Search Results (113)

Many advanced reactor concept designs rely on high-assay low-enriched uranium (HALEU) fuel, enriched up to approximately 19.75% ²³⁵U by weight. Efforts are underway by the US government to increase HALEU production in the United States to meet anticipated needs. However, very few data exist for validation of computational models that include HALEU, beyond a few fresh fuel benchmark specifications in the International Reactor Physics Experiment Evaluation Project. Nevertheless, there are other data with potential value available for developing into quality benchmarks for use in data- and software-validation efforts. This paper reviews the available evaluated HALEU fuel benchmarks and some of the potentially relevant benchmarks for fresh highly enriched uranium. It then introduces experimental data for HALEU fuel irradiated at Idaho National Laboratory, from relatively recent irradiation programs at the Advanced Test Reactor. Such data should be evaluated and, if valuable, collected into detailed benchmark specifications to meet the needs of HALEU-based reactor designers. Full article

The EU conducted the pre-conceptual design (PCD) phase of the demonstration reactor (DEMO) during 2014–2020 under the framework of the EUROfusion consortium. The current strategy of DEMO design is to bridge the breeding blanket (BB) technology gaps between ITER and a commercial fusion power plant (FPP) by playing the role of a “Component Test Facility” for the BB. Within this strategy, a so-called driver blanket, with nearly full in-vessel surface coverage, will aim at achieving high-level stakeholder requirements of tritium self-sufficiency and power extraction for net electricity production with rather conventional technology and/or operational parameters, while an advanced blanket (or several of them) will aim at demonstrating, with limited coverage, features that are deemed necessary for a commercial FPP. Currently, two driver blanket candidates are being investigated for the EU DEMO, namely the water-cooled lithium lead and the helium-cooled pebble bed breeding blanket concepts. The PCD phase has been characterized not only by the detailed design of the BB systems themselves, but also by their holistic integration in DEMO, prioritizing near-term solutions, in accordance with the idea of a driver blanket. This paper summarizes the status for both BB driver blanket candidates at the end of the PCD phase, including their corresponding tritium extraction and removal (TER) systems, underlining the main achievements and lessons learned, exposing outstanding key system design and R&D challenges and presenting identified opportunities to address those risks during the conceptual design (CD) phase that started in 2021. Full article

The present study investigated the release of tritium from beryllium irradiated at 323 K to a neutron fluence of 4.67 × 10²⁶ m⁻² (E > 1 MeV), corresponding up to 22,000 appm helium and 2000 appm tritium productions. The TPD tests revealed a single tritium release peak during thermal desorption tests, irrespective of the heating mode employed. The tritium release peaks occurred at temperatures ranging from 1031–1136 K, depending on the heating mode, with a desorption energy of 1.6 eV. Additionally, the effective tritium diffusion coefficient was found to vary from 1.2 × 10⁻¹² m²/s at 873 K to 1.8 × 10⁻¹⁰ m²/s at 1073 K. The evolution of beryllium microstructure was found to be dependent on the annealing temperature. No discernible differences were observed between the as-received state and after annealing at 473–773 K for 5 h, with a corresponding porosity range of 1–2%. The annealing at temperatures of 873–1373 K for 5 h resulted in the formation of large bubbles, with porosity increasing sharply above 873 K and reaching 30–60%. Full article

Various theoretical studies have shown that highly diluted plutonium solutions could have a positive temperature effect, but up to now, no experimental program has confirmed this effect. The French Plutonium Temperature Effect Experimental Program (or PU+ in short) aims to effectively show that such a positive temperature effect exists for diluted plutonium solutions. The PU+ experiments were conducted in the “Apparatus B” facility at the CEA VALDUC research center in France. It involved several sub-critical approach-type experiments using plutonium nitrate solutions with concentrations of 14.3, 15, and 20 g/L at temperatures ranging from 20 to 40 °C. Fourteen (five at 20 g/L, four at 15 g/L, and five at 14.3 g/L) phase I experiments (consisting of independent sub-critical approaches) were performed between 2006 and 2007. The impact of the uncertainties on solution acidity and plutonium concentration made it difficult to demonstrate the positive temperature effect, requiring an additional phase II experiment (with a unique plutonium solution) from 22 to 28 °C that was performed in July 2007. This phase II experiment has shown the existence of a positive temperature effect of ~+5.17 pcm/°C (from 22 to 28 °C for a plutonium concentration of 14.3 g/L). It has recently been possible to confirm the results of this program with MORET 5 calculations by generating thermal scattering data S(α,β) at the correct experimental temperatures. This paper finally presents a fully documented experimental program highlighting the Plutonium Temperature Effect theoretically described in the literature. Its high level of precision and its “one-step” approach to criticality allowed it to show a significant positive temperature effect for a rather small variation of temperature (+6 °C). The order of magnitude of the effect was confirmed with Monte Carlo calculations using thermal scattering data for hydrogen in the solution produced by IRSN for the purpose of the comparison. Full article

Rhodium is one of the scarcest, most valuable, and useful platinum group metals, a strategically important material relied on heavily by automotive and electronics industries. The limited finite natural sources of Rh and exponentially increasing demands on these supplies mean that new sources are being sought to stabilise supplies and prices. Spent nuclear fuel (SNF) contains a significant quantity of Rh, though methods to recover this are purely conceptual at this point, due to the differing chemistry between SNF reprocessing and the methods used to recycle natural Rh. During SNF reprocessing, Rh partitions between aqueous nitric acid streams, where its speciation is complex, and insoluble fission product waste streams. Various techniques have been investigated for Rh recovery during SNF reprocessing for over 50 years, including solvent extraction, ion exchange, precipitation, and electrochemical methods, with tuneable approaches such as impregnated composites and ionic liquids receiving the most attention recently, assisted by more the comprehensive understanding of Rh speciation in nitric acid developed recently. The quantitative recovery of Rh within the SNF reprocessing ecosystem has remained elusive thus far, and as such, this review discusses the recent developments within the field, and strategies that could be applied to maximise the recovery of Rh from SNF. Full article

Multi-abnormal events, referring to the simultaneous occurrence of multiple single abnormal events in a nuclear power plant, have not been subject to consideration because multi-abnormal events are extremely unlikely to occur and indeed have not yet occurred. Such events, though, would be more challenging to diagnose than general single abnormal events, exacerbating the human error issue. This study introduces an efficient abnormality diagnosis model that covers multi-abnormality diagnosis using a one-vs-rest classifier and compares it with other artificial intelligence models. The multi-abnormality attention diagnosis model deals with multi-label classification problems, for which two methods are proposed. First, a method to effectively cluster single and multi-abnormal events is introduced based on the predicted probability distribution of each abnormal event. Second, a one-vs-rest classifier with high accuracy is employed as an efficient way to obtain knowledge on which particular multi-abnormal events are the most difficult to diagnose and therefore require the most attention to improve the multi-label classification performance in terms of data usage. The developed multi-abnormality attention diagnosis model can reduce human errors of operators due to excessive information and limited time when unexpected multi-abnormal events occur by providing diagnosis results as part of an operator support system. Full article

The timely detection of special nuclear material (SNM) transfers between nuclear facilities is an important monitoring objective in nuclear nonproliferation. Persistent monitoring enabled by successful detection and characterization of radiological material movements could greatly enhance the nuclear nonproliferation mission in a range of applications. Supervised machine learning can be used to signal detections when material is present if a model is trained on sufficient volumes of labeled measurements. However, the nuclear monitoring data needed to train robust machine learning models can be costly to label since radiation spectra may require strict scrutiny for characterization. Therefore, this work investigates the application of semi-supervised learning to utilize both labeled and unlabeled data. As a demonstration experiment, radiation measurements from sodium iodide (NaI) detectors are provided by the Multi-Informatics for Nuclear Operating Scenarios (MINOS) venture at Oak Ridge National Laboratory (ORNL) as sample data. Anomalous measurements are identified using a method of statistical hypothesis testing. After background estimation, an energy-dependent spectroscopic analysis is used to characterize an anomaly based on its radiation signatures. In the absence of ground-truth information, a labeling heuristic provides data necessary for training and testing machine learning models. Supervised logistic regression serves as a baseline to compare three semi-supervised machine learning models: co-training, label propagation, and a convolutional neural network (CNN). In each case, the semi-supervised models outperform logistic regression, suggesting that unlabeled data can be valuable when training and demonstrating value in semi-supervised nonproliferation implementations. Full article

Modeling and simulation lie at the heart of the design process of any nuclear application. An accurate representation of the radiation environment ensures not only the feasibility of new technologies, but it also aids in operation, maintenance, and even decommissioning. With increasingly complex designs, high-fidelity models have become a necessity for design maturity. McCAD has been under development for many years at Karlsruhe Institute of Technology (KIT) to facilitate the process of generating suitable models for nuclear analyses. In this paper, an overview of the major advances in the new version of the code is presented. A novel conversion algorithm has proven to be robust in significantly reducing the processing time to generate radiation transport models, making it easier to iterate on design details. A first-of-a-kind capability to generate hierarchical void cells is also discussed with preliminary analysis showing performance gains for particle tracking. Full article

Quality and safety are intensely related and go hand in hand. Quality of the safety-grade equipment is very important for the safety of a nuclear power plant (NPP) and achieving production goals. During manufacturing of plant components or equipment, deviation from the design might occur at different stages of manufacturing for various reasons, such as a lack of skilled manpower, deviation of materials, human errors, malfunction of equipment, violation of manufacturing procedure, etc. These deviations can be assessed cautiously and taken into consideration in the final safety analysis report (FSAR) before issuing an operating license. In this paper, we propose a Bayesian belief network for quality assessment of safety class equipment of NPPs with a few examples. The proposed procedure is a holistic approach for estimation of equipment failure probability considering manufacturing deviations and errors. Case studies for safety-class dry transformers and reactor pressurizers employing the proposed method are also presented in this article. This study provides insights for probabilistic safety assessment engineers and nuclear plant regulators for improved assessment of NPP safety. Full article

This paper presents the results obtained from numerical evaluations for the possibility of large-scale ²³⁸Pu production in the light-water VVER-1000 reactor and the reactivity effect caused by the loss-of-coolant accident in the central fuel assembly of the reactor core. This fuel assembly containing the Np–Am-component of minor actinides was placed in the center of the reactor core and intended for intense production of ²³⁸Pu. Optimal conditions were found for large-scale production of plutonium with an isotope composition suitable for application in radioisotope thermoelectric generators. The reactivity effect from the loss-of-coolant accident in the central Np–Am fuel assembly was evaluated, and the perturbation theory was used to determine the contributions of some neutron processes (leakage, absorption, and moderation) to the total variation of the effective neutron multiplication factor. Full article

Fluid-fueled molten-salt reactors (MSRs) are actively being developed by several companies, with plans to deploy them internationally. The current IAEA inspection tools are largely incompatible with the unique design features of liquid fuel MSRs (e.g., the complex fuel chemistry, circulating fuel inventory, bulk accountancy, and high radiation environment). For these reasons, safeguards for MSRs are seen as challenging and require the development of new techniques. This paper proposes one such technique through the observation of the reactor’s off-gas. Any reactor design using low-enriched uranium will build up plutonium as the fuel undergoes burnup. Plutonium has different fission product yields than uranium. Therefore, a shift in fission product production is expected with fuel evolution. The passive removal of certain gaseous fission products to the off-gas tank of an MSR provides a valuable opportunity for analysis without significant modifications to the design of the system. Uniquely, due to the gaseous nature of the isotopes, beta particle emissions are available for observation. The ratios of these fission product isotopes can, thus, be traced back to the relative amount and types of fissile isotopes in the core. This proposed technique represents an effective safeguards tool for bulk accountancy which, while avoiding being onerous, could be used in concert with other techniques to meet the IAEA’s timeliness goals for the detection of a diversion. Full article

The use of tungsten fiber-reinforced tungsten composites (W_f/W) has been demonstrated to significantly enhance the mechanical properties of tungsten (W) by incorporating W-fibers into the W-matrix. However, prior research has been restricted by the usage of single fiber-based textile fabrics, consisting of 150 µm warp and 50 µm weft filaments, with limited homogeneity, reproducibility, and mechanical properties in bulk structures due to the rigidity of the 150 µm W-fibers. To overcome this limitation, two novel textile preforms were developed utilizing radial braided W-yarns with 7 core and 16 sleeve filaments (R.B. 16 + 7), with a diameter of 25 µm each, as the warp material. In this study, bulk composites of two different fabric types were produced via a layer-by-layer CVD process, utilizing single 50 µm filaments (type 1) and R.B. 16 + 7 yarns (type 2) as weft materials. The produced composites were sectioned into KLST-type specimens based on DIN EN ISO 179-1:2000 using electrical discharge machining (EDM) and subjected to three-point bending tests. Both composites demonstrated enhanced mechanical properties with pseudo-ductile behavior at room temperature and withstood over 10,000 load cycles between 50–90% of their respective maximum load without sample fracture in three-point cyclic loading tests. Furthermore, a novel approach to predict the fatigue behavior of the material under cyclic loading was developed based on the high reproducibility of the composites produced, especially for the composite based on type 1. This approach provides a new benchmark for upscaling endeavors and may enable a better prediction of the service life of the produced components made of W_f/W in the future. In comparison, the composite based on fabric type 1 demonstrated superior results in manufacturing performance and mechanical properties. With a high relative average density (>97%), a high fiber volume fraction (14–17%), and a very homogeneous fiber distribution in the CVD-W matrix, type 1 shows a promising option to be further tested in high heat flux tests and to be potentially used as an alternative to currently used materials for the most stressed components of nuclear fusion reactors or other potential application fields such as concentrated solar power (CSP), aircraft turbines, the steel industry, quantum computing, or welding tools. Type 2 composites have a higher layer spacing compared to type 1, resulting in gaps within the matrix and less homogeneous material properties. While type 2 composites have demonstrated a notable enhancement over 150 µm fiber-based composites, they are not viable for industrial scale-up unlike type 1 composites. Full article

The production of prismatic dislocation loops in nuclear reactor core materials results in hardening because the loops impede dislocation motion. Yielding often occurs by a localised clearing of the loops through interactions with gliding dislocations called channeling. The cleared channels represent a softer material within which most of the subsequent deformation is localized. Channeling is often associated with hypothetical dislocation pileup and intergranular cracking in reactor components although the channels themselves do not amplify stress as one would expect from a pileup. The channels are often similar in appearance to twins leading to the possibility that twins are sometimes mistakenly identified as channels. Neither twins nor dislocation channels, which are bulk shears, produce the same stress conditions as a pileup on a single plane. At high doses, when cavities are produced (either He-stabilised bubbles at low temperatures or voids at high temperatures), there can be reduced ductility because the material is already in an equivalent advanced stage of microscopic necking. He-stabilised cavities form preferentially on grain boundaries and at precipitate or incoherent twin/ε-martensite interfaces. The higher planar density of the cavities, coupled with the incompatibility at the interface, results in a preferential failure known as He embrittlement. Strain localisation and inter- or intragranular failure are dependent on many factors that are ultimately microstructural in nature. The mechanisms are described and discussed in relation to reactor core materials. Full article

This article studies the optimal design approach for a portable nuclear magnetic resonance (NMR) system for use in non-destructive flow measurement applications. The mechanical and electromagnetic design procedures were carried out using the Ansys Maxwell finite-element analysis (FEA) software tool. The proposed procedure considered homogeneity and strength constraints while ensuring the desired functionality of the intended device for a given application. A modified particle swarm optimization (MPSO) algorithm was proposed as a reference design framework for optimization stages. The optimally designed NMR tool was prototyped, and its functionality was validated via several case studies. To assess the functionality of the prototyped device, Larmor frequency for hydrogen atom was captured and compared with theoretical results. Furthermore, the functionality and accuracy of the prototyped NMR tool is compared to the off-the-shelf NMR tool. Results demonstrated the feasibility and accuracy of the prototyped NMR tool constrained by factors, such as being lightweight and compact. Full article

Machine learning methods allow the prediction of material properties, potentially using only the elemental composition of a molecule or compound, without the knowledge of molecular or crystalline structures. Herein, a composition-based machine learning prediction of the material properties of V–Cr–Ti alloys is demonstrated. Our machine-learning-based prediction of the stability of the V–Cr–Ti alloys is qualitatively consistent with the composition-dependent experimental data of the ductile–brittle transition temperature and swelling. Furthermore, our computational results suggest the existence of a composition region, Cr+Ti ~ 60 wt.%, at a significantly low ductile–brittle transition temperature. This outcome contrasts with a reportedly low Cr+Ti content of less than 10 wt.% in conventional V–Cr–Ti alloys. Machine-learning-based numerical stability prediction is useful for the design and analysis of metal alloys, particularly for multicomponent alloys such as high-entropy alloys, to develop materials for nuclear fusion reactors. Full article