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Results for “Data Projection”

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 127 records · Page 7

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Metallographic examinations and hydrogen measurements of high-burnup spent nuclear fuel cladding

In the US, commercial spent nuclear fuel (SNF) is transferred to interim dry storage casks where it will be stored for decades awaiting transport to a consolidated interim storage facility or a geologic repository. Because the fuel rod cladding is the first barrier against any radioactive material release, understanding the behavior of SNF cladding, particularly at high burnup (HBU), in dry storage conditions is crucial to safely store and transport the spent fuel. In this study, a series of metallographic examinations and cladding hydrogen measurements were conducted on HBU SNF cladding at Oak Ridge National Laboratory as a part of the High Burnup Spent Fuel Data Project, which is sponsored by the US Department of Energy (DOE) Office of Nuclear Energy (NE). Here, t o investigate the effect of simulated drying conditions on the cladding, three as-received fuel rods with different cladding materials—M5, ZIRLO, and Zircaloy-4—were heated to 400°C and then slow-cooled to room temperature. The pellet and cladding were then qualitatively and quantitatively analyzed and compared in terms of pellet crack morphology, HBU rim, waterside oxide, cladding H, and cladding hydride morphologies. This paper presents and discusses the results of these analyses in detail.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Hyperactive nanobacteria with host-dependent traits pervade Omnitrophota

Abstract Candidate bacterial phylum Omnitrophota has not been isolated and is poorly understood. We analysed 72 newly sequenced and 349 existing Omnitrophota genomes representing 6 classes and 276 species, along with Earth Microbiome Project data to evaluate habitat, metabolic traits and lifestyles. We applied fluorescence-activated cell sorting and differential size filtration, and showed that most Omnitrophota are ultra-small (~0.2 μm) cells that are found in water, sediments and soils. Omnitrophota genomes in 6 classes are reduced, but maintain major biosynthetic and energy conservation pathways, including acetogenesis (with or without the Wood-Ljungdahl pathway) and diverse respirations. At least 64% of Omnitrophota genomes encode gene clusters typical of bacterial symbionts, suggesting host-associated lifestyles. We repurposed quantitative stable-isotope probing data from soils dominated by andesite, basalt or granite weathering and identified 3 families with high isotope uptake consistent with obligate bacterial predators. We propose that most Omnitrophota inhabit various ecosystems as predators or parasites.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Quantifying airborne fraction trends and the destination of anthropogenic CO 2 by tracking carbon flows in a simple climate model

Abstract Atmospheric carbon dioxide (CO 2 ) concentrations have increased as a direct result of human activity and are at their highest level over the last 2 million years, with profound impacts on the Earth system. However, the magnitude and future dynamics of land and ocean carbon sinks are not well understood; therefore, the amount of anthropogenic fossil fuel emissions that remain in the atmosphere (the airborne fraction) is poorly constrained. This work aims to quantify the sources and controls of atmospheric CO 2 , the fate of anthropogenic CO 2 over time, and the likelihood of a trend in the airborne fraction. We use Hector v3.0, a coupled simple climate and carbon cycle model with the novel ability to explicitly track carbon as it flows through the Earth system. We use key model parameters in a Monte Carlo analysis of 15 000 model runs from 1750 to 2300. Results are filtered for physical realism against historical observations and CMIP6 projection data, and we calculate the relative importance of parameters controlling how much anthropogenic carbon ends up in the atmosphere. Modeled airborne fraction was roughly 52%, consistent with observational studies. The overwhelming majority of model runs exhibited a negative trend in the airborne fraction from 1960–2020, implying that current-day land and ocean sinks are proportionally taking up more carbon than the atmosphere. However, the percentage of atmospheric CO 2 derived from anthropogenic origins can be much higher because of Earth system feedbacks. We find it peaks at over 90% between 2010–2050. Moreover, when looking at the destination of anthropogenic fossil fuel emissions, only a quarter ends up in the atmosphere while more than half of emissions are taken up by the land sink on centennial timescales. This study evaluates the likelihood of airborne fraction trends and provides insights into the dynamics of anthropogenic CO 2 in the Earth system.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Enhancing synchrotron radiation micro-CT images using deep learning: an application of Noise2Inverse on bone imaging

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

Obata, Yoshihiro (ORCID:0000000303659129)↗

DOE OSTI2023

Deep Learning Based Workflow for Accelerated Industrial X-Ray Computed Tomography

X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a new workflow based on the use of two neural networks to obtain high-quality accelerated reconstructions from sparse-view XCT scans of single material metal parts. The first network, implemented using fully-connected layers, helps reduce the impact of BH in the projection data without the need of any calibration or knowledge of the component material. The second network, a convolutional neural network, maps a low-quality analytic 3D reconstruction to a high-quality reconstruction. Using experimental data, we demonstrate that our method robustly generalizes across several alloys, and for a range of sparsity levels without any need for retraining the networks thereby enabling accurate and fast industrial XCT inspections.

Rahman, Obaid↗

DOE OSTI2024

Quantifying the regime of thermodynamic control for solid-state reactions during ternary metal oxide synthesis

The success of solid-state synthesis often hinges on the first intermediate phase that forms, which determines the remaining driving force to produce the desired target material. Recent work suggests that when reaction energies are large, thermodynamics primarily dictates the initial product formed, regardless of reactant stoichiometry. Here, we validate this principle and quantify its constraints by performing in situ characterization on 37 pairs of reactants. These experiments reveal a threshold for thermodynamic control in solid-state reactions, whereby initial product formation can be predicted when its driving force exceeds that of all other competing phases by ≥60 milli–electron volt per atom. In contrast, when multiple phases have a comparable driving force to form, the initial product is more often determined by kinetic factors. Analysis of the Materials Project data shows that 15% of possible reactions fall within the regime of thermodynamic control, highlighting the opportunity to predict synthesis pathways from first principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

dsgrid (Demand-Side Grid Toolkit) [SWR-21-52]

The demand-side grid model harnesses decades of sector-specific energy modeling expertise to understand current and future U.S. electricity load for power systems analyses. The API provides tools for submitting datasets to dsgrid projects, validating individual datasets and project data as a whole, querying, analysis, and visualization.

Hale, Elaine↗

DOE OSTI2025

SEAM

Library for handling SHERPA-based databasing, summary, and discovery of a project data archive.

Pital, Aaron↗

DOE OSTI2022

The Mechanical Response of High Burnup 17x17 PWR Fuel Rods Under Bending

As a part of the US Department of Energy Office of Nuclear Energy High Burnup Spent Fuel Data Project, Oak Ridge National Laboratory has performed 4pt-bend tests on a series of 17x17 PWR fuel rods that were discharged with rod average burnups between 50 and 60 GWd/MTU. The rods include four different kinds of fuel cladding: standard Zircaloy-4 (Zirc-4), low-tin (LT) Zirc-4, ZIRLO, and M5. Six rods of each type (combining Zirc-4 and LT Zirc-4 as one type) were tested to failure at room temperature and 200 oC. Three of each type experienced a full-length heat treatment cycle from RT to 400 oC to RT with an 8 hr soak at 400 oC to simulate the expected limiting vacuum drying condition, which potentially induces hydride reorientation and causes some annealing of irradiation damage in the cladding. Overall, the fuel rods show significant capacity to deflect in bending and exhibit non-linear deformation before failing. The calculated failure strains range from 1.5% to 3.8%. The simulated vacuum drying heat treatment had the largest effect on fuel rods with ZIRLO and M5 cladding. In both cases the heat treatment caused increases in the plastic strain at failure, suggesting some annealing of irradiation damage is occurring. The finite element modeling of the 4pt bending test assume the pellet is a collection of pellet wedge pieces. The results for the Zircaloy-4 rods suggest that the pellets are bonded to the cladding and friction controls the relative motion of the pellet wedge pieces.

Cantonwine, Paul↗

DOE OSTI2022

FutureGen VSP

Processed SEGY files from the FutureGen2 project. Data includes 5 categories of OVSP data with 15 lines in each category (all in TWT). VSP data was processed from Sterling Seismic.

FutureGen2↗

DOE OSTI2022