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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Nuclear Physics Exascale Requirements Review: An Office of Science Review sponsored jointly by Advanced Scientific Computing Research and Nuclear Physics, June 15 - 17, 2016, Gaithersburg, Maryland

Imagine being able to predict — with unprecedented accuracy and precision — the structure of the proton and neutron, and the forces between them, directly from the dynamics of quarks and gluons, and then using this information in calculations of the structure and reactions of atomic nuclei and of the properties of dense neutron stars (NSs). Also imagine discovering new and exotic states of matter, and new laws of nature, by being able to collect more experimental data than we dream possible today, analyzing it in real time to feed back into an experiment, and curating the data with full tracking capabilities and with fully distributed data mining capabilities. Making this vision a reality would improve basic scientific understanding, enabling us to precisely calculate, for example, the spectrum of gravity waves emitted during NS coalescence, and would have important societal applications in nuclear energy research, stockpile stewardship, and other areas. This review presents the components and characteristics of the exascale computing ecosystems necessary to realize this vision.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Tagged deep inelastic scattering measurement on deuterium with the LAD experiment

Here, the origin of the modification of the quark structure of nucleons in the nuclear medium can be tested with tagged recoil nucleon measurements from deep inelastic scattering off electrons on deuterium. The LAD experiment at the Thomas Jefferson National Laboratory (JLab) will measure the modification of the neutron structure function for high-momentum, highly-virtual neutrons by measuring the spectator recoil protons in coincidence with the scattered electron. An update on the experimental setup and projected results is presented. The experiment will collect data in Fall 2024.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Machine learning in nuclear materials research

Nuclear materials are often demanded to function for extended time in extreme environments, including high radiation fluxes with associated transmutations, high temperature and temperature gradients, mechanical stresses, and corrosive coolants. They also have a wide range of microstructural and chemical makeups, resulting in multifaceted and often out-of-equilibrium interactions. Machine learning (ML) is increasingly being used to tackle these complex time-dependent interactions and aid researchers in developing models and making predictions, sometimes with better accuracy than traditional modeling that focuses on one or two parameters at a time. Conventional practices of acquiring new experimental data in nuclear materials research are often slow and expensive, limiting the opportunity for data-centric ML, but new methods are changing that paradigm. Here we review high-throughput computational and experimental data approaches, especially robotic experimentation and active learning that is based on Gaussian process and Bayesian optimization. We show ML examples in structural materials (e.g., reactor pressure vessel (RPV) alloys and radiation detecting scintillating materials) and highlight new techniques of high-throughput sample preparation and characterizations, and automated radiation/environmental exposures and real-time online diagnostics. Herein, this review suggests that ML models of material constitutive relations in plasticity, damage, and even electronic and optical responses to radiation are likely to become powerful tools as they develop. Finally, we speculate on how the recent trends of using natural language processing (NLP) to aid the collection and analysis of literature data, interpretable artificial intelligence (AI), and the use of streamlined scripting, database, workflow management, and cloud computing platforms that will soon make the utilization of ML techniques as commonplace as the spreadsheet curve-fitting practices of today.

36 MATERIALS SCIENCE↗
DOE OSTI2024

First β -delayed neutron spectroscopy of 24 O

The β decay of 24 O was investigated at NSCL/MSU using a combination of neutron and γ-ray spectroscopy. For the first time, the β-delayed neutron energy spectrum of 24 O was measured, revealing three intensely populated, isolated neutron-unbound states in 24 F. This allowed for the extraction of the decay strength in 24 F up to 6.2 MeV. A comprehensive comparison of the experimental results with various nuclear theories, ranging from the empirical shell model to the most advanced ab initio calculations, was conducted. While most theoretical predictions align with the experimental data for low-lying states, discrepancies arise at higher excitation energies. Finally, in the transition from 24 O to 24 F, shell model calculations using the empirical USDB interaction predicted the structure of both nuclei without invoking the need for a stronger proton-neutron tensor force, which was postulated for the neighboring isotone 25 F.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Proton beam power limits for stationary water-cooled tungsten target with different cladding materials

The proton beam power limit for a solid-tungsten spallation target is largely determined by beam induced thermomechanical structural loads and decay heat power deposition, while its lifetime is limited by radiation damage and fatigue life of the target materials. In this paper, we studied the power limits of a stationary water-cooled solid tungsten target concept. Tantalum clad tungsten was considered as a reference case. Being a low activation material, zircaloy 2 cladding option was studied and its decay heat driven power limit was compared with the reference case. Zirconium alloys have proven operations records in spallation target and nuclear fission environments, supported by materials data obtained from post irradiation examinations. Recent study also demonstrated feasibility of diffusion bonding zirconium to tungsten using vanadium foil inter layer. Particle transport simulations code FLUKA was used to calculate energy deposition and decay heat power deposition in the target, based on the beam parameters technically feasible at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory. The energy deposition data were used for flow, thermal, and structural analyses to determine the beam intensity limit on the target concept studied. The decay heat deposition data were used to calculate the transient temperature evolution in the tungsten volumes in a loss of coolant accident (LOCA) scenario to determine its beam power limit. For a 1.3 GeV proton beam, the power limit on a stationary target was 400 kW for a tantalum clad target model and 800 kW for a zircaloy 2 clad target model.

Lee, Yong Joong↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Semimicroscopic description of isoscalar giant multipole resonances in medium-mass closed-shell nuclei

Main properties of isoscalar giant multipole resonances (up to L=3), including L=0,and 2 overtones, in medium-mass closed-shell nuclei are described within the semimicroscopic particle-hole dispersive optical model. Further, main properties are characterized by the energy-averaged strength distribution, projected (one-body) transition density, and probabilities of direct one-nucleon decay. Calculation results obtained for characteristics of the mentioned resonances in the 48 Ca, 90 Zr, and 132 Sn nuclei are compared with available experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Discovery of new isotopes in the fragmentation of 82 Se and insights into their production

The production cross sections of neutron-rich nuclei for elements just above 60 Ca were measured at the Facility for Rare Isotope Beams (FRIB). Four previously unobserved isotopes ( 63 Sc, 65,66 Ti, and 68 V) were produced, separated, and identified for the first time using the Advanced Rare Isotope Separator (ARIS). One event was found to be consistent with 61 Ca . The new isotopes were created through the interaction of an 82 Se beam with a carbon target at an energy of 228 MeV/𝑢 and a primary beam current of 1.07 p⁢µ⁢A. The event-by-event particle identification of the mass number (𝐴), atomic number (𝑍), and ionic charge state (𝑞) for the reaction products was achieved by combining measurements of energy loss, time of flight, magnetic rigidity, and total kinetic energy. This successful search for new isotopes, conducted at the beginning of FRIB's third year of operation, highlights the facility's discovery potential, which will continue to grow as the beam current increases. The production cross sections were analyzed in the context of the newly developed Δ⁢𝐵⁢𝐸 systematics that provides trends in neutron-rich regions and increased sensitivity to nuclear binding, an important nuclear structure observable. Furthermore, the new systematics addresses gaps in 𝑄 𝑔 -based methods commonly used to interpret the production cross sections of light neutron-rich elements, and provides an efficient empirical approach to describe cross-section trends across isotopic chains. Good agreement with the measured data was obtained when the Δ⁢𝐵⁢𝐸 systematics and abrasion-ablation calculations were performed using the Hartree-Fock-Bogoliubov (HFB-22) mass table.

59 ≤ A ≤ 89↗
DOE OSTI2020

Relativistic configuration-interaction and many-body-perturbation-theory calculations of U i hyperfine constants

Neutral uranium (U i) is a very difficult atom for theoretical calculations due to a large number (six) of valence electrons, strong valence-valence and valence-core correlations, high density of states, and relativistic effects. Configuration-interaction many-body perturbation theory (CI-MBPT) can efficiently treat valence-core correlations and relativistic effects, but because the formalism was developed for a Dirac-Hartree-Fock (DHF) starting potential that does not contain valence electrons, quite large CI space is needed to compensate for the + 6 charge of such a potential. Much more efficient is the relativistic configuration-interaction (RCI) approach, which uses a relatively accurate starting DHF potential that includes some valence electrons to make the valence-electron Hamiltonian diagonally dominated for some states. Here we report calculations of U i hyperfine constants of several low-energy states using the RCI method with the starting potential that includes four f valence electrons. With this starting potential, it is possible to use the single-configuration approximation or small basis sets to obtain quite accurate results for hyperfine-structure constants. In fact, by scaling the nuclear magnetic moment, the agreement for five levels was within 5% and a new magnetic moment can be recommended, 0.43(2). When two states are mixed, it is difficult to predict the exact mixing fractions, so in this case we proposed a method in which the mixing fraction is found by optimization of one parameter in the MBPT correction to obtain correct g factors. The resulting hyperfine constants are significantly improved. Finally, the CI-MBPT approach was also tested, with some success for low-energy states, with the limited basis set. The methods investigated here can be further developed to include more extensive data sets to improve accuracy and can be applied to other atoms and for calculations of other properties, for example, relevant to fundamental symmetry tests.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Semiclassical shell-structure micro-macroscopic approach for the level density

Level density ρ(E,A) is derived for a one-component nucleon system with a given energy E and particle number A within the mean-field semiclassical periodic-orbit theory beyond the saddle-point method of the Fermi gas model. We obtain ρ∝I ν (S)/S ν , with I ν (S) being the modified Bessel function of the entropy S. Within the micro-macro-canonical approximation (MMA), for a small thermal excitation energy U, with respect to rotational excitations E rot , one obtains ν = 3/2 for ρ(E,A). In the case of excitation energy U larger than E rot but smaller than the neutron separation energy, one finds a larger value of ν = 5/2. A role of the fixed spin variables for rotating nuclei is discussed. The MMA level density ρ reaches the well-known grand-canonical ensemble limit (Fermi gas asymptote) for large S related to large excitation energies, and also reaches the finite micro-canonical limit for small combinatorial entropy S at low excitation energies (the constant “temperature” model). Fitting the ρ(E,A) of the MMA to the experimental data for low excitation energies, taking into account shell and, qualitatively, pairing effects, one obtains for the inverse level density parameter K a value which differs essentially from that parameter derived from data on neutron resonances.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

In-beam γ-ray spectroscopy of 32 Mg via direct reactions

Background: The nucleus 32 Mg (N=20 and Z=12) plays a central role in the so-called “island of inversion,” where in the ground states sd-shell neutrons are promoted to the fp-shell orbitals across the shell gap, resulting in the disappearance of the canonical neutron magic number N=20. Purpose: The primary goals of this work are to extend the level scheme of 32Mg, provide spin-parity assignments to excited states, and discuss the microscopic structure of each state through comparisons with theoretical calculations. Method: In-beam γ-ray spectroscopy of 32 Mg was performed using two direct-reaction probes: one-neutron (two-proton) knockout reactions on 33 Mg ( 34 Si). Final-state exclusive cross sections and parallel momentum distributions were extracted from the experimental data and compared with eikonal-based reaction model calculations combined with shell-model overlap functions. Results: Owing to the remarkable selectivity of the one-neutron and two-proton knockout reactions, a significantly updated level scheme for 32 Mg, which exhibits negative-parity intruder and positive-parity normal states, was constructed. The experimental results were confronted with four different nuclear structure models. Conclusions: In some of these models, different aspects of 32 Mg and the transition into the island of inversion are well described. However, unexplained discrepancies remain, and, even with the help of these state-of-the-art theoretical approaches, the structure of this key nucleus is not yet fully captured.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

AMMT 2025 Milestone

Idaho National Laboratory initiated the examination of nickel-based alloys manufactured laser powder directed energy deposition additive manufacturing for potential applications in nuclear, high temperature structural components. With the rapid push towards additive manufacturing, codes do not exist that definitively define what is or is not tolerable for each process and application, such as with conventional, wrought products. This report contains the initial work to understand possible manufacturing methods for high temperature alloys, and specifically, void formation, microstructure evolution, and mechanical properties. To generate mechanical test data, specimens were tested irrespective of voids and microstructures were analyzed to better understand how to negate/improve these issues. The preliminary results showed major decreases in mechanical performance for material tested. Test specimens will continue to be produced to further improve additive manufacturing processes, quantify void acceptance, and better understand the most suitable high temperature alloys receptive to additive manufacturing and high temperature nuclear applications.

36 - MATERIALS SCIENCE↗
DOE OSTI2021

Assessment of Cold Spray Technology for Nuclear Power Applications

This report provides an overview of cold spray (CS) technology as it relates to repair and fabrication of metal alloys in the nuclear power industry. CS is a relatively new technology with applications established in other industries (e.g., aerospace, defense), and greater use of CS is anticipated within the nuclear power industry. This report is prepared in support of the United States Nuclear Regulatory Commission (NRC) Action Plan for Advanced Manufacturing Technologies (AMTs) Rev. 1, which includes objectives to identify the AMTs most likely to be used for nuclear power applications that require NRC approval, and to prepare NRC staff to review regulatory submittals containing components manufacturing using AMTs. In addition to providing an overview of CS technology, this report highlights engineering and scientific knowledge gaps of CS and rates the size of the knowledge gaps. It also assesses importance rankings of the knowledge gaps as they relate to several application categories. The application categories considered in this exercise include factory-applied CS for chloride induced stress corrosion cracking (CISCC) mitigation, field CISCC repair, light water reactor (LWR) factory structural fabrication, and LWR field dimensional restoration and corrosion protection. The combination of a knowledge gap size rating and importance ranking for an application can be used to prioritize additional data or information gathering efforts.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Report on initial development of a database of nuclear graphite characteristics based on microstructural characterization

This report outlines the current and future efforts to produce a comprehensive library of microstructures of nuclear graphite and carbon-based materials that are candidate materials for nuclear applications in the United States. This library must contain extensive characterizations of unirradiated graphite materials, a guide to some of the techniques used to characterize graphite, a compendium of characterization data of neutron-irradiated or oxidized material, and a compendium of microstructural information of carbon-based materials. These characterization efforts are being conducted at various length scales to understand these complex materials’ local structure and property relationships. Achieving this goal requires further developing or adapting advanced characterization techniques that capture graphite’s most relevant characteristics. Some of the general objectives of this project are to aid with the material selection, licensing, management, and core assessments of a graphite core by documenting the unirradiated microstructure of relevant grades or by characterizing the evolution of the microstructure under the reactor environment. Moreover, this project aims to provide additional information, guidelines for characterizing graphite, and a protocol to assess a nuclear graphite grade. This report also summarizes some of the initial results and some of the techniques commonly used to characterize nuclear graphite.

36 MATERIALS SCIENCE↗
DOE OSTI2023

An Assessment of Machine Learning Applied to Ultrasonic Nondestructive Evaluation

In the United States, the nuclear industry performs inservice inspection (ISI) through nondestructive examination (NDE) methods in accordance with guidelines specified in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (BPVC), Section XI, Rules for Inservice Inspection of Nuclear Power Plant Components. Ultrasonic nondestructive testing and evaluation (NDT&E) is one of the more commonly used techniques for inspecting Class 1 structural components in nuclear power systems. As the number of qualified NDE inspectors declines, the nuclear industry is looking to take advantage of advances in automation to enhance inspection capabilities. Advances in computational power, cloud-based computing, and machine learning algorithms make automated data analysis possible. Machine learning (ML) has shown huge potential in automated data analyses for ultrasonic NDE in the context of weld inspections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Measure Effects of Molten Halide Salt on Creep Rupture Lifetime for 316H, 709 and 617

Ongoing commercialization of different molten salt reactor concepts can considerably benefit from quantitative information on the impact of molten halide salts on the engineering properties such as creep and fatigue strength of materials of interest. Limited legacy data from the molten salt reactor experiment (MSRE) program showed a significant reduction in creep rupture strength of a Ni-base alloy in molten fluoride salt. This task evaluated the creep behavior of candidate structural materials 316H, 709 and 617 for advanced nuclear reactors under molten halide salt environments at 650, 700 and 750°C respectively. Creep tests were conducted in air, Ar, fluoride (FLiNaK) and chloride (NaCl-MgCl 2 ) salts. The molten salt environment caused a 50% reduction in creep rupture lifetime for 316H in FLiNaK and an 80% reduction in creep rupture lifetime in NaCl-MgCl 2 compared to air exposures. The molten fluoride salt environment resulted in almost a 90% and 50% reduction in creep rupture time for 709 and 617 irrespective of the cover gas environment (air and Ar). Significant differences in the compositional changes between the salt- and air-facing surfaces were observed in the alloy subsurface which might have potentially lead to formation of phases detrimental to the creep behavior.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Microstructure & Mechanical Properties of Scaled Thermally Aged LPBF 316H SS Builds

As part of the US Department of Energy’s Advanced Materials and Manufacturing Technologies program’s mission to accelerate qualification of advanced manufacturing pathways for nuclear applications, laser powder bed fusion (LPBF) 316H stainless steel (SS) has been selected as a model system to develop a rapid code case framework. This effort directly addresses the grand challenges of (1)expanding the limited portfolio of materials currently codified for elevated-temperature nuclear structural service under Section III, Division 5of the American Society of Mechanical Engineers’ Boiler and Pressure Vessel Code; and (2) significantly reducing qualification timelines that traditionally exceed a decade. The strategic importance of LPBF 316H lies in its immediate industrial relevance, existing data foundation from wrought 316H, and alignment with ongoing code case development for LPBF 316L.Prior work revealed accelerated precipitation of deleterious secondary phases and reduced creep ductility in as-printed LPBF 316H. Building on that prior research, FY2025activities focused on establishing an understanding of the key failure mechanisms of crept 316H specimens to aid in code case development and on evaluating stress relief (SR) parameters on the high-temperature performance and thermal aging-induced degradation of tensile and fracture behavior in LPBF316H.

36 MATERIALS SCIENCE↗