J. Nucl. Eng., Volume 4, Issue 3 (September 2023) – 9 articles

An analysis of the peculiarities of the German Uranium Project (1939–1945) reveals that it was, in many ways, different from what one would expect. There was no work at all on a possible bomb, nor on plutonium. The reactor experiments were limited to subcritical systems and did not attempt to achieve the proclaimed goal of a self-sustaining chain reaction. The so-far identified deficits (lack of interest in Nazi circles, mismanagement, scientific mistakes, and deteriorating work conditions during the war) are relevant but not sufficient for explaining the peculiarities. We deduce that the scientists involved, and even the Heereswaffenamt (army ordnance), shied away from making progress, not only towards a bomb but even towards a reactor. They did not fail; they rather renounced a possible success in order not to provoke political interest in the development of a bomb. Full article

Rhenium-186 (Re-186) has attracted attention as a medical isotope. The feasibility of producing Re-185, the raw material for Re-186, using a fast reactor was evaluated using a continuous energy Monte Carlo code. The irradiation of natural tantalum (Ta) in the fast reactor can produce Re-185 with an isotopic purity of 99%. A two-step irradiation process with different moderators was found to improve the production rate of Re-185. Specifically, this can be achieved by using zirconium hydride (ZrH_1.7) as a moderator in the first transmutation process from natural Ta to tungsten (W), and then zirconium deuteride (ZrD_1.7) as a moderator in the second transmutation process from W to Re-185. Due to the two-step irradiation, the production rate of Re-185 from Ta can be increased up to a maximum of 470 times compared with irradiation without a moderator, and 2.3 g of Re-185 can be obtained from 1571 g of Ta in 1 year of irradiation. The proposed isotope production method is a new method that is different from the conventional electromagnetic enrichment process. Full article

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