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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Connecting Nuclear Structure to Stellar Astrophysics: Neutron Skin in Tin Isotopes

The first observation of a neutron star merger by the LIGO-Virgo collaboration in 2017 highlights the need to improve our fundamental understanding of the equation of state of dense, neutron rich matter. The origin of heavy elements in the r-process and the structure of neutron stars are governed by the properties of neutron rich matter, for which experimental data is limited. Further analysis of this historic event and all future neutron star mergers relies on constraining the nuclear equation of state with experimental observables. We propose a novel method for systematically studying the evolution of the neutron skin in stable tin isotopes, by measuring the low-energy nuclear dipole strength over the broadest possible range of neutron-to-proton ratios in a single element. Nuclear resonance fluorescence with 100% linearly polarized photons from the High Intensity Gamma-ray Source (HIGS) facility was used to selectively measure the E1 photoabsorption strength of 112 Sn and 124 Sn at excitation energies from 3.5 MeV up to neutron separation, where the Pygmy Dipole Resonance dominates. The dipole polarizabilities of 112 Sn and 124 Sn were measured to be 9.03 ± 0.23 fm 3 and 9.11 ± 0.24 fm 3 , respectively. These uniquely systematically consistent measurements provide highly accurate experimental data for improving microscopic nuclear models as well as calculations of astrophysical nucleosynthesis and neutron star structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2025

C 12 ( n , n 1 ′ γ ) partial γ -ray cross section measured using the GENESIS array

Improved neutron inelastic scattering cross sections have repeatedly been identified as a top priority nuclear data need, important for basic science and a range of applications in nuclear energy, stockpile stewardship, and proliferation detection. For the C 12 ( n , n ′ γ ) reaction in particular, recent measurements have unveiled some structural discrepancies, demonstrating incongruities among themselves and in relation to the ENDF/B-VIII.0 nuclear data evaluation. To help resolve these disagreements, a measurement was performed at the 88-Inch Cyclotron at Lawrence Berkeley National Laboratory using a broad-spectrum neutron beam and a 99.8% pure natural carbon target. The Gamma Energy Neutron Energy Spectrometer for Inelastic Scattering (GENESIS) was employed to measure energy-differential γ -ray emission spectra as a function of incident neutron energy in the energy range of 5.5 to 16.7 MeV. The C 12 partial γ -ray cross sections were extracted at 63 ∘ , 122 . 5 ∘ , and 150 ∘ with respect to the incoming neutron beam and integrated using angular distribution data available in the literature. The data show agreement with a recent literature measurement and evaluation from 11 to 15 MeV, but indicate a larger cross section for incident neutron energies between 5.5 and 8.5 MeV. The measured relative angular distributions are also reported and were found to agree with evaluation. Published by the American Physical Society 2025

Gordon, J. M. (ORCID:0009000789886897)↗
DOE OSTI2021

Structural Health Monitoring of Microreactor Safety Systems Using Convolutional Neural Networks

Microreactors, a class of modular reactors with net power output of less than 20 MWth, have innovative applications in nuclear and nonnuclear industries due to their portability, reliability, resilience, and high capacity factors. In order to operate microreactors on a wider scale, it is essential to bring down maintenance life-cycle costs while ensuring the integrity of operating such systems. Autonomous operations in microreactors using augmented digital-twin (DT) technology can serve as a cost-effective solution by increasing awareness about the system’s health. Structural health monitoring (SHM) is a key component of nuclear DT frameworks. Artificial neural networks can be beneficial to detect degradation in the nuclear safety systems, such as piping equipment systems, by monitoring the sensor data obtained from the plant and its corresponding structures, systems and components. In this report, an SHM methodology is presented which uses convolutional neural networks to determine degraded locations and their corresponding degradation-severity levels at various locations of nuclear piping equipment systems. A simple pipe system, subjected to seismic loads, is selected to design the post-hazard SHM framework. The effectiveness of the proposed SHM methodology is demonstrated by obtaining high accuracy in detecting degraded locations as well as the severity levels.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

F2 extraction from Inclusive Cross Section Data at Large Bjorken x

While we have made significant progress in probing nuclear structure at low Bjorken x and high Q 2 , we still have gaps in our knowledge regarding the high Bjorken x and intermediate Q 2 kinematic region. This is not an accident, but instead due to the diffculty in assessing the non-perturbative region of nuclear physics. For this reason, the E12-10-002 experiment measured the H(e,e') and D(e,e') inclusive cross section in the resonance region. This will allow us to study both perturbative and non-perturbative physics in a kinematic region that lacks precision measurements. Measurements were made up to a Bjorken x of 0.99 and Q 2 up to 17 GeV 2 . These measurements are made using the independent HMS and SHMS spectrometers at the upgraded beam energy of 11 GeV in Hall C at Jefferson Lab. Further, we extract F 2 structure functions from the measured data and study the effect of their inclusion in a global Parton Distribution Function analysis.

Araiza Gonzalez, Fernando↗
DOE OSTI2025

Gamma Decay of the 154 Sm Isovector Giant Dipole Resonance: Smekal-Raman Scattering as a Novel Probe of Nuclear Ground-State Deformation

𝛾 decays of the isovector giant dipole resonance (IVGDR) of the deformed nucleus 154 Sm were measured using 2$^{+}_{1}$-Smekal-Raman and elastic scattering of linearly polarized, quasimonochromatic photon beams. The two scattering processes were disentangled through their distinct angular distributions. Their branching ratio and cross sections were determined at six excitation energies covering the 154 Sm IVGDR. Both agree with the predictions of the geometrical model for the IVGDR and confirm 𝛾 decay as an observable sensitive to the structure of the resonance. Consequently, the data place strong constraints on the nuclear shape, including the degree of triaxiality. The derived 154 Sm shape parameters 𝛽 = 0.2925⁢(25) and 𝛾 = 5.0⁢(15)° agree well with other measurements and recent Monte Carlo shell-model calculations.

150 ≤ A ≤ 189↗
DOE OSTI2020

Extended experimental inferential structure determination method in determining the structural ensembles of disordered protein states

Proteins with intrinsic or unfolded state disorder comprise a new frontier in structural biology, requiring the characterization of diverse and dynamic structural ensembles. Here we introduce a comprehensive Bayesian framework, the Extended Experimental Inferential Structure Determination (X-EISD) method, which calculates the maximum log-likelihood of a disordered protein ensemble. X-EISD accounts for the uncertainties of a range of experimental data and back-calculation models from structures, including NMR chemical shifts, J-couplings, Nuclear Overhauser Effects (NOEs), paramagnetic relaxation enhancements (PREs), residual dipolar couplings (RDCs), hydrodynamic radii (R h ), single molecule fluorescence Förster resonance energy transfer (smFRET) and small angle X-ray scattering (SAXS). We apply X-EISD to the joint optimization against experimental data for the unfolded drkN SH3 domain and find that combining a local data type, such as chemical shifts or J-couplings, paired with long-ranged restraints such as NOEs, PREs or smFRET, yields structural ensembles in good agreement with all other data types if combined with representative IDP conformers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Nuclear Sciences References (NSR)

Nuclear Science literature database, containing about 250,000 articles covering more than 120 years of nuclear science research. Approximately 80 journals are routinely scanned for relevant articles; it also includes PhD thesis and private communications. Each article in NSR is assigned a distinctive 8-character primary key, known as ‘key number’, and a set of keywords that briefly describe the article's content. These keywords follow strict rules since they are used in web search forms. Typically, an article is incorporated in NSR within a few weeks after its web publication. About 4,000 articles are added per year. All the compilation effort is supported by the US Nuclear Data Program and coordinated by the National Nuclear Data Center at Brookhaven National Laboratory, which is also responsible for its web dissemination.

Basic Nuclear Research, Applied Nuclear Technologi↗
DOE OSTI2020

Key Nuclear Data Impacting Reactivity in Advanced Reactors

Advanced reactor concepts currently being developed throughout the industry are significantly different from light water reactor (LWR) designs with respect to geometry, materials, and operating conditions, and consequently, with respect to their reactor physics behavior. Given the limited operating experience with non-LWRs, the accurate simulation of reactor physics and the quantification of associated uncertainties are critical for ensuring that advanced reactor concepts operate within the appropriate safety margins. Nuclear data are a major source of input uncertainties in reactor physics analysis. As part of an ongoing project at Oak Ridge National Laboratory (ORNL), the effects of nuclear data uncertainties on key figures of merit associated with advanced reactor safety are being assessed for selected advanced reactor technologies. Key nuclear data relevant for reactor safety analysis for each selected advanced reactor technology were identified, and their impact on important key figures of merit was assessed. Available advanced reactor specifications were reviewed, results from studies performed at ORNL and other research institutions were consulted, and available evaluated nuclear data libraries were analyzed. This report summarizes the key nuclear data for nuclides in the fuel, as well as other significant data, including scattering and neutron capture in various materials for the moderator, coolant, and structure of the considered advanced reactors. For the considered advanced reactors that use low-enriched uranium (LEU) fuel, results from LWR studies provided insight into relevant nuclear data given the lack of available studies specifically addressing these new systems. The major nominal missing data that were identified consist of thermal scattering data and 135m Xe cross section data for molten salt reactor (MSR) analysis. The identified major gaps with respect to nuclear data uncertainties are missing uncertainties of thermal scattering data for high temperature gas-cooled reactors and moderated MSR systems, and incomplete uncertainties on angular distributions in particular for fast spectrum systems, such as sodiumcooled fast reactors, fast molten salt reactors, and heat pipe reactors. Furthermore, it was found that special attention should be paid to cross section and uncertainty differences between different evaluated nuclear data library releases, because significant differences in nuclear data that can lead to major differences in reactivity calculations were found, even for well-known nuclides.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

First observation of a high-𝐾 band structure in 162 Er and implications in the context of the identical bands phenomenon

The first ever identification of a high-𝐾 band structure in 162 Er is reported. Based on a 𝐾 𝜋 = 7 (−) isomer, it is found to be identical in nature to the corresponding 𝐾 𝜋 = 7 − sequence in 164 Er up to its highest observed spin. Furthermore, the phenomenon of identical high-K bands built on a two-quasiparticle configuration in an isotopic chain is reported here for the first time. While this is a notable addition to the systematics of known identical bands in nuclei at normal deformation, a satisfactory global understanding of the phenomenon remains elusive.

150 ≤ A ≤ 189↗
DOE OSTI2021

Dipole response in Te 128 , 130 below the neutron threshold

Numerous studies of the ground-state decay of the pygmy dipole resonance (PDR) have been carried out in the past. However, data on the decay of the PDR to low-lying excited states is still very scarce due to limitations of the sensitivity to weak branching transitions of experimental setups. Here, we present a detailed examination of the low-energy dipole response of 128 Te and 130 Te below their neutron separation thresholds of 8.8 and 8.5 MeV, respectively. Photonuclear reactions with the subsequent γ-ray spectroscopy of the decay channel with continuous-energy bremsstrahlung at varying endpoint energies and linearly polarized quasimonochromatic γ-ray beams with energies ranging from 2.7 to 8.9 MeV in steps of roughly 250 keV were used for probing the decay behavior of the low-energy dipole response in 128Te and 130Te. In addition, (γ,γ' γ") reactions were used to study the population of low-lying states of 128 Te. Spin-parity quantum numbers and reduced transition probabilities are determined for individual photo-excited states. The analysis of average decay properties for nuclear levels in narrow excitation-energy bins enable the extraction of photoabsorption cross sections, average branching ratios to the $2$$^{+}_{1}$ state, and the distinction between E1 and M1 transitions to the ground state and to the $2$$^{+}_{1}$ state accounting for resolved and unresolved transitions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

From Data to Knowledge: A Graph-Based Reliability Approach to Assess System Health

With the goal of maximizing plant reliability and availability, complex systems such as nuclear power plants continuously monitor and record the performance and the health status of many components, assets, and systems. Such data may take the form of online monitoring data, condition reports, and maintenance reports and it carries the potential to provide system engineers with insights into anomalous behaviors or degradation trends as well as the possible causes behind them and to predict their direct consequences. The analysis of such data poses however few challenges. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly tackles these challenges, and it focuses on the integration of all these data elements in order to assist plant system engineers in analyzing component, assets, and systems performances and optimize maintenance activities. This is performed by 1) extracting knowledge from textual data via technical language processing methods, and 2) quantifying system, asset, and component health from numeric condition-based data. We rely on model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Numeric and textual data elements are then associated with an MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Knowledge Graph Approach to Analyze Systems and Assets Health

Nuclear power plants collect large amounts of equipment reliability data elements that contain information on the statuses of component, assets, and systems. All these data elements precisely record asset and system performance and health throughout the lifecycle of those assets and systems. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers or databases), others are conceptual in nature (i.e., data elements come in different formats, numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). This paper directly focuses on the integration of numeric and textual data elements in order to assist plant system engineers in analyzing equipment reliability data. This task begins with preprocessing the data by extracting knowledge from textual data via natural language processing methods and quantifying system, asset, and component health based on numeric data. We then employed model-based system engineering (MBSE) models of systems and assets to identify their architecture and functional (i.e., cause and effect) relations. Data elements were then associated with a single MBSE graph element, based on their nature. This bonding of MBSE models and data elements constitutes a first-of-its-kind knowledge graph of a nuclear power plants system, with data elements being organized in a structured manner that enables system engineers to identify cause-effect trends in data elements and carry out appropriate actions in response.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

United States Nuclear Power Reactor Used Nuclear Fuel Database and Applications

The Unified Database (UDB) within STANDARDS serves as the foundational data infrastructure for managing the United States' spent nuclear fuel inventory of 315,111 discharged assemblies totaling 91,036 metric tons of heavy metal. The database organizes this complex inventory through over 200 interconnected tables structured into eight primary attribute categories, supporting integrated analyses across storage, transportation, and disposal domains. Data enters the UDB through the GC-859 Nuclear Fuel Data Survey, which transitioned to web-based collection in 2023, improving data quality through real-time validation. The UDB enables automated generation of input files for nuclear safety analyses, reducing preparation time from weeks to hours while maintaining traceability. Applications include national inventory reporting, Certificate of Compliance assessments, and facility optimization. The three-tier distribution model balances accessibility with security requirements for federal agencies, national laboratories, and research organizations. The UDB provides essential data infrastructure as spent fuel management transitions from site-specific to integrated national campaigns.

Stefanovic, Peter↗
DOE OSTI2025

Evaluations for medium- and high-mass nuclei for FUSION applications

There is a renewed attention to nuclear fusion as a commercial source of carbon-free energy, however there are many scientific needs that must be addressed to enable the future success of fusion as an economical energy option. Among these is the proper description of the impact of radiation produced in the fusion vessel chamber and all other components of the reactor. In this work we will focus on the nuclear data needs to describe the interaction between primary and secondary neutron radiation and the medium- and high-mass nuclei commonly present in structural (such as stainless steel) and superconducting (e.g., electromagnets) materials.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

The coordination properties and ionic radius of actinium: A 120-year-old enigma

Actinium is an elusive element with untamed properties and represents a peculiar case in the periodic table, as its isotopes are all radioactive, the longest-lived one having only a 22-year half-life, and the availability of actinium isotopes remains very low (microgram level, at best), hindering research on its compounds. Despite being a natural element discovered more than 120 years ago, and despite an increasing interest in using one of its isotopes ( 225 Ac) for highly efficient cancer therapies, the chemistry of actinium is still largely unknown relative to other elements. Since Ac is the first element of the actinide series, it is accepted that its ion, Ac 3+ , is the most voluminous trivalent cation of the periodic table. However, the structural data available on Ac 3+ compounds are scarce and have mainly been collected in the 1940-1960's, when actinide chemistry was still in its infancy, and have not been put in perspective with the advances in the chemistry of other elements, making it difficult to accurately evaluate its actual size and coordination chemistry. Here, we review progress made on the chemistry of lanthanides and actinides and reevaluate the structural data published on Ac 3+ since the era of the Manhattan Project. The data are combined across different spectroscopic and characterization methods and presented in the context of periodic trends. When considering crystallographic data, solution chemistry results, and the nuclear properties of actinium isotopes, it appears that some structural parameters ascribed to the Ac 3+ ion may have been overestimated. This review can guide researchers interested in actinide sciences and those who are pursuing the development of actinium-based radiotherapies, from isotope production to clinical trials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗