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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Photometric cross-calibration of the SDSS Stripe 82 Standard Stars catalogue with Gaia EDR3, and comparison with Pan-STARRS1, DES, CFIS, and GALEX catalogues

ABSTRACT We extend the SDSS Stripe 82 Standard Stars catalogue with post-2007 SDSS imaging data. This improved version lists averaged SDSS ugriz photometry for nearly a million stars brighter than r ∼ 22 mag. With 2–3 times more measurements per star, random errors are 1.4–1.7 times smaller than in the original catalogue, and about three times smaller than for individual SDSS runs. Random errors in the new catalogue are $\lesssim$0.01 mag for stars brighter than 20.0, 21.0, 21.0, 20.5, and 19.0 mag in u, g, r, i, and z-bands, respectively. We achieve this error threshold by using the Gaia Early Data Release 3 (EDR3) Gmag photometry to derive grey photometric zeropoint corrections, as functions of RA and Declination, for the SDSS catalogue, and use the Gaia BP–RP colour to derive corrections in the ugiz bands, relative to the r-band. The quality of the recalibrated photometry, tested against Pan-STARRS1, DES, CFIS, and GALEX surveys, indicates spatial variations of photometric zeropoints ≦ 0.01 mag (rms), with typical values of 3–7 millimag in the RA, and 1–2 millimag in the Declination directions, except for $\lesssim$6 millimag scatter in the u-band. We also report a few minor photometric problems with other surveys considered here, including a magnitude-dependent ∼0.01 mag bias between 16 ≤ GGaia ≤ 20 in the Gaia EDR3. Our new, publicly available catalogue offers robust calibration of ugriz photometry below 1 per cent level, and will be helpful during the commissioning of the Vera C. Rubin Observatory Legacy Survey of Space and Time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Estimating cluster masses from SDSS multiband images with transfer learning

ABSTRACT The total masses of galaxy clusters characterize many aspects of astrophysics and the underlying cosmology. It is crucial to obtain reliable and accurate mass estimates for numerous galaxy clusters over a wide range of redshifts and mass scales. We present a transfer-learning approach to estimate cluster masses using the ugriz-band images in the SDSS Data Release 12. The target masses are derived from X-ray or SZ measurements that are only available for a small subset of the clusters. We designed a semisupervised deep learning model consisting of two convolutional neural networks. In the first network, a feature extractor is trained to classify the SDSS photometric bands. The second network takes the previously trained features as inputs to estimate their total masses. The training and testing processes in this work depend purely on real observational data. Our algorithm reaches a mean absolute error (MAE) of 0.232 dex on average and 0.214 dex for the best fold. The performance is comparable to that given by redMaPPer, 0.192 dex. We have further applied a joint integrated gradient and class activation mapping method to interpret such a two-step neural network. The performance of our algorithm is likely to improve as the size of training data set increases. This proof-of-concept experiment demonstrates the potential of deep learning in maximizing the scientific return of the current and future large cluster surveys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Generation of Fragment Angular Momentum in Fission

A recent analysis of experimental data [J. Wilson et al., Nature (London) 590, 566 (2021)] found that the angular momenta of nuclear fission fragments are uncorrelated. Based on this finding, the authors concluded that the spins are therefore determined only after scission has occurred. We show here that the nucleon-exchange mechanism, as implemented in the well-established event-by-event fission model freya, while agitating collective rotational modes in which the two spins are highly correlated, nevertheless leads to fragment spins that are largely uncorrelated. We find that this counterexample invalidates the conclusion in [J. Wilson et al.] that uncorrelated spins must necessarily have been generated after scission (a potentious conclusion that would rule out all models that generate the fragment spins prior to scission). Furthermore, it was reported [J. Wilson et al.] that the mass dependence of the average fragment spin has a sawtooth structure. We demonstrate that such a behavior naturally emerges when shell and deformation effects are included in the moments of inertia of the fragments at scission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

The Next Breakthroughs in Neutrino Physics (LDRD Final Report)

The neutrino is an important fundamental particle, one of the building blocks of the universe. Abetter understanding of the neutrino will answer questions regarding the origin of mass, the matter and anti-matter asymmetry of the universe, and the nature of dark matter. The nation's research community has recognized that the answers to these and other questions are within reach and has assigned neutrino experiments the highest priority for both nuclear and particle physics programs. We are investigating three aspects of neutrino experimentation: detection techniques, target materials, and data analysis. Our efforts targeted multiple applications including experiments to measure neutrinoless double-beta-decay, neutrino-oscillation and neutrino mass. Our research objectives include (1) developing approaches that make detectors scalable to larger sizes and insensitive to background signals; (2) increasing the signal strength and reducing noise from targets; and (3) efficiently distinguishing background noise from signals during analysis. In this LDRD we have advanced all of these areas. We have demonstrated the scale up of a metal organic framework that can adsorb xenon directly from the air that will allow for large neutrino detectors made from xenon. We have studied the use of Cherenkov radiation to reduce the signal backgrounds. We demonstrated the cracking of hydrogen to make atomic tritium for neutrino mass measurements, and lastly we demonstrated the benefits of machine learning techniques to improve signal to noise during analysis

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

RAVEN regression tests' description

Regression tests for the Python RAVEN framework are found in raven/tests/framework. There is a hierarchy of folders with tests collected by similar testing. Every test is described in a special XML node (< TestInfo >) within the < Simulation > block.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2020

Integrated Risk-Informed Condition Based Maintenance Capability and Automated Platform: Technical Report 1

Due to continuing global energy market trends, driven heavily by the abundant preserves of natural gas, there is an immediate need to reduce costs associated with operation and maintenance (O&M) for the current domestic nuclear power industry and for future reactor developments. This is to ensure that nuclear power generation remains an economically competitive and viable option in the energy market. O&M costs include labor-intensive preventive maintenance (PM) programs, which involve manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets, irrespective of their condition. This has resulted in an expensive, labor-centric business model to achieve high capacity factors. Fortunately, there are technologies (advanced sensors, data analytics, and risk assessment methodologies) that can enable the transition from a labor-centric business model to a technology-centric business model. The technology-centric business model will result in a significant reduction of PM activities, laying the foundation for real-time condition assessment of plant assets, reducing overall labor and part costs. To enable this transition, PKMJ Technical Services LLC is partnering with the U.S. Department of Energy’s Idaho National Laboratory (operated by the Battelle Energy Alliance, LLC) and the Public Services Enterprise Group (PSEG) Nuclear, LLC in the Integrated Risk-Informed Condition-Based Maintenance Capability and Automated Platform Project. In this report, the configuration of a digital cloud platform using Microsoft Azure is discussed, data from the PSEG Salem Nuclear Generating Station Units 1 & 2 are imported into a digital cloud platform, and the data is used for an evaluation of several key areas: cost impact analysis, risk-informed model development, and preventive maintenance strategy optimization. First, the cost impact analysis reviews which plant assets are potential good candidates for condition-based monitoring. Next, INL utilized the data in their local environment to develop the risk-informed model; which provides estimates of failure rates and probability of failures of assets based upon their past performance. The developed model is performed on assets selected from the cost impact analysis. Lastly, engineers assess the preventive maintenance strategy for the selected assets at PSEG against maintenance strategies in the nuclear industry for similar assets to potentially identify acceptable justification for the extension of current maintenance frequencies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Radioisotope Identification with List-Mode Gamma-Ray Data

This work explores the potential of utilizing temporal data from gamma-ray detectors, known as list-mode data, to enhance radioisotope identification. Traditional identification methods, which rely on full gamma-ray spectrum analysis, often require long dwell times and struggle with spectra containing similarly spaced spectral peaks. We hypothesize that by leveraging the probabilistic nature of nuclear decay and the time-encoded information from decay sequences and interactions with surrounding materials, we can improve classification accuracy over static spectral analysis. This research examines the temporal content of list-mode data through exploratory data analysis via correlation discovery and qualitative distribution analysis. Additionally, we propose a probabilistic classification model that can utilize spectral data, temporal data, or both to determine if the incorporation of temporal information improves radioisotope identification. Our findings suggest that the temporal information present in list-mode gamma-ray data has merit and should be further investigated to develop more robust and optimal methods for utilizing this temporal information in applications requiring radioisotope identification.

List-mode data↗
DOE OSTI2024

Examining Graphite Degradation in Molten Salt Environments: A Chemical, Physical, and Material Analysis

Molten-salt reactors (MSRs) are Generation IV nuclear reactors that use liquid salt as a coolant and/or fuel. In several MSR designs, graphite serves as a moderator and/or reflector. However, due to limited experimental data and operational experience, our understanding of graphite behavior in molten salt environments remains incomplete. This report aims to identify the degradation mechanisms of nuclear graphite in MSRs, detail the mechanisms of each factor, and provide an initial assessment of their impact on the structural integrity of graphite components. This assessment is based on an extensive literature review and insights from subject matter experts. Furthermore, given the limited data, a modeling strategy using existing Grizzly software is proposed for a more thorough analysis where appropriate. Additionally, it presents mitigation strategies where applicable. The report covers physical degradation mechanisms such as infiltration, erosion, and abrasion, as well as chemical degradation mechanisms including fluorination, intercalation, corrosion, and oxidation. Molten salt can infiltrate the porous structure of graphite, leading to several detrimental effects. Entrapment of fissile products within the graphite pores can cause radiation damage and could pose challenges in the handling and disposal of contaminated components. The differential thermal expansion between the infiltrated salt and graphite, along with internal stress from pressurized molten salt and volumetric heating, can compromise the structural integrity of graphite. To mitigate these effects, employing ultra-fine graphite grades and applying sealants and coatings are effective strategies. A computational model based on coupled solid mechanics and heat transfer phenomena could be used to predict the internal stresses using Grizzly software. In pebble-bed MSRs, graphite fuel pebbles can cause abrasion against reactor components due to friction and wear. The severity of wear is influenced by various factors such as temperature, environment, and the presence of lubricants. Tribological studies reveal that higher temperatures and molten salt environments, such as FLiBe, significantly reduce wear rates compared to dry conditions. Additionally, the chemical composition of the salt can further optimize graphite's tribological performance. Long-term wear effects can be modeled by incorporating surface defects into the geometry and predict stresses under thermal and radiation effects using Grizzly software. Chemical degradation of graphite in a molten salt environment can occur through fluorination and intercalation. Fluorination can occur via replacement of hydrogen or oxygen atoms, or at the active sites, but does not cause structural degradation. Intercalation, on the other hand, can lead to exfoliation, where layers of graphite separate and peel away, damaging the graphite. Protective coatings can enhance graphite's resistance to intercalation. Graphite generally exhibits good chemical stability in molten salt environments, though it can corrode under specific conditions, particularly in the presence of impurities or oxidants. Studies have shown that protective coatings, such as plasma-sprayed partially stabilized zirconia (PSZ), can effectively prevent such degradation. Corrosion behavior varies significantly with different graphite grades and coating applications, underscoring the need for detailed studies on uncoated and coated graphite to understand and mitigate corrosion mechanisms in MSRs. Research indicates that the presence of oxidants and impurities can accelerate graphite degradation in molten salts, making it essential to explore acceptable impurity limits. Oxidation is another critical degradation mechanism, leading to weight loss and structural damage due to the formation of CO and CO 2 from the reaction of carbon atoms with oxygen. This process creates new porosity and compromises graphite's integrity. While extensive research on graphite oxidation has been conducted for gas-cooled reactors, studies specific to MSRs are limited. Findings from the coal industry suggest that molten alkali metal salts can significantly accelerate graphite oxidation, a hypothesis worth exploring for fluoride salts in MSRs. Understanding oxidation behavior in MSRs is vital for developing protective measures. The analysis of post-irradiated graphite from the MSRE experiment demonstrated exceptional chemical compatibility with molten fluoride salt, suggesting that the extent of chemical attack on graphite largely depends on the salt's infiltration capability. Therefore, the use of ultra-fine grade graphite could help mitigate chemical degradation effects. Existing oxidation modeling capabilities in Grizzly, which use reaction-diffusion equations to model graphite-air interactions, could be adapted to simulate the chemical degradation effects of graphite in molten salt environments.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Techno-Economic Analysis of Product Diversification Options for Sustainability of the Monticello and Prairie Island Nuclear Power Plants

The objective of this work was to perform techno-economic analysis (TEA) of hybrid options that could be integrated with light water reactor (LWR) nuclear power plants (NPPs) in order to improve the viability and sustainability of existing LWRs through product diversification by utilizing nuclear energy not only to produce grid electricity but to also produce carbon free products such as hydrogen, ammonia or synthetic fuels. Much of the analysis herein could be generally applied to any LWR (high temperature steam electrolysis- HTSE design), but hydrogen demand and the optimization of the HTSE was completed with specific collaboration and data from Xcel Energy’s Prairie Island and Monticello nuclear generating stations and the surrounding market and logistics potential in the greater Minneapolis region. Xcel Energy has set aggressive goals with regards to decarbonization, including an 80% reduction in CO 2 emissions from 2005 levels, by 2030 and 100% carbon free by 2050. Other TEAs regarding hydrogen production with nuclear energy have been completed in collaboration with other utility companies previously. Besides being the first TEA in this regard specific to Xcel Energy and the surrounding markets and logistics, this TEA adds to the previously completed TEAs by providing the most up to date and state of the art analysis and optimization of the hydrogen production plant integrated with nuclear power (NPP-HTSE).

08 HYDROGEN↗
DOE OSTI2022

Integrated Operations for Nuclear Business Operation Model Analysis and Industry Validation

The purpose of this report is to refine and analyze five work reduction opportunities first presented in INL/EXT-21-64134, Process for Significant Nuclear Work Function Innovation Based on Integrated Operations Concepts. This report seeks to further refine and analyze five work reduction opportunities first presented in the original report. Researchers selected five work reduction opportunities from the full Integrated Operations for Nuclear (ION) suite. A selected group of utilities then verified details and inputs from the original report. Categories for verification included capital cost, technology requirements, and savings. Researchers then modeled the data points and data ranges using probabilistic analysis which predicts the likelihood of positive or negative net present value. Research results show four out of the five work reduction opportunities have a greater than fifty percent chance of a positive net present value outcome when analyzed independently. When the five work reduction opportunities are grouped and analyzed together the model indicates a sixty percent chance that the outcome of all five taken together will be positive. The nuclear industry should interpret these results as encouraging. In line with the ION model, positive financial analysis supports the investment of capital dollars into existing nuclear power plants along the ION model. Implementation of the five work reduction opportunities in this report is likely to result in substantive long-term savings for the owners and operators of domestic nuclear power plants.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Can we safely go to 200 °C? An integrated approach to assessing impacts to the engineered barrier system in a high-temperature repository

Abstract. This presentation gives on overview of the complex thermo-hydro-mechanical-chemical (THMC) processes occurring during the disposal of heat-producing high-level radioactive waste in geologic repositories. A specific focus is on the role of compacted bentonite, which is commonly used as an engineered backfill material for emplacement tunnels because of its low permeability, high swelling pressure, and radionuclide retention capacity. Laboratory and field tests integrated with THMC modeling have provided an effective way to deepen our understanding of temperature-related perturbations in the engineered barrier system; however, most of this work has been conducted for maximum temperatures around 100 ∘C. In contrast, some international disposal programs have recently started investigations to understand whether local temperatures in the bentonite of up to 200 ∘C could be tolerated with no significant changes to safety relevant properties. Raising the maximum temperature is attractive for economical and safety reasons but faces the challenge of exposing the bentonite to significant temperature increases. Strong thermal gradients may induce complex moisture transport processes while geochemical processes, such as cementation and perhaps also illitization effects may occur, all of which could strongly affect the bentonite and near-field rock properties. Here, we present initial investigations of repository behavior exposed to strongly elevated temperatures. We will start discussing our current knowledge base for temperature effects in repositories exposed to a maximum temperature of 100 ∘C, based on data and related modeling analysis from a large heater experiment conducted for over 18 years in the Grimsel Test Site in Switzerland. We then show results from coupled THMC simulations of a nuclear waste repository in a clay formation exposed to a maximum temperature of 200 ∘C. We also explore preliminary data from a bench-scale laboratory mock-up experiment, which was designed to represent the strong THMC gradients occurring in a “hot” repository, and we finally touch on a full-scale field heater test to be conducted soon in the Grimsel Test Site underground research laboratory in Switzerland (referred to as HotBENT).

Birkholzer, Jens T. (ORCID:0000000279891912)↗
DOE OSTI2020

Weak neutral current axial form factor using $ \left(\overline{\nu}\right)\nu $-nucleon scattering and lattice QCD inputs

We present a determination of the neutral current weak axial charge $G$ $^{Z}_{A}$(0) = –0.654(3) stat (5) sys using the strange quark axial charge $G$ $^{s}_{A}$(0) calculated with lattice QCD. We then perform a phenomenological analysis, where we combine the strange quark electromagnetic form factor from lattice QCD with (anti)neutrino-nucleon scattering differential cross section from MiniBooNE experiments in a momentum transfer region 0.24 ≲ Q 2 ≲ 0.71 GeV 2 to determine the neutral current weak axial form factor $G$ $^{Z}_{A}$(Q 2 ) in the range of 0 ≲ Q 2 ≤ 1 GeV 2 . This yields a phenomenological value of $G$ $^{Z}_{A}$(0) = –0.687(89)stat(40)sys. The value of $G$ $^{Z}_{A}$(0) constrained by the lattice QCD calculation of $G$ $^{s}_{A}$(0), when compared to its phenomenological determination, provides a significant improvement in precision and accuracy and can be used to provide a constraint on the fit to $G$ $^{Z}_{A}$(Q 2 ) for Q 2 > 0. This constrained fit leads to an unambiguous determination of (anti)neutrino-nucleon neutral current elastic scattering differential cross section near Q 2 = 0 and can play an important role in numerically isolating nuclear effects in this region. We show a consistent description of $G$ $^{Z}_{A}$(Q 2 ) obtained from the (anti)neutrino-nucleon scattering cross section data requires a nonzero contribution of the strange quark electromagnetic form factor. We demonstrate the robustness of our analysis by providing a post-diction of the BNL E734 experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Beowulf v2.5.3 User Guide: Revision 5

This document is a user guide for the Beowulf (rebranding of Watchmen) tool. It describes how operators can access and utilize the application's functionality. Beowulf is a research software application developed by Pacific Northwest National Laboratory (PNNL) that incorporates the scientific and operational expertise for reviewing data from treaty monitoring radionuclide stations. These stations are part of a worldwide network to monitor for nuclear explosions, and the data they produce are critical to make the determination of whether a sample is from a nuclear explosion or some other source (i.e., nuclear reactor or medical isotope production facility). Stations deliver their measurements and system status to the International Monitoring System (IMS), which forwards it via email to all subscribers. Beowulf is capable of processing data from several radioxenon station types and development is in progress on a solution for particulate stations. Screening of data in Beowulf may be done by a number of different users such as radionuclide analysts, evaluators, and data quality experts. This guide is provided to assist those users in navigating the application. The term Beowulf is used generically throughout this document to refer to any of the various components in the software application. The user interface that is viewed with a web browser is the primary focus of this user guide. Other components include a database to store measurements and state of health (SOH) data; and the data loader that monitors incoming emails, parses the data, populates the database, does the initial analysis, and routes data for review.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

FRAM Isotopic Analysis of High-Resolution CZT Data [Slides]

Abstract: H3D Inc. introduced a new type of CZT detectors with resolution several times better than that of a typical CZT; The current FRAM v.6.1 can analyze uranium spectra with minimal modification of the parameter sets and get acceptable results; The exceptionally large high-energy tail of the peaks makes it difficult to achieve good uranium results and impossible to successfully analyze plutonium spectra; The FRAM code needed to be modified to better fit the high-energy tail of the peaks to analyze plutonium data and to get better uranium analysis results.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗