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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Many-Body Benchmark of Electronic Charge and Spin Densities for Li 1–x NiO 2

Accurate benchmarks are particularly important for highly correlated oxides as mean-field approximations often fail to describe the subtle balance of charge transfer and magnetism in these materials with an accuracy comparable to experimental needs. Here we present accurate diffusion Monte Carlo (DMC) results of the electronic charge and spin densities for the tunable highly correlated oxide Li 1–x NiO 2 for x = 0, 1/2, and 1. To enable quantitative comparisons, we introduce a robust density-partitioning scheme, extending Voronoi analysis to assign atomic charges from spatially noisy DMC densities. We then benchmark common approximations used in density functional theory (DFT). Comparison against DMC shows that r 2 SCAN delivers the most balanced performance across charge, spin, and radial density descriptors, nearly reproducing DMC results for LiNiO 2 and apical Ni sites in Li 0.5 NiO 2 . Hybrid functionals (PBE0, SCAN0) perform unexpectedly poorly, and PBE + U + V yields inconsistent trends between charge and spin densities. Therefore, the r 2 SCAN functional minimizes errors relative to DMC while capturing the variable valence of the Ni ion and also retaining the computational efficiency of DFT for large-scale simulations of the tunable structural and electronic phases of Li1−xNiO2. Our study highlights the importance of accurate benchmarking of the fundamental quantities involved in DFT to select appropriate DFT approximations in order to advance the predictive modeling of charge-transfer-driven phenomena in correlated electron systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Role of Mineralogy in Controlling Fracture Formation

The presence of fractures in caprocks can pose increased risks in subsurface energy systems and processes like CO 2 sequestration by introducing high-permeability leakage paths. Fracture apertures and permeability can be altered through mineral dissolution and precipitation reactions, but the reactive evolution of fractures is not well understood. In fractures, minerals that are otherwise inaccessible to reactive fluids can become exposed, resulting in mineral reactions unpredicted by bulk formation data. This work seeks to understand the relationship between mineralogy and fracture formation to enhance our understanding of reactive fracture evolution and CO 2 leakage potential. Here, the mineral compositions of mechanically induced fracture surfaces in samples of the Mancos and Marcellus shales have been quantified and compared to those of the near-fracture matrices using imaging and bulk X-ray diffraction (XRD) data. In the Mancos shale, the concentrations of clay minerals are enhanced along fracture surfaces with respect to the bulk, and the fracture is most likely to form at kaolinite–kaolinite interfaces. Further, evaluation of the mineralogical spatial variability through cross-correlation analysis of the surrounding matrix in images of samples cut perpendicular to the fracture shows that clay is 16.7 times more likely to be present than carbonate minerals near the fracture surface. The high correlation persists roughly 200 μm into the surrounding matrix for the Mancos sample and implies that the fracture formed within a defined clay-rich lithofacies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Exploration of LIBS as a novel and rapid elemental mapping technique of nuclear fuels in the form of surrogate TRISO particles

Laser-induced breakdown spectroscopy (LIBS) was employed to characterize coatings on surrogate fuel particles. Tri-structural isotropic (TRISO) particles are a proposed nuclear fuel alternative for high temperature reactors. These particles are constructed of a ZrO 2 kernel (as a surrogate to uranium), surrounded by an inner pyrolytic carbon layer and are surrounded by an outer carbide layer (ZrC, presented here) to act as a barrier to fission products generated during nuclear reactions. These particles are embedded within a graphite compact and housed within the reactor core. Simply put, due to their robust nature, performing elemental analysis of these particles poses a challenge. Presented here, LIBS is explored as a method for characterizing elemental constituents of these particles, with the focus being on rapid elemental mapping and depth profiling. Different from traditional elemental analysis techniques (e.g., inductively coupled plasma – based methods), LIBS is advantageous because it can directly analyze the sample surface and can detect light elements such as C and O, making it a viable technique for the analysis of small, multilayered particles as spatial elemental information is warranted in the production of these particles. In the work presented here, LIBS was successfully used for discerning small layers (30–50 μm), detecting the location of carbon and oxygen layers, providing fast 2-D mapping (<5 min per particle) and rapid depth profiling (10 s per particle).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Impact of bar resonances in the velocity–space distribution of the solar neighbourhood stars in a self-consistent N -body Galactic disc simulation

ABSTRACT The velocity–space distribution of the solar neighbourhood stars shows complex substructures. Most of the previous studies use static potentials to investigate their origins. Instead we use a self-consistent N-body model of the Milky Way, whose potential is asymmetric and evolves with time. In this paper, we quantitatively evaluate the similarities of the velocity–space distributions in the N-body model and that of the solar neighbourhood, using Kullback–Leibler divergence (KLD). The KLD analysis shows the time evolution and spatial variation of the velocity–space distribution. The KLD fluctuates with time, which indicates the velocity–space distribution at a fixed position is not always similar to that of the solar neighbourhood. Some positions show velocity–space distributions with small KLDs (high similarities) more frequently than others. One of them locates at $(R,\phi)=(8.2\,\,\rm{\mathrm{kpc}}, 30^\circ)$, where R and ϕ are the distance from the galactic centre and the angle with respect to the bar’s major axis, respectively. The detection frequency is higher in the inter-arm regions than in the arm regions. In the velocity maps with small KLDs, we identify the velocity–space substructures, which consist of particles trapped in bar resonances. The bar resonances have significant impact on the stellar velocity–space distribution even though the galactic potential is not static.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Lignin structural changes and high p -coumaroylation in incipient lignification in moso bamboo

Lignification is a crucial process for strengthening plant tissues, facilitating water transport, and providing defense against pathogens. In the Poaceae family, p-hydroxycinnamic acids are commonly incorporated into lignin, with acylation by p-coumarate (pCA) occurring during lignification. In this study, we performed DFRC and 2D HSQC-NMR analyses to investigate changes in lignin substructures and the degree of lignin pCA-acylation throughout bamboo stem development. Furthermore, immunohistochemical analysis was conducted to elucidate the spatial distribution of lignin substructures within different cell types. Our results revealed that, in young tissues, β–O–4-linked lignin units are predominantly derived from monolignol-pCA conjugates, specifically coniferyl- and sinapyl-pCA. Both lignin structure and the pattern of pCA acylation varied depending on the stage of cell wall formation and the cell type, particularly between vascular fiber cells and parenchyma cells. Based on our results, moso bamboo culms exhibit a distinctive feature during incipient lignification, in which monolignols are predominantly acylated with pCA. As a result, this feature has not been reported in other grasses, suggesting that extensive p-coumaroylation of monolignols plays an important role in the rapid elongation of bamboo culms.

Munekata, Noriaki [Kyoto University (Japan); Unive↗
DOE OSTI2022

Reversible motions and disordered structure of soft particles in amorphous solids

In amorphous solids, soft vibrational modes derived from normal mode analysis can be utilized to identify the soft particles that are prone to irreversible rearrangements. However, the normal mode analysis approach cannot explain why the spatial distributions of clustered soft particles do not change over time. We define a softness parameter based on the vibrational density of states calculated directly from molecular dynamics simulations with both the harmonic vibrations and anharmonic relaxations being properly captured at finite temperatures. This parameter spontaneously correlates with the real space atomic motions and the dynamics heterogeneity. Using the softness parameter, we show that the softest particles are confined within rigid cages. These particles keep rearranging reversibly without long-range diffusion. The moderately soft particles rearrange irreversibly, and the hard particles mainly participate in vibrations without rearrangement. Here, we also show that the soft particles form locally disordered structures, while the hard particles present strong ordering. These findings confirm the defective nature of soft particles, and provide insights on the nature of softness as the ability to rearrange, but not necessarily irreversibly.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Milky Way Satellite Census. III. Constraints on Dark Matter Properties from Observations of Milky Way Satellite Galaxies

We perform a comprehensive study of Milky Way (MW) satellite galaxies to constrain the fundamental properties of dark matter (DM). This analysis fully incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and marginalizes over uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk. Our results are consistent with the cold, collisionless DM paradigm and yield the strongest cosmological constraints to date on particle models of warm, interacting, and fuzzy dark matter. At $95\%$ confidence, we report limits on (i) the mass of thermal relic warm DM, $m_{\rm WDM} > 6.5\ \mathrm{keV}$ (free-streaming length, $\lambda_{\rm{fs}} \lesssim 10\,h^{-1}\ \mathrm{kpc}$), (ii) the velocity-independent DM-proton scattering cross section, $\sigma_{0} < 8.8\times 10^{-29}\ \mathrm{cm}^{2}$ for a $100\ \mathrm{MeV}$ DM particle mass (DM-proton coupling, $c_p \lesssim (0.3\ \mathrm{GeV})^{-2}$), and (iii) the mass of fuzzy DM, $m_{\phi}> 2.9 \times 10^{-21}\ \mathrm{eV}$ (de Broglie wavelength, $\lambda_{\rm{dB}} \lesssim 0.5\ \mathrm{kpc}$). These constraints are complementary to other observational and laboratory constraints on DM properties.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Computational Fluid Dynamics Study of a Cross-Flow Marine Hydrokinetic Turbine and the Combined Influence of Struts and Helical Blades

A computational fluid dynamics study was performed for a cross-flow marine hydrokinetic turbine. The analysis was done in three dimensions and used the unsteady Reynolds-averaged Navier-Stokes solver in the commercial code STAR-CCM+. The base turbine configuration is the RivGen(R) Turbine, designed by the Ocean Renewable Power Company. A convergence and uncertainty analysis was performed for both the spatial and temporal discretization; this was done using the base configuration, which features support struts and helical foils. Both struts and helical blades introduce three-dimensional flow effects, influencing the complex flow phenomenon of dynamic stall. The study compares the relative impact of struts on power performance and blade loading for both helical and straight blades, and found that for this turbine the relative loss in power from struts was lower with helical blades.

CFD↗
DOE OSTI2024

Red–green–blue Boolean image analysis of particulate debris laced with luminescent tracers

Abstract Particulate mass estimation from 3-pixel images is desirable in many fields. Red–green–blue (RGB) analysis and Boolean logic were shown to estimate the mass of luminescent tracers in microscopic images. With a controlled background intensity, an estimation error of 1.8 to 3.5% was achieved; in uncontrolled backgrounds, an error of about 18% was achieved. RGB analysis is a valuable tool for spatial location of particulates. This work shows it is possible to estimate the particulate mass in an image and gives RGB an extension into mass quantification that has far-reaching impacts in fields involving the fate and transport of particulate matter. Graphical abstract

36 MATERIALS SCIENCE↗
DOE OSTI2022

Heating and Acceleration of Solar Wind by Parametric Decay Instability Mediated Turbulence

One of the main goals of this project to understand the origin of density fluctuations in the solar winds, as revealed by the NASA’s Flagship satellite Parker Solar Probe (PSP) measurements, which motivates us to simulate and analyze magnetohydrodynamic (MHD) turbulence in the compressible regime. To achieve this goal, we have developed a set of sophisticated turbulence driving methods to study how MHD turbulence will develop under different time-correlated fluctuations at large-scales. Another key advance is that we have developed a new 3+1 (three spatial plus time domain) 4D FFT analysis of simulation data-cubes so that we can perform the spatio-temporal spectrum and mode decomposition studies on MHD turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Impacts of Substrate Thinning on FPGA Performance and Reliability [Slides]

Substrate thinning is necessary in devices with flip-chip BGA packages to enable both radiation testing and component qualification and high-spatial resolution beam-based failure analysis methods. We investigated three factors affecting device performance: subsurface damage from the thinning process, reduced heat spreading in thin substrates, and changes in device switching speed. We conclude subsurface damage to crystalline Si caused by the thinning process is removable with sufficient SiO 2 slurry polishing. Local temperature differences increase minimally in devices thinned to 3 μm. Compressive stress in the Si increases globally after device thinning and leads to slowing of ring oscillator frequency by about 0.5% compared to full-thickness devices. Future work will include extending the results to submicron Si thickness values, which also has important benefits for failure analysis, debug, and security assessments. We also plan to extend this type of work to other FPGAs and other devices like memory and processors.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Status Report on Characterization of High Burnup Fuel with Advanced Nondestructive Pulsed Neutron PIE

Characterizing irradiated or spent nuclear fuels with pulsed neutron techniques provides microstructural data such as phase fractions as well as crystallographic data, e.g. lattice parameters, from diffraction analysis. Diffraction characterization is complemented by spatially resolved mapping of isotope densities from energy-resolved neutron imaging, in particular neutron absorption resonance imaging, and overall bulk isotope assay with better sensitivity for minority isotopes from neutron absorption resonance spectroscopy without spatial resolution. Furthermore, after characterization at ambient condition, heating of irradiated or spent fuel will allow to characterize differences of e.g. lattice thermal expansion or phase transition temperature and kinetics compared to fresh fuel as well as enable the study of disappearance of irradiation defects. This data enables benchmarking of predictions of properties of irradiated fuels for which otherwise experimental data is sparse. The effort described here strives to characterize a section cut from a high-burnup fuel. Volumes smaller than entire fuel pellets or rodlets as proposed here, e.g. sections cut from a fuel pellet, to pave the way to characterize entire pellets or rodlets in the future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Improving North American Wildfire Prediction by Integrating a Machine-Learning Fire Model in a Land Surface Model

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Simulated wildfire burned area over the CONUS during 2001-2020

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM). A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001-2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM.

Liu, Ye↗
DOE OSTI2025

Hyperspectral segmentation of plants in fabricated ecosystems

Hyperspectral imaging provides a powerful tool for analyzing above-ground plant characteristics in fabricated ecosystems, offering rich spectral information across diverse wavelengths. This study presents an efficient workflow for hyperspectral data segmentation and subsequent data analytics, minimizing the need for user annotation through the use of ensembles of sparse mixed scale convolution neural networks. The segmentation process leverages the diversity of ensembles to achieve high accuracy with minimal labeled data, reducing labor-intensive annotation efforts. To further enhance robustness, we incorporate image alignment techniques to address spatial variability in the dataset. Downstream analysis focuses on using the segmented data for processing spectral data, enabling monitoring of plant health. This approach provides a scalable solution for spectral segmentation, and facilitates actionable insights into plant conditions in complex, controlled environments. Our results demonstrate the utility of combining advanced machine learning techniques with hyperspectral analytics for high-throughput plant monitoring.

Zwart, Petrus H.↗
DOE OSTI2020

Mars Upper Atmospheric Temperature and Atomic Oxygen Density Derived from the O i 130.4 nm Emission Observed by NASA’s MAVEN Mission

The abundance of atomic oxygen is a major unknown for the Mars upper atmosphere, and systematic measurements of this key species is a primary objective of NASA’s Mars Atmosphere and Volatile Evolution (MAVEN) mission. Moreover, the Martian dayside temperature structure and its variability has been a subject of considerable debate in the past. In this study, an inversion analysis of the periapsis limb scans of the O i 130.4 nm emission observed by MAVEN is performed to quantify the neutral temperature and atomic oxygen abundance in the Mars upper atmosphere. Our analysis yields unprecedented resolution of the spatial variation of Mars atomic oxygen, showing that its exobase density decreases with the solar zenith angle (SZA) from ∼0° to 60° by about an order of magnitude, from ∼10{sup 8} to ∼10{sup 7} cm{sup −3} near perihelion (2015 April) and from ∼10{sup 7} to ∼10{sup 6} cm{sup −3} near aphelion (2017 November). For a given SZA the exobase densities near perihelion are about an order of magnitude higher than those near aphelion. The [O]/[CO{sub 2}] mixing ratio at 130 km altitude can vary from ∼0.1% to 4.6%, which also decreases with increasing SZA. Moreover, it is shown that the Mars exobase temperature does not vary significantly with SZA from ∼0° to ∼60°, which is estimated to be ∼239.2 ± 27.6 K near perihelion and ∼162.4 ± 19.9 K near aphelion, with a difference of 76.8 K between these two seasonal extremes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Anisotropic Energy Transfer and Conversion in Magnetized Compressible Turbulence

We present a spatial filtering (or coarse-graining) analysis on 3D magnetized magnetohydrodynamic (MHD) turbulence simulations. The filtered compressible MHD formulae show transfer of kinetic and magnetic energies from large to small scales, as well as energy conversion between kinetic, magnetic, and thermal energies. The anisotropic filtering enables separate analyses of the energy flows perpendicular and parallel to the global mean magnetic field. Anisotropy in energy cascade is demonstrated by the larger perpendicular energy cascade rate and also the larger perpendicular wavenumbers associated with the peak energy transfer rate. We also find that the “inertial range” along the parallel (perpendicular) direction in the anisotropic energy cascade formulation is no longer strictly dissipation-free, because it includes the dissipation in the perpendicular (parallel) direction. A change in the driving force (kinetic only versus kinetic and magnetic) affects the energy conversion between kinetic and magnetic energies. While the compressibility of the driving force changes the partition of different channels of energy transfer and conversion, and also increases the total energy transfer rate, the global energy flow remains unaffected by compressibility qualitatively. Our analysis can be applied to multispacecraft observations of turbulence in the solar wind or a planetary magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Abstract. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the main set of future climate projections, based on concentration-driven simulations, within the Coupled Model Intercomparison Project phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century (2081–2100) encompassing the Tier 1 experiments based on the Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by close to 1.5 ∘C) reached at the upper end of the 5 %–95 % envelope of the highest scenario (SSP5-8.5). This is due to both the wider range of radiative forcing that the new scenarios cover and the higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensemble spreads, according to a set of initial condition ensemble simulations available under SSP3-7.0. These experiments suggest a tendency for internal variability to decrease along the course of the century in this scenario, a result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades around mid-century, as represented in SSP5-3.4OS, does not affect the end outcome of temperature and precipitation changes by 2100, which return to the same levels as those reached by the gradually increasing SSP4-3.4 (not erasing the possibility, however, that other aspects of the system may not be as easily reversible). Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level might be biased by the inclusion of models that have shown faster warming in the historical period than the observed. Those estimates show all scenarios reaching 1.5 ∘C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering between 20 and 27 years from present. The warming level of 2 ∘C of warming is reached as early as 2039 by the ensemble mean under SSP5-8.5 but as late as the mid-2060s under SSP1-2.6. The highest warming level considered (5 ∘C) is reached by the ensemble mean only under SSP5-8.5 and not until the mid-2090s.

54 ENVIRONMENTAL SCIENCES↗