Spinodal instability at the onset of collective expansion in nuclear collisions
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The Facility for Rare Isotope Beams (FRIB) that will begin operations later this year will vastly increase the number of unstable isotopes available for experiments that, together with new theoretical developments will expand our understanding of nuclear structure and nuclear reactions that produce the elements of the periodic table and power the cosmos. The knowledge and understanding gained from FRIB will benefit a variety of applications including stockpile science, nuclear forensics, nonproliferation, nuclear energy, and nuclear medicine. LLNL is actively involved in a variety of basic and applied science activities related to FRIB. These activities are listed below and documented in the following sections: 1. FRIB Decay Station, 2. Isotope Harvesting, 3. Direct Reactions, 4. Surrogate Reactions, 5. Nuclear Structure and Collectivity, 6. Nuclear Fission, and 7. Nuclear Theory.
The collective-flow-assisted nuclear shape-imaging method in ultrarelativistic heavy-ion collisions (UHICs) has recently been used to characterize nuclear collective states. In this paper, we assess the foundations of the shape-imaging technique employed in these studies. We argue that some current UHIC nuclear imaging techniques neglect fundamental aspects of spontaneous symmetry breaking and symmetry restoration in colliding ions and incorrectly infer one-body multipole moments from studies of nucleonic correlations. Therefore, the impact of this approach on nuclear structure research has been overstated. Conversely, efforts to incorporate existing knowledge on nuclear shapes into analysis pipelines can be beneficial for benchmarking tools and calibrating models used to extract information from ultrarelativistic heavy-ion experiments.
A Monte-Carlo method for the generation of correlation and covariance matrices for independent and cumulative fission yields has been developed. The method uses a constrained Monte-Carlo resampling structure in order to vary evaluated fission yield libraries in a way that meets basic conservation principles. This results in the generation of correlation/covariance matrices with limited model bias and uncertainty; the matrices are primarily reflective of the evaluated fission yield uncertainties and correlations that arise from the evaluation process. This method has been applied to generate correlation and covariance matrices for all of the fissioning systems of the ENDF/B-VIII.0 and JEFF-3.3 evaluations, marking the first time such matrices have been generated for all of these systems. These covariance matrices have been published online for immediate public use. These correlation and covariance matrices can be used to improve uncertainty estimation in calculations of reactor antineutrino emission rates, decay heat problems, and nuclear forensics.
One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.
Research into the cosmic synthesis of the elements, the evolution and explosion of stars, the nature of the early Universe, and other important topics in nuclear astrophysics are at the forefront of nuclear science. These studies are motivating laboratory measurements and theoretical calculations that, after significant investments, are pushing the boundaries of what is possible. The latest nuclear results, however, must be specially prepared before they can be used to advance our knowledge of the cosmos. This processing requires a set of resources unique to nuclear astrophysics, and an impressive collection of nuclear reaction and nuclear structure datasets, processing codes, thermonuclear reaction rate libraries, and simulation codes and services have been developed for the field. There are, however, some serious challenges to these efforts that will only worsen in the future, making it important to develop strategies and act now to ensure a sustainable future for this work. After detailing the specific data types needed for nuclear astrophysics and the available data resources, the major challenges in this work and their implications are discussed. A set of initiatives are proposed to meet those challenges along with suggested implementations and possible ways that they may advance our understanding of the Universe and strengthen the field of nuclear astrophysics.
Advanced reactor technologies have attracted considerable financial support, catalyzing innovation in the nuclear energy landscape. These next generation reactors are designed with enhanced passive safety systems and the potential for cost reductions. As these advanced reactors approach technological maturity, their commercial success also requires focused examination and essential support. Economic assessments in the nuclear energy sector often emphasize construction costs and the risk of scheduling delays as primary contributors to deployment expenditures. Traditionally, vital elements of nuclear energy deployment including civil/structural engineering, design sophistication and automation have been either undervalued or postponed in the development cycles. Additionally, the nuclear industry’s collective experience in nuclear project execution has diminished over the past several decades due to the infrequency of new plant constructions. Hence, the National Reactor Innovation Center’s (NRIC) Advanced Construction Technology Initiative (ACTI) aims to reduce cost overruns and schedule slippages that have plagued the construction of nuclear power plant projects. With this initiative, NRIC is facilitating the development of advanced nuclear plant construction technologies and approaches through partnerships that would provide game-changing benefits to the construction of advanced nuclear power plants.
Advanced reactor technologies have attracted considerable financial support, catalyzing innovation in the nuclear energy landscape. These next generation reactors are designed with enhanced passive safety systems and the potential for cost reductions. As these advanced reactors approach technological maturity, their commercial success also requires focused examination and essential support. Economic assessments in the nuclear energy sector often emphasize construction costs and the risk of scheduling delays as primary contributors to deployment expenditures. Traditionally, vital elements of nuclear energy deployment including civil/structural engineering, design sophistication and automation have been either undervalued or postponed in the development cycles. Additionally, the nuclear industry’s collective experience in nuclear project execution has diminished over the past several decades due to the infrequency of new plant constructions. Hence, The National Reactor Innovation Center’s (NRIC) Advanced Construction Technology Initiative (ACTI) program aims to reduce cost overruns and schedule slippages that have plagued the construction of nuclear power plant projects. With this initiative, NRIC is facilitating the development of advanced nuclear plant construction technologies and approaches through partnerships that would provide game-changing benefits to the construction of advanced nuclear power plants.
NSRC’s lineages dates to the Technical Library formed by J. Robert Oppenheimer in 1943 as part of the Manhattan Project. Houses 75+ years of nuclear weapons research, designs, procedures, reports, etc. This is the largest collection of nuclear weapons information anywhere in the nation.
The facilities at the Materials and Fuels Complex (MFC) contain a diverse collection of nuclear research and development capabilities which enable experiments and engineering to drive the world’s nuclear energy future. It is our responsibility to ensure safe, efficient, and reliable operation of MFC facilities to support INL’s nuclear science and technology and national security missions. The MFC management plan makes it clear that safely achieving this takes the right people using good processes.
The 252 Cf spontaneous fission (sf), prompt fission neutron spectrum (PFNS) is a fundamental quantity for nuclear physics measurements of neutron-emitting reactions. This energy distribution of neutrons emitted from fission has been considered a neutron data standard for decades and has been utilized as a reference for neutron detection efficiency, validation of Monte Carlo simulations, benchmarking of dosimetry standards, and more. A significant portion of the global collection of nuclear data on neutron-induced reactions is correlated with the 252 Cf (sf) PFNS. Despite the reliance on this quantity by the nuclear physics community, the historical collection of 252 Cf PFNS measurements display systematic disagreements that are not understood or easily explained. These experimental discrepancies could potentially bias the 252 Cf PFNS Standard evaluation. On top of this, these past experiments frequently employed correlated experimental measurement or analysis methods. The artificial intelligence (AI)/machine learning (ML)-informed californium chi-nuclear data experiment (AIACHNE) project was formed to (a) investigate these discrepancies utilizing AI/ML methods to identify outlying regions of literature data, assign these regions to features of the experiment itself, and perform an improved evaluation of the 252 Cf PFNS and (b) perform a new experimental measurement of this quantity designed to improve upon the existing literature database. Here, in this work, we report on the AIACHNE 252 Cf PFNS experiment utilizing a new analysis method uncorrelated with all previous measurements: neutron efficiency determinations based on elastic neutron scattering on 12 C and 9 Be . This new method provides an independent test of the existing literature data and evaluation of the 252 Cf (sf) PFNS. The method is described with detailed covariance quantification procedures, as well as a direct discussion of the sources of uncertainty described as requirements in the “Templates” series of papers. The 252 Cf (sf) PFNS reported in this work agrees well with the overall shape of the existing standard PFNS evaluation as well as many literature measurements, thus verifying the current evaluation utilizing new techniques. However, the results suggest that there are deficiencies in the angle-differential 12 C and 9 Be (𝑛, 𝑛) evaluated nuclear data, which produce unphysical structures in the reported result. While these structures are relatively minor, they become obvious because of the high statistical precision of the data and the expected smooth continuity of the 252 Cf (sf) PFNS.
Nuclear clusters or voids in the inner crust of neutron stars were predicted to have various shapes collectively nicknamed nuclear pasta. Furthermore, the recent review by López, Dorso and Frank summarized their systematic investigations into properties especially the morphological and thermodynamical phase transitions of the nuclear pasta within a Classical Molecular Dynamics model, providing further stimuli to find more observational evidences of the predicted nuclear pasta in neutron stars.
Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition, to actual data. Additionally, experimental nuclear reaction data (EXFOR) and Nuclear Science References (NSR) databases contain compilations based on primary (journals) and secondary (conference proceedings, theses, preprints, etc.) publications, and data received from authors via private communications. The secondary library materials and private communications often represent a bottleneck for nuclear data verification, compilation, evaluation, and dissemination activities. To address this issue, bibliographic materials were scanned into PDF (Portable Document Format) files and uploaded in a relational database. The traditional scope of nuclear databases that includes meta-data and numbers derived from data in specialized formats was broadened to accommodate the large volumes of original nuclear data publications. The complete PDF publication files were stored in a relational database as Binary Large OBjects (BLOB). This unique collection of nuclear data compilations and supporting publications generate many opportunities for machine learning applications. The Web interfaces for authorized and public access to the EXFOR-NSR nuclear publications database were implemented at the U.S. National Nuclear Data Center, https://www.nndc.bnl.gov/ and IAEA Nuclear Data Section, https://www-nds.iaea.org/ . The current system is complementary to major nuclear libraries and narrowly focused on nuclear data compilation and evaluation procedures. The contents of the PDF database, details of implementation, and Web interface are described. New capabilities for data curation, knowledge preservation, worldwide dissemination, and natural language processing (NLP) applications are given.
The experimental results published before Aug 2022 from the various reaction and decay studies leading to nuclides of Z=56 to Z=72, 154 Ba, 154 La, 154 Ce, 154 Pr, 154 Nd, 154 Pm, 154 Sm, 154 Eu, 154 Gd, 154 Tb, 154 Dy, 154 Ho, 154 Er, 154 Tm, 154 Yb, 154 Lu, 154 Hf, in the A=154 mass chain have been reviewed. These data are collected and presented in decay or reaction datasets, together with Adopted Levels and gammas datasets that are the most extensive collections of nuclear structure data for each nuclide. Furthermore this work is intended to supersede the previous evaluation of the A=154 nuclides by C.W. Reich (2009Re14), which was published in Nuclear Data Sheets 110, 2257 (2009).
With the increasing number of data-driven models in nuclear applications, large volumes of numerical data are required to accurately model and predict the health status of a plant component. However, many historical operation logs that contain useful information are not fully utilized due to the lack of a systematic approach of digitization. To overcome this issue, this study proposes an automatic pipeline for extracting information from handwritten tabular documents collected from nuclear power plants. In our pipeline, we first denoise scanned documents with morphological operations, and then extract relevant parts from individual pages using both traditional computer vision and neural network methods. Handwriting recognition is applied to obtain text and numbers. As the most challenging step is how to crop only relevant information, the main focus of our paper is to detect tables and cells from scanned handwritten documents. Here we evaluate the efficiency and accuracy of our proposed method on handwritten operational reports obtained from a real-world case study. The results demonstrate the high accuracy and practicality of our proposed method.
Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.
What is the NSRC? It is: Los Alamos National Laboratory's classified library; World’s most-comprehensive collection of nuclear security materials; Supports our national security mission; and, is guided by Highly-trained, expert staff.