Journal of Nuclear Engineering

The accurate calculation of reactor core heating is vital for the design and safety analysis of reactor physics. However, negative KERMA factors may be produced when processing and evaluating libraries of the nuclear data files ENDF/B-VII.1 and ENDF/B-VIII.0 with the NJOY2016 code, and the continuous-energy neutron cross-section library ENDF71x with MCNP also has the same problem. Negative KERMA factors may lead to an unreasonable reactor heating rate. Therefore, it is important to investigate the influence of negative KERMA factors on the calculation of the heating rate. It was also found that negative KERMA factors can be avoided with the CENDL-3.2 library for some nuclides. Many negative KERMA nuclides are found for structural materials; there are many non-fuel regions in fast reactors, and these negative KERMA factors may have a more important impact on the power distribution in non-fuel regions. In this study, the impact of negative KERMA factors on power calculation was analyzed by using the RBEC-M benchmark and replacing the neutron cross-section library containing negative KERMA factors with one containing normal KERMA factors that were generated based on CENDL-3.2. For the RBEC-M benchmark, the deviation in the maximum neutron heating rate between the negative KERMA library and the normal library was 6.46%, and this appeared in the reflector region. In the core region, negative KERMA factors had little influence on the heating rate, and the deviations in the heating rate in most assemblies were within 1% because the heating was mainly caused by fission. However, in the reflector zone, where gamma heating was dominant, the total heating rate varied on account of the gamma heating rate. Therefore, negative KERMA factors for neutrons have little influence on the calculation of fast reactor heating according to the RBEC-M benchmark. Full article

The nuclear fuel cycle forms the basis for producing special nuclear materials used in nuclear weapons via a series of interdependent industrial operations. These industrial operations each produce characteristic emanations that can be gathered to ascertain signatures of facility operations. Machine learning and deep learning techniques were applied to time series magnetic field sensor data collected at the High Flux Isotope Reactor (HFIR) to assess the feasibility of determining the ON/OFF operational state of the reactor. When data collected by the sensor near the cooling fans, position 9, are transformed to the frequency domain, it was found that both machine and deep learning methods were able to classify the operational state of the reactor with a balanced accuracy of over 90%. This result suggests that the utilized methods show promise for application as techniques to verify declared activities involving nuclear reactors. Additional effort is recommended to develop models and architectures that will more fully capitalize on the data’s temporal nature by incorporating the magnetic field’s time dependence to improve the model’s robustness and classification performance. Full article

This paper presents an investigation of the oxidation of Alloy X-750 containing 5 wt% iron in a simulated boiling water reactor (BWR) environment. The specimens were exposed by a water jet (10 m/s) at 286 °C for durations ranging from 2 to 840 h, and the development of the oxide microstructure was mainly studied using electron microscopy. The results showed that the oxide scale consists of blocky crystals of trevorite on top of a porous inner layer rich in Ni and Cr. After the longest exposure time, the trevorite crystals completely covered the specimen surface. The study further revealed that the rate at which the oxide grew and the metal dissolved both decreased with time, and the metal thinning process appeared to be sub-parabolic. Given the significant variation in iron content in the X-750 specification, the influence of this element on the material’s corrosion performance in BWR was examined by comparing the results from this investigation with those from previous work on material containing 8 wt% Fe. The study demonstrates that the oxide growth, metal dissolution and metal thinning were slower in the material with a higher iron content, indicating the importance of this element in limiting the degradation of Alloy X-750 in BWR environments. Full article

The Monte Carlo neutron transport method is used to accurately estimate various quantities, such as k-eigenvalue and integral neutron flux. However, in the case of estimating a distribution of a desired quantity, the Monte Carlo method does not typically provide continuous distribution. Recently, the functional expansion tally (FET) and kernel density estimation (KDE) methods have been developed to provide a continuous distribution of a Monte Carlo tally. In this paper, we propose a method to estimate a continuous distribution of a quantity in all phase-space variables using a fully connected feedforward artificial neural network (ANN) model with Monte Carlo-based training data. As a proof of concept, a continuous distribution of iterated fission probability (IFP) was estimated by ANN models in two distinct fissile systems. The ANN models were trained on the training data created using the Monte Carlo IFP method. The estimated IFP distributions by the ANN models were compared with the Monte Carlo-based data that include the training data. Additionally, the IFP distributions by the ANN models were also compared with the adjoint angular neutron flux distributions obtained with the deterministic neutron transport code PARTISN. The comparisons showed varying degrees of agreement or discrepancy; however, it was observed that the ANN models learned the general trend of the IFP distributions from the Monte Carlo-based training data. Full article

With the ever-increasing role that nuclear power is playing to meet the aim of net zero carbon emissions, there is an intensified demand for understanding the thermal hydraulic phenomena at the heart of current and future reactor concepts. In response to this demand, the development of high-resolution flow analysis instrumentation is of increased importance. One such under-utilised and under-researched instrumentation technology, in the context of fluid flow analysis, is fibre Bragg grating (FBG)-based sensors. This technology allows for the construction of simple, minimally invasive instruments that are resistant to high temperatures, high pressures and corrosion, while being adaptable to measure a wide range of fluid properties, including temperature, pressure, refractive index, chemical concentration, flow rate and void fraction—even in opaque media. Furthermore, concertinaing FBG arrays have been developed capable of reconstructing 3D images of large phase structures, such as bubbles in slug flow, that interact with the array. Currently a significantly under-explored application, FBG-based instrumentation thus shows great potential for utilisation in experimental thermal hydraulics; expanding the available flow characterisation and imaging technologies. Therefore, this paper will present an overview of current FBG-based flow characterisation technologies, alongside a systematic review of how these techniques have been utilised in nuclear thermal hydraulics experiments. Finally, a discussion will be presented regarding how these techniques can be further developed and used in nuclear research. Full article

Molten Salt Reactors (MSRs) have recently gained resurged research and development interest in the advanced reactor community. Several computational tools are being developed to capture the strong neutronics/thermal-hydraulics coupling effect in this special reactor configuration. This paper presents a consistent one-dimensional (1D) multigroup neutron diffusion model for MSR analysis, with the primary aim for fast and accurate calculations for long transients, as well as sensitivity and uncertainty analysis of the reactor. A fictitious radial leakage cross section is introduced in the model to properly account for the radial leakage effects of the reactor. The leakage cross section and other consistent neutronics parameters are generated with the Monte Carlo code Serpent using high-fidelity three-dimensional (3D) models. The accuracy of the 1D consistent model is verified by the reference solution from the Monte Carlo model on the Molten Salt Reactor Experiment (MSRE) configuration. The 1D consistent model successfully reproduced the integrated flux from the 3D model and the reactor multiplication factor k_eff with the error in the range of 95 to 397 pcm (per cent mille), depending on discretized energy group structures. The developed model is also extended to estimate the reactivity loss due to fuel circulation in MSRE. The estimate of reactivity loss in dynamics analysis is in great agreement with the experimental data. This model functions as the first step in the development of a 1D fully neutronics/thermal-hydraulics coupled model for short- and long-term MSRE transient analysis. Full article

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

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