to the difference in the preferential site occupation of Cr ions in crystal lattices. In Cr{sub 1∕3}NbSe{sub 2}, the Cr ions are predominantly distributed over 2b Wyckoff site, which determines a centrosymmetric character of the crystal structure unlike Cr{sub 1∕3}NbS{sub 2}, where the Cr ions are mainly located in 2c position and the crystal structure is non-centrosymmetric.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Representational analysis of extended disorder in atomistic ensembles derived from total scattering data

With the increased availability of high-intensity time-of-flight neutron and synchrotron X-ray scattering sources that can access wide ranges of momentum transfer, the pair distribution function method has become a standard analysis technique for studying disorder of local coordination spheres and at intermediate atomic separations. In some cases, rational modeling of the total scattering data (Bragg and diffuse) becomes intractable with least-squares approaches, necessitating reverse Monte Carlo simulations using large atomistic ensembles. However, the extraction of meaningful information from the resulting atomistic ensembles is challenging, especially at intermediate length scales. Representational analysis is used here to describe the displacements of atoms in reverse Monte Carlo ensembles from an ideal crystallographic structure in an approach analogous to tight-binding methods. Rewriting the displacements in terms of a local basis that is descriptive of the ideal crystallographic symmetry provides a robust approach to characterizing medium-range order (and disorder) and symmetry breaking in complex and disordered crystalline materials. Lastly, this method enables the extraction of statistically relevant displacement modes (orientation, amplitude and distribution) of the crystalline disorder and provides directly meaningful information in a locally symmetry-adapted basis set that is most descriptive of the crystal chemistry and physics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment

To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes the most use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent sub-problems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.

42 ENGINEERING↗
DOE OSTI2018

Structure of a Deformed Inhomogeneous Material on the Data of Acoustic Emission and X-Ray Computer Microtomography

Accumulation of defects at various stages of the deformation of a structurally inhomogeneous material (granite) has been studied using two nondestructive methods: acoustic emission (AE) and X-ray computer microtomorgaphy (CT). The quasi-static testing of cylindrical samples of a Westerly granite was carried out under uniaxial compression. The control of the defect formation was realized using the real-time monitoring of acoustic emission. For each sample under study, several steps of the loading and tomographic imaging have been performed. We have found that an exponential or power-law function of the energy distribution of the AE signals makes it possible to select a sample region, in which the system of defects has transited into a self-organized criticality state and large cracks have been formed. This result coincides with the data of the X-ray tomography Computer Microtomography.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Postglacial migration shaped the genomic diversity and global distribution of the wild ancestor of lager-brewing hybrids

The wild, cold-adapted parent of hybrid lager-brewing yeasts, Saccharomyces eubayanus, has a complex and understudied natural history. The exploration of this diversity can be used both to develop new brewing applications and to enlighten our understanding of the dynamics of yeast evolution in the wild. Here, we integrate whole genome sequence and phenotypic data of 200 S. eubayanus strains, the largest collection known to date. S. eubayanus has a multilayered population structure, consisting of two major populations that are further structured into six subpopulations. Four of these subpopulations are found exclusively in the Patagonian region of South America; one is found predominantly in Patagonia and sparsely in Oceania and North America; and one is specific to the Holarctic ecozone. Plant host associations differed between subpopulations and between S. eubayanus and its sister species, Saccharomyces uvarum. S. eubayanus is most abundant and genetically diverse in northern Patagonia, where some locations harbor more genetic diversity than is found outside of South America, suggesting that northern Patagonia east of the Andes was a glacial refugium for this species. All but one subpopulation shows isolation-by-distance, and gene flow between subpopulations is low. However, there are strong signals of ancient and recent outcrossing, including two admixed lineages, one that is sympatric with and one that is mostly isolated from its parental populations. Using our extensive biogeographical data, we build a robust model that predicts all known and a handful of additional regions of the globe that are climatically suitable for S. eubayanus, including Europe where host accessibility and competitive exclusion by other Saccharomyces species may explain its continued elusiveness. We conclude that this industrially relevant species has rich natural diversity with many factors contributing to its complex distribution and natural history.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Boosting H I -Galaxy Cross-Clustering Signal through Higher-Order Cross-Correlations

After reionization, neutral hydrogen (${\rm H\, \small {I}}$) traces the large-scale structure (LSS) of the Universe, enabling ${\rm H\, \small {I}}$ intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study ${\rm H\, \small {I}}$ clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of k-nearest neighbor (kNN) optical galaxies and the ${\rm H\, \small {I}}$ brightness temperature field smoothed at relevant scales (the kNN-field framework), providing sensitivity to all higher-order cross-correlations, unlike two-point statistics. To simulate ${\rm H\, \small {I}}$ data from actual surveys, we add random thermal noise and apply a simple foreground cleaning model, filtering out Fourier modes of the brightness temperature field with k ∥ < k min,∥ . Under current levels of thermal noise and foreground cleaning, typical of a Canadian Hydrogen Intensity Mapping Experiment (CHIME)-like survey, the ${\rm H\, \small {I}}$-galaxy cross-correlation signal in our simulations, using the kNN-field framework, is detectable at >30σ across r = [3, 12] h –1 Mpc. In contrast, the detectability of the standard two-point correlation function (2PCF) over the same scales depends strongly on the foreground filter: a sharp k ∥ filter can spuriously boost detection to 8σ due to position-space ringing, whereas a less sharp filter yields no detection. Nonetheless, we conclude that kNN-field cross-correlations are robustly detectable across a broad range of foreground filtering and thermal noise conditions, suggesting their potential for enhanced constraining power over 2PCFs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Beyond Badger’s Rule: The Origins and Generality of the Structure–Spectra Relationship of Aqueous Hydrogen Bonds

The structure of hydrogen bonded networks is intimately intertwined with their dynamics. Despite the incredibly wide range of hydrogen bond strengths encountered in water clusters, ion-water clusters and liquid water, we demonstrate that the previously reported correlation between the change in the equilibrium bond length of the hydrogen bonded OH covalent bond and the corresponding shift in its harmonic frequency in water clusters is much more broadly applicable. Surprisingly, this correlation describes the ratios for both the equilibrium OH bond length/harmonic frequency, and the vibrationally averaged bond length/anharmonic frequency in water, hydronium water and halide water clusters. Consideration of harmonic and anaharmonic data leads to a correlation of -19 ± 1 cm -1 /0.001 Å. The basic nature of this correlation is further confirmed through the analysis of ab initio Molecular Dynamics (AIMD) trajectories for liquid water. We show that this simple correlation for both harmonic and anharmonic systems can be modeled by the response of an OH bond to an external field. Treating the OH bond as a Morse oscillator, we develop analytic expressions, which relate the ratio of the shift in the vibrational frequency of the hydrogen-bonded OH bond to the shift in OH bond length, to parameters in the Morse potential and the ratio of the first and second derivatives of the field-dependent projection of the dipole moment of water onto the hydrogen-bonded OH bond. Based on our analysis, we develop a protocol for reconstructing the AIMD spectra of liquid water from the sampled distribution of the OH bond lengths. Our conclusions elucidate the origins of the relationship between the molecular structure of the fleeting hydrogen-bonded network and the ensuing dynamics, which can be probed by vibrational spectroscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves

The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.

42 ENGINEERING↗
DOE OSTI2023

High-Resolution Near-Surface Imaging at the Basin Scale Using Dark Fiber and Distributed Acoustic Sensing: Toward Site Effect Estimation in Urban Environments

Near-surface seismic structure, particularly the shear wave velocity (V s ), can strongly affect local site response, and should be accurately estimated for ground motion prediction during seismic hazard assessment. The Imperial Valley (California), occupying the southern end of the Salton Trough, is a seismically active basin with thick surficial lacustrine sedimentary deposits. In this study, we utilize ambient noise records and local earthquake events for high-resolution near-surface characterization and site effect estimation with an unlit fiber-optic telecommunication infrastructure (dark fiber) in Imperial Valley by using the distributed acoustic sensing (DAS) technique. We apply ambient noise interferometry to retrieve coherent surface waves from DAS records, and evaluate performances of three different surface wave methods on DAS ambient noise dispersion imaging. We develop a quality control workflow to improve the dispersion measurement of noisy portions of the DAS data set by using a data selection strategy. Using the joint inversion of both the fundamental mode and higher overtones of Rayleigh waves, a high resolution two-dimensional (2D) V s structure down to 70 m depth is obtained. We successfully achieve an improved V s 30 (the time-averaged shear-wave velocity in the top 30 m) model with higher spatial-resolution and reliability compared to the existing community model for the area. We also explore the potential for utilizing DAS earthquake events for site amplification estimation. The preliminary results reveal a clear anti-correlation between the approximated site response and the V s 30 profile. In conclusion, our results indicate the potential utility of DAS deployed on dark fiber for near-surface characterization in appropriate contexts.

58 GEOSCIENCES↗
DOE OSTI2017

Infrared and multi-wavelength Raman spectroscopy of regio-regular P3HT and its deutero derivatives

The recently synthesized regio–regular poly(3–hexylthiophene–2,5–diyl) selectively deuterated on the main chain backbone and/or on the hexyl side chain have given the opportunity to record their infrared (IR) and Raman spectra, and to carry out a spectroscopic study supported by density functional theory calculations. The Effective Conjugation Coordinate associated with the collective C=C stretching mode, with Raman scattering and IR absorption near 1,450 cm –1 , is used as a probe of the electronic structure and the molecular conformation of the chain backbone. With the help of the data collected from the deuterated species, the vibrational assignment for the structurally relevant 1,600–1,300 cm –1 region has been clarified. The excitation–dependent wavenumbers and intensities of the C=C stretching Raman modes are discussed. Raman spectra excited in–resonance or off–resonance show the existence of a multimodal distribution of effective conjugation lengths that are ascribed to a phase hairy–A with a practically flat backbone chain, a phase hairy–B where the conformation of the backbone is slightly distorted, and an amorphous phase. As a result, IR spectra provide additional information on the Effective Conjugation Coordinate.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Strain induced grain boundary migration effects on grain growth of an austenitic stainless steel during static and metadynamic recrystallization

Static and metadynamic recrystallization of an AISI 304L austenitic stainless steel was investigated at 1100 °C and 10{sup −} {sup 2} s{sup −} {sup 1} strain rate. The kinetics of recrystallization was determined through double hit compression tests. Two strain levels were selected for the first compression hit: ε{sub f} = 0.15 for static recrystallization (SRX) and 0.25 for metadynamic recrystallization (MDRX). Both the as-deformed and the recrystallized microstructures were investigated through optical microscopy and electron back-scattered diffraction (EBSD) technique. During deformation, strain induced grain boundary migration appeared to be significant, producing a square-like grain boundary structure aligned along the directions of the maximum shear stresses in compression. EBSD analysis revealed to be as a fundamental technique that the dislocation density was distributed heterogeneously in the deformed grains. Grain growth driven by surface energy reduction was also investigated, finding that it was too slow to explain the experimental data. Based on microstructural results, it was concluded that saturation of the nucleation sites occurred in the first stages of recrystallization, while grain growth driven by strain induced grain boundary migration (SIGBM) dominated the subsequent stages. - Highlights: • Recrystallization behavior of a stainless steel was investigated at 1100 °C. • EBSD revealed that the dislocation density distribution was heterogeneous during deformation. • Saturation of nucleation sites occurred in the first stages of recrystallization. • Strain induced grain boundary migration (SIGBM) effects were significant. • Grain growth driven by SIGBM dominated the subsequent stages.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials

There is a need to characterize complex materials and their dynamics under reaction conditions to accelerate materials design. Adsorbate vibrational excitations are selective to adsorbate/surface interactions and infrared (IR) spectra associated with activating adsorbate vibrational modes are accurate, capture details of most modes, and can be obtained operando. Current interpretation depends on heuristic peak assignments for simple spectra, precluding the possibility of obtaining detailed structural information. Here, we combine data-based approaches with chemistry-dependent problem formulation to develop physics-driven surrogate models that generate synthetic IR spectra from first-principles calculations. Using synthetic IR spectra of carbon monoxide on platinum, we implement multinomial regression via neural network ensembles to learn probability distributions functions (pdfs) that describe adsorption sites and quantify uncertainty. We use these pdfs to infer detailed surface microstructure from experimental spectra and extend this methodology to other systems as a first step towards characterizing complex interfaces and closing the materials gap.

Science & Technology - Other Topics↗
DOE OSTI2026

Active learning enables generation of molecules that advance the known Pareto front

Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the properties of the structures represented in the training distribution. We find that this limitation arises not only from the molecule generation process itself, but also from the poor generalization capabilities of molecular property predictors. We address this challenge by creating a closed-loop molecule generation pipeline with iterative retraining on new quantum chemical simulation data. Compared against static, single-pass generative modeling approaches, only our closed-loop iterative workflow generates molecules with properties extending beyond the training distribution (up to 0.44 standard deviations beyond the original range) and achieves a 79% improvement in out-of-distribution molecule classification accuracy. Furthermore, by conditioning molecular generation on thermodynamic stability data obtained during the iterative loop, the proportion of stable and hence potentially synthesizable molecules generated is 3.5x higher than the next-best model.

Chemistry↗
DOE OSTI2022

Density reconstruction in convergent high-energy-density systems using x-ray radiography and Bayesian inference

X-ray radiography is a technique frequently used to diagnose convergent high-energy-density (HED) systems, such as inertial confinement fusion implosion, and provides unique information that is not available through self-emission measurements. In this work, we investigate the scope and limits of that information using a radiography simulation combined with the Bayesian inference workflow. The accuracy of density reconstruction from simulated radiographs of spherical implosions driven with 27 kJ laser energy is assessed, including the increase or decrease in accuracy due to the addition of Lagrangian marker layers, Poisson noise, and improved prior information. This work is the first to present the full uncertainty distributions inferred from radiography analysis in HED systems and demonstrates the importance of constructing the full posterior probability density, as opposed to a point estimate, due to the modal structure of the likelihood surface introduced by typical experimental noise sources. This general methodology can be used both for robust analysis of radiographic data and for an improved design of radiography experiments by modeling the full experimental system.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗
DOE OSTI2015

Secondary structure of rat and human amylin across force fields

The aggregation of human amylin has been strongly implicated in the progression of Type II diabetes. This 37-residue peptide forms a variety of secondary structures, including random coils, α-helices, and β-hairpins. The balance between these structures depends on the chemical environment, making amylin an ideal candidate to examine inherent biases in force fields. Rat amylin differs from human amylin by only 6 residues; however, it does not form fibrils. Therefore it provides a useful complement to human amylin in studies of the key events along the aggregation pathway. In this work, the free energy of rat and human amylin was determined as a function of α-helix and β-hairpin content for the Gromos96 53a6, OPLS-AA/L, CHARMM22/CMAP, CHARMM22*, Amberff99sb*-ILDN, and Amberff03w force fields using advanced sampling techniques, specifically bias exchange metadynamics. This work represents a first systematic attempt to evaluate the conformations and the corresponding free energy of a large, clinically relevant disordered peptide in solution across force fields. The NMR chemical shifts of rIAPP were calculated for each of the force fields using their respective free energy maps, allowing us to quantitatively assess their predictions. We show that the predicted distribution of secondary structures is sensitive to the choice of force-field: Gromos53a6 is biased towards β-hairpins, while CHARMM22/CMAP predicts structures that are overly α-helical. OPLS-AA/L favors disordered structures. Amberff99sb*-ILDN, AmberFF03w and CHARMM22* provide the balance between secondary structures that is most consistent with available experimental data. In contrast to previous reports, our findings suggest that the equilibrium conformations of human and rat amylin are remarkably similar, but that subtle differences arise in transient alpha-helical and beta-strand containing structures that the human peptide can more readily adopt. We hypothesize that these transient states enable dynamic pathways that facilitate the formation of aggregates and, eventually, amyloid fibrils.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

STELLAR POPULATION SYNTHESIS BASED MODELING OF THE MILKY WAY USING ASTEROSEISMOLOGY OF 13,000 KEPLER RED GIANTS

With current space-based missions it is now possible to obtain age-sensitive asteroseismic information for tens of thousands of red giants. This provides a promising opportunity to study the Galactic structure and evolution. We use asteroseismic data of red giants, observed by Kepler, to test the current theoretical framework of modeling the Galaxy based on population synthesis modeling and the use of asteroseismic scaling relations for giants. We use the open source code Galaxia to model the Milky Way and find the distribution of the masses predicted by Galaxia to be systematically offset with respect to the seismically inferred observed masses. The Galactic model overestimates the number of low-mass stars, and these stars are predominantly old and of low metallicity. Using corrections to the Δν scaling relation suggested by stellar models significantly reduces the disagreement between predicted and observed masses. For a few cases where non-seismic mass estimates are available, the corrections to Δν also improve the agreement between seismic and non-seismic mass estimates. The disagreement between predictions of the Galactic model and the observations is most pronounced for stars with [Fe/H]<−0.5 and [Fe/H]>0 or for T{sub eff}>4700 K. Altering the star formation rate in order to suppress stars older than 10 Gyr improves the agreement for mass but leads to inconsistent color distributions. We also tested the predictions of the TRILEGAL Galactic model. However, unlike Galaxia, it had difficulties in reproducing the photometric properties of the Kepler Input Catalog because it overestimates the number of blue stars. We conclude that either the scaling relations and/or the Galactic models need to be revised to reconcile predictions of theory with asteroseismic observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Nucleon Structure from Lattice QCD Calculations

Parton distribution and correlation functions describe the relation between a hadron and the quarks and gluons (or collectively, the partons) within it, and carry rich information on hadron’s partonic structure that cannot be calculated by QCD perturbation theory. In this talk, I will review what lattice QCD can and cannot do for calculating the parton distribution and correlation functions, and the new ideas and efforts around the world to explore nucleon structure from lattice QCD calculations by combining the strength of both lattice QCD and perturbative QCD in such a way that is complementary to our on-going effort to extract these fundamental functions from experimental data.

Qiu, Jian-Wei↗