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The decay sublibrary aims to describe all known spontaneous decays and branching ratios for all nuclei across the nuclide chart. ENDF/B-VIII.1 remains unchanged from ENDF/B-VIII.0 for the decay sublibrary.
The neutron standards sublibrary describes specific reaction cross sections, in a limited range, that are so well-known that they are used as ratios or references in other measurements. ENDF/B-VIII.1 remains unchanged from ENDF/B-VIII.0 for the neutron standards sublibrary
The use of subsize specimens in nuclear materials testing has been a subject of ongoing interest due to radiation safety concerns and resource conservation needs. It is also of interest in additive manufacturing (AM), also known as 3D-printing, as subsize specimens can more accurately represent the behavior of the small, intricate geometries often produced using AM. A novel, extremely small geometry, called the Subsize Teeny (SST) was recently developed and is of interest for implementation. Given its extraordinarily small size and the complexities associated with subsize specimen testing, adequate vetting of this geometry is necessary to ensure data quality. Here, a variety of unirradiated nuclear structural materials were tested in the SST geometry and compared against the well-established SSJ3 geometry. In addition, two case studies implementing the SST as a screening geometry for AM materials were also conducted. The question of SST viability was found to be highly nuanced and will often be dependent on the context or application in question. It was determined, however, that the SST is a largely invalid geometry for exceptionally coarse-grained materials or in cases where the physical defect volume equals or exceeds 0.1 % or where the specimen machining parameters result in significant surface alterations. On the other hand, it was determined that the SST may be employed with confidence if the test material is nearly or totally free of physical defects, isotropic, demonstrates homogeneous plastic deformation, and possesses a fine-grained, nearly or totally homogeneous microstructure with at least twelve slip systems.
Special nuclear material in sub-critical and critical configurations measured in integral experiments are important for validation and adjustment of nuclear data. Many different evaluations of nuclear data exist, and these different evaluations can provide different values for individual cross sections that vary due to the uncertainties in differential experiments or lack of such data. For integral experiments, differences in these individual cross sections can have compensating errors, which lead to the same answer. One example of this is the Jezebel critical assembly, where k eff of the system is correctly computed by both ENDF/B-VIII.0 and JEFF-3.3, despite having substantially different underlying evaluated values for specific reactions (such as elastic and inelastic cross sections). To reduce compensating errors in nuclear data, the Experiments Underpinned by Computational Learning for Improvements in Nuclear Data (EUCLID) project has utilized machine learning to design a set of sub-critical and critical experiments. These experiments include slab- and cube-like configurations of 239 Pu in the form of the ZPPR plates. Six different responses were measured on a total of thirteen different configurations. One of these responses, the neutron leakage spectrum, was measured using an EJ301D detector. Finally, the results of the neutron leakage spectra show good agreement (within 1–2 σ ) with the expected spectrum from simulations and will be used in the subsequent nuclear data adjustment done by the EUCLID team.
Analysis and evaluation of nuclear debris is a key component to identify forensic signatures in a post-detonation environment. Currently, methods exist and are practiced in obtaining signatures from post-detonation debris. However, efforts to reduce the timeline in obtaining signatures is continuous within nuclear forensics research and will be forwarded by this research. This education and research project will explore combined material chemistry and separations of radionuclides to provide actionable signatures in less time than standard methods. The research will use the phase distribution of radionuclides in debris to develop targeted and rapid dissolution methods. These dissolution methods will be coupled with microfluidics to achieve samples suitable for analysis within reduced timelines.
Large language models (LLMs) are increasingly capable of answering technical questions, synthesizing domain knowledge, and supporting engineering workflows. For nuclear science and engineering, these capabilities require careful, domain-specific evaluation before they can be credibly incorporated into safety-related activities, regulatory review, or technical decision support. This paper presents preliminary results from benchmarking framework for evaluating LLM capabilities in nuclear contexts. The framework is organized into three evaluation categories: nuclear fundamentals, general dual-use knowledge, and plant specific knowledge. These categories are intended to distinguish general nuclear engineering competence from broader technical reasoning and more context-dependent nuclear knowledge. Initial evaluations focus on nuclear fundamentals using questions representative of the knowledge expected of a nuclear professional engineer. Results indicate that contemporary frontier models perform at a high level and substantially exceed the performance of older model generations, with some models approaching saturation of the current benchmark. These findings suggest both the rapid improvement of LLM capabilities in specialized technical domains and the need for more discriminating evaluation methods. The paper presents the benchmark structure, preliminary model-comparison results, and ongoing work. This work supports development of verifiable, responsible, and safety-conscious methods for assessing AI systems in nuclear engineering applications.
Irradiation experiments are a prerequisite for evaluating nuclear reactor system designs, analyzing the performance of these systems, and obtaining licenses. Likewise, irradiation facilities are necessary for producing the radioisotopes used in industrial and medical applications. Recent developments in modeling and simulation capabilities and advancements in computational resources have further enabled the design of irradiation experiments for evaluating radiation-induced phenomena and determining nuclear fuel, material, and system design and safety criteria pertaining to both normal and accident scenarios. These computational tools and models require comprehensive experimental datasets acquired under prototypic radiation conditions—for exploring material and system performance under the uniquely harsh environments found in nuclear reactors—to enable verification and validation for qualification and licensing purposes. However, qualification of irradiation experimental facilities, primarily research and test reactors (RTRs), necessitates that their performance be evaluated based on the irradiation environment (e.g. flux, power, testing capabilities) using an appropriate scoring matrix. Although many university campus RTRs are available for research and development (R&D) activities and initiatives, this study focuses on evaluating and qualifying the irradiation facilities (mostly RTRs) within the United States that are suitable for advanced nuclear fuel, material, and system irradiation experiments aimed at establishing operational-performance limits and informing component and fuel designs so as to improve operational efficiencies and mitigate proliferation vulnerabilities, as well as for radioisotope production aimed at multipurpose applications. As a result, the findings of the present study support the acceleration of nuclear fuel and material qualifications, thus hastening new and advanced nuclear energy system demonstrations and radioisotope production efforts by using extended R&D.
Technical presentation for paper titled: The Technical, Economic, Risk, and Adoption Assessment for Evaluating Innovation Projects in the Nuclear Industry
The anticipated expansion of the nuclear industry and the deployment of new nuclear reactors (200 + GW of new nuclear capacity by 2050) require the development of monitoring systems that align with safety and security concerns, providing enhanced evaluation capabilities. A remote monitoring system using satellites and deep learning techniques was evaluated for its ability to detect anomalies and capture various features of nuclear reactors independently of the conditions on the ground. Satellite images of current operational and under-construction nuclear power plants were collected from Google Earth Pro as a surrogate database. Subsequently, five datasets were created from the collected images. Transfer learning technique was used for several classification tasks utilizing VGG16, ResNet50V2, Xception, DenseNet121, and MobileNetV2 pre-trained models. In the first task, the capability of the monitoring system to detect abnormal conditions or processes in a nuclear power plant was investigated. In the second task, the ability to capture operational features remotely was examined. As an example, for the purposes of this study, these features included classifying reactors based on type, power range, or onsite condition. Several evaluation metrics were used to compare the performance of the pre-trained models and the overall monitoring system. Here, the evaluation results demonstrated that deep learning techniques and pre-trained models applied to satellite images have the potential to facilitate further and expand capabilities in monitoring systems to assess plant operation details.
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Evaluation of the hydrogen storage capacity of porous rocks is crucial for underground hydrogen storage. Using 1 H nuclear magnetic resonance (NMR) spectroscopy, we successfully characterized the hydrogen responses and identified storage mechanisms in Berea sandstone under varying water saturation. Here, the results indicate that the injected hydrogen behaves as a free gas phase and is capable of occupying the empty pore volume regardless of the saturation state. No hysteresis was observed during injection and production cycles.
The role of chlorine as a neutron poison and as a seed for producing radioactive waste in nuclear systems has driven a renewed interest to improve its nuclear data uncertainties. Additionally, basic and applied science programs that use CLYC (Cs 2 LiYCl 6 :Ce) detectors for neutron spectroscopy and monitoring are also very sensitive to any change in chlorine nuclear data for simulations of the detector response. In this work, sensitivities relevant for these different applications are addressed through simulations of the efficiency of CLYC detectors in a fast fission spectrum when applying new chlorine nuclear data as input. These simulations are validated by an experimental measurement using CLYC detectors coupled to an ionization chamber loaded with a 252 Cf spontaneous fission source. The results are then used to obtain the first reliable direct measurement of the 35 Cl(n,p 0 ) and summed Cl(n,p+n,α) fission spectrum average cross sections, found to be 54.7(32) and 105.0(98) mb, respectively. The results are within uncertainty of calculated fission spectrum averaged cross sections based on recently re-evaluated chlorine nuclear data, which confirm recent impact studies performed for the Molten Chloride Reactor Experiment. Meanwhile, there currently exists only one published criticality benchmark experiment that is sufficiently sensitive to chlorine nuclear data. Discrepancies are found with this set of criticality safety benchmarks, which are more sensitive to thermal and epithermal neutron energies than the energies, above 100 keV, tested in this current work. Hence, there is still a need to re-evaluate the chlorine nuclear data at lower energies to assess these discrepancies. Interpretation of the data from future “faster” criticality benchmarks, which are needed for next-gen fast reactor designs, benefit from the improved constraints on the chlorine nuclear data validated in this work.
IRMA (In)elastic Representation of Materials As S(α,β) evaluations IRMA turns one phonon model into three outputs that usually require three separate tool chains: an evaluated nuclear-data file, predicted neutron-scattering spectra, and scattering kernels for Monte Carlo transport. The three outputs draw on a single, consistent description of the material, so the evaluation, the spectroscopy that can validate it, and the transport that uses it always agree about the physics. Nuclear data. IRMA writes ENDF-6 File 7 thermal scattering evaluations on automatically constructed (α, β) grids. This part reimplements and generalizes NJOY's LEAPR: the classic kernels reproduce freshly generated NJOY2016 tapes digit for digit and published reference tapes to about 1e-4, and the generalized paths add the exact coherent one-phonon term, anisotropic Debye-Waller tensors, coherent elastic for arbitrary crystals, and a per-species partition for polyatomic materials. The tapes feed NJOY, AMPX, FUDGE, and every transport code downstream of them. Neutron spectroscopy. The irma.spectra forward model projects the same physics onto an instrument's kinematics and resolution: INS spectra for VISION and generic indirect geometries, and 2-D S(Q,E) powder maps for direct-geometry spectrometers, from a phonopy model or straight from a phonon DOS. It can be used to predict a proposed measurement before beam time; in analysis, it supplies the calculated single-scattering counterpart of a measured spectrum, from the same material description the evaluation was built from. Monte Carlo transport. The irma.ncrystal exporter writes per-temperature scattering kernels for the companion NCrystal plugin, so McStas, OpenMC, and other NCrystal-aware codes sample the same physics. The exported kernels carry the per-site anisotropic Debye-Waller tensors, keeping directional coherent-elastic physics that NCrystal's standard scalar treatment does not represent. With the same physics inside a transport code, an entire beamline becomes a virtual experiment: IRMA's end-to-end validation ran a custom McStas implementation of the ARCS spectrometer, assembled from the existing McVine and McStas models, against measured data. From a bare crystal structure. The irma mlip front end builds the phonon model itself: a structure file and a choice of potential are enough. Nine pretrained machine-learned interatomic potentials are supported, on a laptop CPU, with no first-principles calculation; an approximate phonon model for a new material costs minutes, not a DFT campaign, and the build emits prefilled inputs for all three outputs. The result is a good starting point rather than a finished evaluation: survey-quality physics with every parameter exposed for review. A converged atomistic calculation enters the same way, as a phonopy model, when higher fidelity is needed.
Abstract not provided.
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.
This work investigates a 4 He-detector active interrogation system that leverages neutron spectroscopy to classify nuclear waste streams. MCNP models tested the concept through the simulation of a D-D neutron generator, an array of 4 He detectors, and various waste compositions. The fast-neutron Differential Die-Away signature was augmented with a neutron-energy discrimination signature. This signature isolates induced fission neutrons, the energy of which is greater than that of the D-D monoenergetic spectrum. With the incorporation of this spectroscopic technique, the measurement time decreased by 3–9% (depending on the degree of neutron moderation and absorption presented by the sample), demonstrating how neutron spectroscopy can enhance active interrogation methods. The reduced measurement times would have significant financial and logistical benefits for facilities with large footprints of low-level waste production.
Human system interface design in industrial process control is guided by industry standards, human factors best practices, and domain-specific conventions, and often there is a conflict between one or more of the sources of design input for specific design elements. In the nuclear domain, one design element for which conflict arises is the use of color to represent equipment state. Here, this study evaluates the tradeoffs associated with using color in a process control display versus using white and shades of gray. The performance metrics were response time, accuracy, and eye movement metrics using a simplified experimental task and professional operators. Results revealed that adhering to color conventions in nuclear power yielded small advantages in simple tasks, but did not exist for more complex tasks. The results did not provide strong evidence for or against using a particular color scheme and revealed the need for further research on the use of color for commercial nuclear power plants and other process control industries.
In natural uranium, the 234 U isotope represents only 0.0055%, however, this minor isotope can affect highly enriched uranium metal benchmark calculations. In fact, enriched uranium contains more 234 U than natural uranium as the result of the uranium enrichment process. Therefore, the n+ 234 U nuclear data evaluation is one of the milestones of the APPENDIX B within the Nuclear Criticality Safety Program (NCSP).