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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

High-resolution infrared spectroscopy of jet cooled CH 2 Br radicals: The symmetric CH stretch manifold and absence of nuclear spin cooling

Direct laser absorption of a slit supersonic discharge expansion provides the first high-resolution spectroscopic results on the symmetric CH stretch excitation (ν 1 ) of the bromomethyl (CH 2 Br) radical in the ground electronic state. Narrowband (<1 MHz) mid-infrared radiation is produced by difference-frequency generation of two visible laser beams, with the open shell halohydrocarbon radical generated by electron dissociative attachment of CH 2 Br 2 in a discharge and rapidly cooled to T rot = 18 ± 1 K in the subsequent slit-jet supersonic expansion. A rovibrational structure in the radical spectrum is fully resolved, as well as additional splittings due to spin-rotation effects and 79 Br/ 81 Br isotopologues in natural abundance. Spectroscopic constants and band origins are determined by fitting the transition frequencies to a non-rigid Watson Hamiltonian, yielding results consistent with a vibrationally averaged planar radical and an unpaired electron in the out-of-plane p π orbital. Additionally, extensive satellite band structure from a vibrational hot band is observed and analyzed. The hot band data is compared to CFOUR/VPT2 (CCSD(T)cc-pVQZ) ab initio anharmonic predictions of the vibration rotation alpha matrix, which permits unambiguous assignment to CH 2 symmetric-stretch excitation built on the singly excited CH 2 out-of-plane bending mode (ν 1 + ν 4 ← ν 4 ). Longitudinal cooling of the Doppler width in the slit-jet expansion geometry also reveals partially resolved hyperfine structure on transitions out of the lowest angular momentum states in excellent agreement with predictions based on microwave studies. High level ab initio MOLPRO calculations [CCSD(T)-f12b/VnZ-f12 (n = 3, 4, CBS)] are also performed with explicitly correlated f12 electron methods for the out-of-plane CH 2 bending mode over the halogen series CH 2 X (X = F, Cl, Br, I), which clearly reveals a non-planar geometry for X = F (with a ΔE ≈ 0.3 kcal/mol barrier) and yet planar equilibrium geometries for X = Cl, Br, and I. Finally, a detailed Boltzmann analysis of the transition intensities provides support for negligible collisional equilibration of the entangled H atom nuclear spin states on the few hundred microsecond time scale and high collision densities of a slit supersonic expansion.

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

Alkali-silica reaction expansion model for confined concrete with stress-dependency and casting direction anisotropy

Alkali-silica reaction (ASR) is a deleterious chemical reaction between alkali hydroxyl ions and types of silica found in some aggregates of concrete. Owners and regulators of nuclear power plants aim to ensure the safety of the concrete structures with optimal maintenance strategies. A new model was developed for predicting the expansion of concrete structures affected by alkali-silica reaction. The model includes a novel combination of existing models as an alkali-silica reaction advancement model, a casting direction anisotropic expansion model, a stress-dependent anisotropic expansion model, and a material property evolution model dependent on the degree of ASR expansion. The model parameters were calibrated based on existing literature data and data generated by previous efforts of this study. The calibrated model was then validated with the experiments carried out in previous efforts of this study. The model was shown to accurately predict the ASR-expansion of large-scale reinforced concrete specimens with confinement.

36 MATERIALS SCIENCE↗
DOE OSTI2024

STRUCTURAL MODELING TO SUPPORT POST-YIELD ACCEPTANCE CRITERIA FOR SPENT NUCLEAR FUEL CLADDING

Spent nuclear fuel (SNF) is evaluated for structural failure during storage and transportation scenarios. The U.S. Department of Energy’s Spent Fuel and Waste Science and Technology (SFWST) program has sponsored significant research in quantifying mechanical loads on SNF during storage and transportation scenarios using experimental and modeling methods. The SFWST program has also performed significant research on measuring the mechanical behavior of irradiated SNF as defueled cladding segments and cladding with fuel pellets to measure composite behavior. This paper considers some of the key material data from the Sibling Pin testing and uses structural modeling and analysis methods that have been informed by testing to consider post-yield acceptance criteria for SNF cladding structural analysis. Test data published by Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory (PNNL) are the foundation for informing the material behavior of the models developed in this study. In particular, four-point bend (4PB) tests of fueled and defueled cladding segments provide significant information about the bending failure mode of SNF. ORNL’s 4PB test data is on fueled cladding segments, so the composite behavior of SNF is demonstrated. This paper describes PNNL’s coincident beam model that was developed to approximate the composite behavior of SNF. This paper also presents PNNL’s structural dynamic finite element models of a cask tip-over scenario, which is predicted to cause the strongest mechanical loads on SNF of all postulated storage and transportation scenarios. SNF bending loads predicted in the cask tip-over scenario and cladding acceptance criteria beyond yield are considered, with justification based on the Sibling Pin test data. ASME Boiler and Pressure Vessel code stress intensity limits are also considered. The ultimate goal of this work is to aid in the justification of structural acceptance criteria for SNF cladding beyond the cladding’s irradiated yield strength for use in structural analysis of all storage and transportation scenarios.

Klymyshyn, Nicholas A.↗
DOE OSTI2021

Ducted Assembly Steady-State Heat Transfer Software (DASSH)

The Ducted Assembly Steady-State Heat transfer software (DASSH) is being developed at Argonne National Laboratory to perform a steady-state thermal fluids calculation to determine the coolant flow and temperature distribution for a hexagonal reactor core configuration with ducted assemblies. DASSH is intended to be used in the early stages of the reactor design process when assembly components can be undefined or are undergoing considerable design changes. DASSH provides a rapid assessment of the temperature and flow distribution to allow quick characterization of the system and identification of problem areas. This document is a guide for DASSH users. It summarizes the core capabilities of DASSH and highlights differences between DASSH and SE2-ANL. Instructions for obtaining and installing DASSH are provided along with some guidelines and recommendations for newer Python users. It covers the structure of the input file, provides directions for running DASSH, describes the various output files produced by the code, and provides instructions for visualizing data. Examples demonstrating various DASSH options are included. DASSH is under active development, so it is anticipated that this guide will grow and evolve as the code is updated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Quantum Choreography of the Nucleus: Rotations, Vibrations, and Emergent Structure

Abstract Nuclei are complex many-body quantum systems where interactions of the neutrons and protons via the strong, the weak, and the electromagnetic forces lead to the emergence of simple patterns of energy states that have been described by various theoretical approaches. One of the goals of all the theoretical models is the development of a universal theory that can be applied across the entire chart of nuclides. Significant progress has been made by experiments as well as the increasing sophistication of models, but a universal theory has yet to be established. A recent review of nuclei in the Z = 50–82 region of the chart of nuclides has analyzed all the available compiled data from several decades of studies towards a clarification of the low-lying structure of nuclei. Other reviews have reported and explained the emergence of multiple different shapes in nuclei at somewhat higher excitation energies than the ground state. Some have challenged the interpretation of the first excited K π = 0 + band as a vibration of ground state. This work attempts to provide a guide to determining the nature of the first excited K π = 0 + band in nuclei by the combined use of nuclear lifetimes, energy level evolutions, dynamic moments of inertia, and intrinsic quadrupole moments extracted from transition probabilities. The result is that for a subset of the nuclei in this region, the K π = 0 + band is consistent with the traditional 𝛽-vibration description of an oscillation built on the ground state.

CHFB+5DCH↗
DOE OSTI2024

Smooth trends in fermium charge radii and the impact of shell effects

The quantum-mechanical nuclear-shell structure determines the stability and limits of the existence of the heaviest nuclides with large proton numbers Z ≳ 100. Shell effects also affect the sizes and shapes of atomic nuclei, as shown by laser spectroscopy studies in lighter nuclides. However, experimental information on the charge radii and the nuclear moments of the heavy actinide elements, which link the heaviest naturally abundant nuclides with artificially produced superheavy elements, is sparse. Here we present laser spectroscopy measurements along the fermium (Z = 100) isotopic chain and an extension of data in the nobelium isotopic chain (Z = 102) across a key region. Multiple production schemes and different advanced techniques were applied to determine the isotope shifts in atomic transitions, from which changes in the nuclear mean-square charge radii were extracted. A range of nuclear models based on energy density functionals reproduce well the observed smooth evolution of the nuclear size. Both the remarkable consistency of model prediction and the similarity of predictions for different isotopes suggest a transition to a regime in which shell effects have a diminished effect on the size compared with lighter nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Identifying chemically similar multiphase nanoprecipitates in compositionally complex non-equilibrium oxides via machine learning

Abstract Characterizing oxide nuclear fuels is difficult due to complex fission products, which result from time-evolving system chemistry and extreme operating environments. Here, we report a machine learning-enhanced approach that accelerates the characterization of spent nuclear fuels and improves the accuracy of identifying nanophase fission products and bubbles. We apply this approach to commercial, high-burnup, irradiated light-water reactor fuels, demonstrating relationships between fission product precipitates and gases. We also gain understanding of the fission versus decay pathways of precipitates across the radius of a fuel pellet. An algorithm is provided for quantifying the chemical segregation of the fission products with respect to the high-burnup structure, which enhances our ability to process large amounts of microscopy data, including approaching the atomistic-scale. This may provide a faster route for achieving physics-based fuel performance modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗
DOE OSTI2023

α -cluster microscopic study of C 12 + C 12 fusion toward the zero energy limit

The carbon burning process is a fundamental step of stellar evolution and governs the synthesis of chemical elements important for the formation of life. In this work, we utilize the microscopic hybrid α cluster (HαC) model and an analytical approach, both in the framework of the Imaginary Time Method (ITM), to study the carbon fusion reaction towards zero energy. We obtain the values of the cross sections, astrophysical factors and correlate our results to collective motion. We also include a calculation for the 2 + carbon fusion and discuss a possible experimental investigation. Our results confirm direct experimental and theoretical results close to the barrier, while suggest possible 2 + mixtures in the indirect experimental data. Furthermore, our study offers an accurate view of the burning process in the somewhat unexplored low energy region.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Effects of nuclear structure and quantum interference on diffractive vector meson production in ultra-peripheral nuclear collisions

Here we study diffractive vector meson production in ultra-peripheral collisions (UPCs) of heavy nuclei, utilizing a theoretical framework based on the Color Glass Condensate (CGC) formalism. We focus on Au + Au, U + U, Ru + Ru, Zr + Zr, and Pb + Pb collisions, examining the transverse momentum dependence of vector meson production cross-sections and co s(2ΔΦ)asymmetries in the decay product distributions to explore the role of nuclear geometry. The angular modulation is due to the linear polarization of the incoming photons and quantum interference effects. We extract nuclear radii and find them to be consistent with experimental data from the STAR collaboration. The amplitudes of the cos(2ΔΦ)modulation in the cross-section and the extracted radii depend on the nuclear geometry. This dependence is dominated by the geometry-dependent variation of the minimum impact parameter required for ultra-peripheral collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components. Third Annual Progress Report

Additive manufacturing (AM) of high-strength corrosion resistance alloys for nuclear energy applications, such as stainless steel and Inconel, is currently based on laser powder bed fusion (LPBF) process. Some of the challenges with using LPBF method for nuclear manufacturing include the possibility of introducing pores into metallic structures. Probability of crack initiation at the pore depends on size, shape, and orientation of the defect. Pulsed Infrared Thermography Imaging (PIT) provides a capability for non-destructive evaluation (NDE) of sub-surface defects in arbitrary size structures. The PIT method is based on recording material surface temperature transients with infrared (IR) camera following thermal pulse delivered on material surface with flash light. The PIT method has advantages for NDE of actual AM structures because the method involves one-sided non-contact measurements and fast processing of large sample areas captured in one image. Following initial qualification of an AM component for deployment in a nuclear reactor, a PIT system can also be used for in-service nondestructive evaluation (NDE) applications. In this report, we describe recent progress in enhancing PIT capabilities in detecting microscopic subsurface defects in metals, and classifying shapes and orientation of pores in thermal images. For detection of microscopic defects in PIT imaging data, we have developed Spatial Temporal Denoised Thermal Source Separation (STDTSS) unsupervised machine learning (ML) image processing algorithm. We show that flat bottom hole (FBH) defects as small as 200µm in SS316 and IN718 specimens, can be detected with STDTSS algorithm. To the best of our knowledge, these are the smallest detected defects which are reported in literature. For classification of defects shapes, we have previously developed thermal tomography (TT) algorithm to obtain depth reconstructions of material defects from data cube of sequentially recorded surface temperatures. However, interpretation of TT images is non-trivial because of blurring with increasing depth. To address this challenge, we have developed a deep learning convolutional neural network (CNN) to classify size and orientation subsurface defects in simulated TT images.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Atomic masses are a foundational quantity in our understanding of nuclear structure, astrophysics, and fundamental symmetries. The longstanding goal of creating a predictive global model for the binding energy of a nucleus remains a significant challenge, however, and prompts the need for precise measurements of atomic masses to serve as anchor points for model developments. We present precise mass measurements of neutron-rich Ru and Pd isotopes performed at the Californium Rare Isotope Breeder Upgrade facility at Argonne National Laboratory using the Canadian Penning Trap mass spectrometer. The masses of 108 Ru, 110 Ru, and 116 Pd were measured to a relative mass precision $\delta$⁢$m/m$ ≈ 10 -8 via the phase-imaging ion-cyclotron-resonance technique, and represent an improvement of approximately an order of magnitude over previous measurements. Further, these mass data were used in conjunction with the physically interpretable machine learning (PIML) model, which uses a mixture density neural network to model mass excesses via a mixture of Gaussian distributions. The effects of our new mass data on a Bayesian-updating of a PIML model are presented.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

FY24 Progress Report: SRNL Analysis of ICCWR LCM and WAMS data for Corrosion and Cracking

Algorithms for Machine Learning (ML) and data analysis for the 3013 Surveillance Program have been developed in an ongoing collaborative effort by the Savannah River National Laboratory (SRNL) and the University of South Carolina (USC). The objective of the algorithms is to automate the identification of corrosion and crack formation in the Inner Container Closure Weld Region (ICCWR) of the canister system used to store Pu-bearing material. Data for corrosion and cracking is collected from large binary files generated by a Laser Confocal Microscope (LCM), the Wide Area 3D Measurement System (WAMS), or,in a recent proposal, by a Scanning Electron Microscope (SEM). The ML software uses the physical attributes in the data files (e.g., one or more of: height, color, and 16-bit grayscale values as functions of position in a plane projection) to detect signs of surface corrosion and cracking after being trained on similar data, with the features to be detected. Although the initial scope included screening for broader indicators of corrosion, e.g., pitting, identification of potential cracks was prioritized for the past several years at the request of program leadership. Labeled training data is essential to developing the ML algorithm, and enhancements to data labeling capability have been developed to address this essential precursor to application of ML routines. Efficient labeling is particularly important in view of the large volume of data required to train ML algorithms and the relative rarity of cracks in the ICCWR data set. The updated program will read binary data from either LCM, WAMS or SEM files, interrogate data attributes, facilitate user labeling of data for training ML algorithms, execute ML algorithms, output parameters from trained ML algorithms, report ML model accuracy with respect to labeled data, and generate graphical representations for various analyses. In FY24, hourglass neural networks (HNNs) that were initiated in FY22 were further developed and tested using available LCM data, and their performance was tested against that of the alternative U-Net Neural Network algorithm structure. HNNs along with previously developed Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) comprise a suite of ML tools for identification of cracks in the ICCWR

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A Data Deposition Platform for Sharing Nuclear Magnetic Resonance Data

Nuclear magnetic resonance (NMR) data are rarely deposited in open databases, leading to loss of critical scientific knowledge. Existing data reporting methods (images, tables, lists of values) contain less information than raw data, and are poorly standardized. Together, these issues limit FAIR (findable, accessible, interoperable, reusable) access to these data, which in turn creates barriers for compound dereplication and the development of new data-driven discovery tools. Existing NMR databases are either not designed for natural products data, or employ complex deposition interfaces that disincentivize deposition. Journals, including the Journal of Natural Products (JNP), are now requiring data submission as part of the publication process, creating the need for a streamlined, user-friendly mechanism to deposit and distribute NMR data. Recently, our team reported the development of the Natural Products Magnetic Resonance Database (NP-MRD; www.np-mrd.org). Here in this paper we present a new data deposition platform for the NP-MRD project that is designed to enable users to deposit NMR data for published or submitted manuscripts in under five minutes. This platform includes a suite of automated data extraction and standardization tools, together with a simple-to-use web-based interface and detailed error reporting to simplify the data deposition process and is available at www.np-mrd.org/submissions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

The Technical, Economic, Risk, and Adoption Assessment for Evaluating Work Reduction Opportunities in the Nuclear Industry

Automation and cost-saving initiatives, such as process automation with advances in artificial intelligence, are gaining traction in modernization efforts across the nuclear industry. As these innovations are increasingly adopted, it becomes crucial to evaluate their impacts comprehensively. Various technical and economic attributes, along with risk and human readiness factors, must be achieved to ensure that innovative projects enabling automation and modernization are successful. However, no systematic or integrated framework exists that allows plants to evaluate these innovative projects. To address this gap, the Technical, Economic, Risk, and Adoption (TERA) assessment offers a structured method to evaluate innovative technologies, ensuring solutions meet both operational and safety standards. The TERA framework integrates the disparate perspectives to assess modernization opportunities in nuclear operations. It combines qualitative and quantitative models to evaluate the relationship between performance and business impacts, while also enabling continuous re-evaluation during project development. This approach helps plant owners identify high-priority opportunities, optimize cost savings, and minimize risks, ensuring projects remain on track to achieve desired returns. By providing a comprehensive, systematic methodology, TERA enables informed, data-driven decisions that support successful modernization efforts, enhancing efficiency, safety, and cost savings across nuclear operations. This paper explains the TERA framework and its benefits for the nuclear industry.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

The structure of low-lying 1 - states in 90,94 Zr from (α,α'γ) and (p,p'γ) reactions

The low-lying dipole strength in the 90,94 Zr nuclei was investigated via (ρ,ρ'γ) at 80 MeV and (α, α', γ) at 130 MeV. The experiments, made at RCNP, used the magnetic spectrometer Grand Raiden for the scattered particles and the array CAGRA with HPGe detectors for the γ-decay. For 94 Zr these are the first data for both reactions and for 90 Zr these are the first data with (ρ,ρ'γ) and the first ones at high resolution for (α, α', γ). The comparison of the present results for the two nuclei with existing (γ, γ') data shows that both nuclear probes produce an excitation pattern different than that of the electromagnetic probes. DWBA calculations were made using form factors deduced from transition densities, based on RPA calculations, characterized by a strong neutron component at the nuclear surface. A combined analysis of the two reactions was performed for the first time to investigate the isoscalar character of the 1 - states in 90,94 Zr. The (ρ,ρ'γ) cross section was calculated using values for the isoscalar electric dipole energy-weighted sum rule (E1 ISEWSR) obtained from the (α, α', γ) data. The isoscalar strength for 90 Zr was found to exhaust 20 ± 2.5% of the EWSR in the energy range up to 12 MeV. In case of 94 Zr, a strength of 9 ± 1.1% of the EWSR was found in the range up to 8.5 MeV. Although an overall general description was obtained in the studied energy intervals, not all proton cross sections were well reproduced using the isoscalar strength from (α, α', γ). This might suggest mixing of isoscalar and isovector components and that this mixing and the degree of collectivity are not the same for all the 1 - states below the particle binding energy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

AI to Predict Glass Compositions Satisfying Property and Cooling Rate Criteria

This project aimed to develop a predictive, artificial intelligence/machine learning-based model to identify glass compositions satisfying specified property requirements. Such a model would provide a systematic approach for narrowing down the nearly infinite range of possible compositions for glasses and minimize unnecessary experimental trial and error. A large empirical data set for training and testing the algorithm was obtained from the SciGlass database. It contains glass compositions and corresponding property data from a wide range of literature sources. However, the currently available form of this data, recently released under an open database license, is not conducive to easy querying and use. The data structure was deciphered and a customized parsing code developed to make this data more usable for the current and future work. Neural network models were developed and trained on viscosity data from the database and demonstrated potential for improving prediction accuracy over a traditional regression model.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Energetics of oxidation and formation of uranium mononitride

Uranium mononitride (UN) is an advanced nuclear fuel currently being considered for use in several generation IV fast and thermal neutron spectrum core designs, with additional applications to thermal and electric nuclear propulsion reactors. Here, to better understand the thermal behavior and thermodynamic stability of UN, we investigated the bulk thermal oxidation process and thermochemical reactions, including the enthalpy of oxidation and standard enthalpy of formation, by conducting thermalgravimetric analysis – differential scanning calorimetry coupled with mass spectrometry (TGA-DSC-MS), and high temperature transposed temperature drop and oxide melt drop solution calorimetry. The bulk oxidation of UN (containing a small amount of α-UN 1.5+x ) in air was found to follow a step-wise process characterized by consecutive oxidative reactions UN-UN 1.5+x -UO 2 → UO 2 -UO 3 —N k → UO 3 —N k → UO 3 → U 3 O 8 . TGA results support that the UO 2 – U 2 N 3+x passivating layer delays the onset of rapid bulk oxidation of UN in air up to 662 K. Synchrotron X-ray diffraction (XRD) and extended X-ray absorption fine structure (EXAFS) analyses were performed to characterize UN and its final oxidized product. The standard enthalpy of formation (ΔH° f ) of UN was determined to be –144.4 ± 5.9 kJ/mol·atom, in good agreement with previously determined values from Pt encapsulation and bomb calorimetric experiments. Lastly, a negative linear correlation between ΔH° f and the N/U molar ratio was established based on the thermochemical data obtained in this work and previously reported enthalpies of formations of β-UN 1.5-x and α-UN 1.5+x .

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗