J. Nucl. Eng., Volume 4, Issue 4 (December 2023) – 5 articles

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