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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Validating Nuclear Data Uncertainties Obtained from a Statistical Analysis of Experimental Data with the “Physical Uncertainty Bounds” Method

Concerns within the nuclear data community led to substantial increases of Neutron Data Standards (NDS) uncertainties from its previous to the current version. For example, those associated with the NDS reference cross section 239 Pu(n,f) increased from 0.6–1.6% to 1.3–1.7% from 0.1–20 MeV. These cross sections, among others, were adopted, e.g., by ENDF/B-VII.1 (previous NDS) and ENDF/B-VIII.0 (current NDS). There has been a strong desire to be able to validate these increases based on objective criteria given their impact on our understanding of various application uncertainties. Here, the “Physical Uncertainty Bounds” method (PUBs) by Vaughan et al. is applied to validate evaluated uncertainties obtained by a statistical analysis of experimental data. We investigate with PUBs whether ENDF/B-VII.1 or ENDF/B-VIII.0 239 Pu(n,f) cross-section uncertainties are more realistic given the information content used for the actual evaluation. It is shown that the associated conservative (1.5–1.8%) and minimal realistic (1.1–1.3%) uncertainty bounds obtained by PUBs enclose ENDF/B-VIII.0 uncertainties and indicate that ENDF/B-VII.1 uncertainties are underestimated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Case study of a bore wind-ramp event from lidar measurements and HRRR simulations over ARM Southern Great Plains

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55-70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. In conclusion, the results also provide information about model errors between sites of different surface and vegetation types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Pressure-induced structural and dielectric changes in liquid water at room temperature

Understanding the pressure-dependent dielectric properties of water is crucial for a wide range of scientific and practical applications. In this study, we employ a deep neural network trained on density functional theory data to investigate the dielectric properties of liquid water at room temperature across a pressure range of 0.1–1000 MPa. We observe a nonlinear increase in the static dielectric constant ɛ 0 with increasing pressure, a trend that is qualitatively consistent with experimental observations. This increase in ɛ 0 is primarily attributed to the increase in water density under compression, which enhances collective dipole fluctuations within the hydrogen-bonding network as well as the dielectric response. Despite the increase in ɛ 0 , our results reveal a decrease in the Kirkwood correlation factor G K with increasing pressure. Furthermore, this decrease in G K is attributed to pressure-induced structural distortions in the hydrogen-bonding network, which weaken dipolar correlations by disrupting the ideal tetrahedral arrangement of water molecules.

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

Equity and modeling in sustainability science: Examples and opportunities throughout the process

Equity is core to sustainability, but current interventions to enhance sustainability often fall short in adequately addressing this linkage. Models are important tools for informing action, and their development and use present opportunities to center equity in process and outcomes. This Perspective highlights progress in integrating equity into systems modeling in sustainability science, as well as key challenges, tensions, and future directions. We present a conceptual framework for equity in systems modeling, focused on its distributional, procedural, and recognitional dimensions. We discuss examples of how modelers engage with these different dimensions throughout the modeling process and from across a range of modeling approaches and topics, including water resources, energy systems, air quality, and conservation. Synthesizing across these examples, we identify significant advances in enhancing procedural and recognitional equity by reframing models as tools to explore pluralism in worldviews and knowledge systems; enabling models to better represent distributional inequity through new computational techniques and data sources; investigating the dynamics that can drive inequities by linking different modeling approaches; and developing more nuanced metrics for assessing equity outcomes. We also identify important future directions, such as an increased focus on using models to identify pathways to transform underlying conditions that lead to inequities and move toward desired futures. By looking at examples across the diverse fields within sustainability science, we argue that there are valuable opportunities for mutual learning on how to use models more effectively as tools to support sustainable and equitable futures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Strongly intermittent far scrape-off layer fluctuations in Alcator C-Mod plasmas close to the empirical discharge density limit

Abstract Intermittent plasma fluctuations in the boundary region of the Alcator C-Mod device were comprehensively investigated using data time-series from gas puff imaging and mirror Langmuir probe diagnostics. Fluctuations were sampled during stationary plasma conditions in ohmically heated, lower single null diverted configurations with scans in both line-averaged density and plasma current, with Greenwald density fractions up to 0.85. Utilizing a stochastic model, we describe the plasma fluctuations as a super-position of uncorrelated pulses, with large-amplitude events corresponding to blob-like filaments moving through the scrape-off layer (SOL). A deconvolution method is used to estimate the pulse arrival times and amplitudes. The analysis reveals a significant increase of pulse amplitudes and waiting times as the line-averaged density approaches the empirical discharge density limit. Broadened and flattened average radial profiles are thus accompanied by strongly intermittent and large-amplitude fluctuations. Although these filaments are arriving less frequently at high line-averaged densities, we show that there are significant increases in radial far-SOL particle and heat fluxes which will further enhance plasma–wall interactions. The stochastic model has been used as a framework for study of the scalings in the intermittency parameter, flux and mean amplitude and waiting times, and is being used to inform predictive capability for the effects of filamentary transport as a function of Greenwald fraction.

Physics↗
DOE OSTI2024

Milky Way white dwarfs as sub-GeV to multi-TeV dark matter detectors

Here, we show that Milky Way white dwarfs are excellent targets for dark matter (DM) detection. Using Fermi and H.E.S.S. Galactic center gamma-ray data, we investigate sensitivity to DM annihilating within white dwarfs into long-lived or boosted mediators and producing detectable gamma rays. Depending on the Galactic DM distribution, we set new constraints on the spin-independent scattering cross section down to 10 -45 -10 -41 cm 2 in the sub-GeV DM mass range, which is multiple orders of magnitude stronger than existing limits. For a generalized NFW DM profile, we find that our white dwarf constraints exceed spin-independent direct detection limits across most of the sub-GeV to multi-TeV DM mass range, achieving sensitivities as low as about 10 -46 cm 2 . In addition, we improve earlier versions of the DM capture calculation in white dwarfs, by including the low-temperature distribution of nuclei when the white dwarf approaches crystallization. This yields smaller capture rates than previously calculated by a factor of a few up to two orders of magnitude, depending on white dwarf size and the astrophysical system.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

High-pressure neutron diffraction at ultra-low temperature

Neutron diffraction is a powerful tool in the study of quantum materials and associated emergent phenomena as it allows for direct detection of magnetic structure even at ultra-low temperatures. As pressure can drive systems across quantum critical points and tune emergent behaviors at the quantum scale, coupling neutron diffraction with extreme conditions of high pressure at (ultra-)low temperatures has been a key research target over decades. This has proven challenging as magnetic neutron diffraction typically requires large sample volumes due to flux limitations, whereas high-pressure studies require small sample volumes due to pressure limitations. Combining these two contradictory requirements poses many difficulties, but also brings opportunities in research and development. Here, we address these shortcomings through the use of a diamond anvil cell equipped with polycrystalline anvils with relatively large sample volumes coupled with the high neutron flux made available by the high-pressure diffractometer of the Spallation Neutron Source. This particular diamond anvil cell configuration has previously proven useful for single-crystal samples and is here coupled with the ultra-low temperatures of a dilution refrigerator for the first time. Our study on a single crystal of Yb 2 ⁢O 3 successfully collected magnetic neutron diffraction data at 5.3 GPa below 200 mK inside a dilution refrigerator. We have performed a detailed analysis of the magnetic diffraction data to investigate the magnetic structure under pressure and observed a subtle shift in the transition temperature. This development will open the door for future studies on magnetic materials with these much-desired coupled extremes of high pressure and ultra-low temperatures.

Wu, Yan [Oak Ridge National Laboratory (ORNL), Oak↗
DOE OSTI2020

Results on low-mass weakly interacting massive particles from an 11 kg d target exposure of DAMIC at SNOLAB

We present constraints on the existence of weakly interacting massive particles (WIMPs) from a 11 kg-day target exposure of the DAMIC experiment at the SNOLAB underground laboratory. The observed energy spectrum and spatial distribution of ionization events with electron-equivalent energies $>$200 eV$_{\rm ee}$ in the DAMIC CCDs are consistent with backgrounds from natural radioactivity. An excess of ionization events is observed above the analysis threshold of 50 eV$_{\rm ee}$. While the origin of this low-energy excess requires further investigation, our data exclude spin-independent WIMP-nucleon scattering cross sections $\sigma_{\chi-n}$ as low as $3\times 10^{-41}$ cm$^2$ for WIMPs with masses $m_{\chi}$ from 7 to 10 GeV$c^{-2}$ . These results are the strongest constraints from a silicon target on the existence of WIMPs with $m_{\chi}$$<$9 GeV$c^{-2}$ and are directly relevant to any dark matter interpretation of the excess of nuclear-recoil events observed by the CDMS silicon experiment in 2013.

WIMP search, Dark Matter, DAMIC, SNOLAB↗
DOE OSTI2022

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Novel CHI3L1 ‐Associated Angiogenic Phenotypes Define Glioma Microenvironments: Insights From Multi‐Omics Integration

ABSTRACT The CHI3L1 signaling pathway significantly influences glioma angiogenesis, but its role in the tumor microenvironment (TME) remains elusive. We propose a novelCHI3L1‐associated vascular phenotype classification for glioma through integrative analyses of multiple datasets with bulk and single‐cell transcriptome, genomics, digital pathology, and clinical data. We investigated the biological characteristics, genomic alterations, therapeutic vulnerabilities, and immune profiles within these phenotypes through a comprehensive multi‐omics approach. We constructed the vascular‐related risk (VR) score based onCHI3L1‐associated vascular signatures (CAVS) identified by machine learning algorithms. Utilizing unsupervised consensus clustering, gliomas were stratified into three distinct vascular phenotypes: Cluster A, marked by high vascularization and stromal activation with a relatively low levels of tumor‐infiltrating lymphocytes (TILs); Cluster B, characterized by moderate vascularization and stromal activity, coupled with a high density of TILs; and Cluster C, defined by low vascularization and sparse immune cell infiltration. We observed that the CAVS effectively indicated glioma‐associated angiogenesis and immune suppression by single‐cell RNA‐seq analysis. Moreover, the high‐VR‐score group exhibited enhanced angiogenic activity, reduced immune response, resistance to immunotherapy, and poorer clinical outcomes. The VR score independently predicted glioma prognosis and, combined with a nomogram, provided a robust clinical decision‐making tool. Potential drug prediction based on transcription factors for high‐risk patients was also performed. Our study reveals thatCHI3L1‐associated vascular phenotypes shape distinct immune landscapes in gliomas, offering insights for optimizing therapeutic strategies to improve patient outcomes.

Oncology↗
DOE OSTI2024

Comparative genomics and stable isotope analysis reveal the saprotrophic-pathogenic lifestyle of a neotropical fungus

In terrestrial forested ecosystems, fungi may interact with trees in at least three distinct ways: (i) associated with roots as symbionts; (ii) as pathogens in roots, trunks, leaves, flowers, and fruits; or (iii) decomposing dead tree tissues on soil or even on dead tissues in living trees. Distinguishing the latter two nutrition modes is rather difficult in Hymenochaetaceae (Basidiomycota) species. Herein, we have used an integrative approach of comparative genomics, stable isotopes, host tree association, and bioclimatic data to investigate the lifestyle ecology of the scarcely known neotropical genus Phellinotus, focusing on the unique species Phellinotus piptadeniae. This species is strongly associated with living Piptadenia gonoacantha (Fabaceae) trees in the Atlantic Forest domain on a relatively high precipitation gradient. Phylogenomics resolved P. piptadeniae in a clade that also includes both plant pathogens and typical wood saprotrophs. Furthermore, both genome-predicted Carbohydrate-Active Enzymes (CAZy) and stable isotopes (δ 13 C and δ 15 N) revealed a rather flexible lifestyle for the species. Altogether, our findings suggest that P. piptadeniae has been undergoing a pathotrophic specialization in a particular tree species while maintaining all the metabolic repertoire of a wood saprothroph.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Relationships between summertime surface albedo and melt pond fraction in the central Arctic Ocean: The aggregate scale of albedo obtained on the MOSAiC floe

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), the HELiX uncrewed aircraft system (UAS) was deployed over the sea ice in the central Arctic Ocean during summer 2020. Albedo measurements were obtained with stabilized pyranometers, and melt pond fraction was calculated from orthomosaic imagery from a surface-imaging multispectral camera. This study analyzed HELiX flight data to provide insights on the temporal and spatial evolution of albedo and melt pond fraction of the MOSAiC floe during the melt season as it drifted south through Fram Strait. The surface albedo distributions showed peak values changing from high albedo (0.55–0.6) to lower values (0.3) as the season advanced. Inspired by methods developed for satellite data, an algorithm was established to retrieve melt pond fraction from the orthomosaic images. We demonstrate that the near-surface observations of melt pond fraction were highly dependent on sample area, offering insight into the influence of subgrid scale features and spatial heterogeneity in satellite observations. Vertical observations conducted with the HELiX were used to quantify the influence of melt pond scales on observed surface albedo as a function of sensor footprint. These scaling results were used to link surface-based measurements collected during MOSAiC to broader-scale satellite data to investigate the influence of surface features on observed albedo. Albedo values blend underlying features within the sensor footprint, as determined by the melt pond size and concentration. This study framed the downscaling (upscaling) problem related to the airborne (surface) observations of surface albedo across a variety of spatial scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Advancing Sea Ice Predictability in E3SM with Machine Learning

Focal area(s): To improve predictions of sea ice in E3SM we propose to develop a hierarchy of data-driven models using observational and simulation data to investigate the most important Earth system drivers of sea ice variability and loss, develop surrogates that build on the reduced parameter space of important drivers, and, where appropriate, couple machine learning models with standard PDE models to capture important physical behavior at different scales. This work falls under Focal Area 2. Predictive modeling through the use of AI techniques.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantitative assessment of Distributed Acoustic Sensing at the Source Physics Experiment, Phase II

In this report, we assess the data recorded by a Distributed Acoustic Sensing (DAS) cable deployed during the Source Physics Experiment, Phase II (DAG) in comparison with the data recorded by nearby 4.5-Hz geophones. DAS is a novel recording method with unprecedented spatial resolution, but there are significant concerns around the data fidelity as the technology is ramped up to more common usage. Here we run a series of tests to quantify the similarity between DAS data and more conventional data and investigate cases where the higher spatial resolution of the DAS can provide new insights into the wavefield. These tests include 1D modeling with seismic refraction and bootstrap uncertainties, assessing the amplitude spectra with distance from the source, measuring the frequency dependent inter-station coherency, estimating time-dependent phase velocity with beamforming and semblance, and measuring the cross-correlation between the geophone and the particle velocity inferred from the DAS. In most cases, we find high similarity between the two datasets, but the higher spatial resolution of the DAS provides increased details and methods of estimating uncertainty.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Quantitative assessment of Distributed Acoustic Sensing at the Source Physics Experiment (Phase II)

In this report, we assess the data recorded by a Distributed Acoustic Sensing (DAS) cable deployed during the Source Physics Experiment, Phase II (DAG) in comparison with the data recorded by nearby 4.5-Hz geophones. DAS is a novel recording method with unprecedented spatial resolution, but there are significant concerns around the data fidelity as the technology is ramped up to more common usage. Here we run a series of tests to quantify the similarity between DAS data and more conventional data and investigate cases where the higher spatial resolution of the DAS can provide new insights into the wavefield. These tests include 1D modeling with seismic refraction and bootstrap uncertainties, assessing the amplitude spectra with distance from the source, measuring the frequency dependent inter-station coherency, estimating time-dependent phase velocity with beamforming and semblance, and measuring the cross-correlation between the geophone and the particle velocity inferred from the DAS. In most cases, we find high similarity between the two datasets, but the higher spatial resolution of the DAS provides increased details and methods of estimating uncertainty.

58 GEOSCIENCES↗
DOE OSTI2023

VA-Environmental Determinants of Health’s Software Pipeline Framework

In order to characterize US communities in terms of social and environmental determinants of health (EDH) that affect veterans, the Social and Environmental Determinants of Health (SEDH) project, a collaboration between the Veterans Affairs (VA) Social Department and the Oak Ridge National Laboratory (ORNL), aims to identify, extract, and prepare datasets that include community variables known to be associated with health outcomes in veterans. The EDH project's responsibilities include investigating SEDH data, providing technical documentation for SEDH datasets, and automating the download, extraction, and preparation of the data through the creation of a software pipeline framework.

54 ENVIRONMENTAL SCIENCES↗