This study explores the factors influencing the cognitive processes of operators in digital nuclear power plants, with a focus on the correlation between these factors and electroencephalogram (EEG) features. Initially, based on expert consultations, seven factors were considered: stress, time, fatigue, procedural complexity, user interface experience, procedural clarity, and efficiency. From these, four were identified as the most crucial for each stage of the cognitive process, highlighting their significant roles in influencing cognitive performance and potentially correlating with distinct EEG characteristics. These were assessed using the fuzzy analytic hierarchy process (FAHP) to determine the weightings of influences across the cognitive stages of monitoring, decision making, and execution. Employing a simulated scenario of a steam generator tube rupture, subjective questionnaires were utilized to gauge participant perceptions of influencer impacts at each stage, calculating human factors fuzzy synthetic values. Concurrently, EEG signals were segmented by operational steps, extracting around 114 features across the time, frequency, and time-frequency domains, which were then dimensionally reduced to 17 principal components via adaptive principal components analysis (APCA). A correlation analysis was performed between the human factors fuzzy synthetic values and the APCA-reduced EEG features of participants. Subsequently, the EEG feature columns of the eight selected participants were used as inputs to construct a transformer-based self-attention network model to evaluate the participants’ human factors fuzzy comprehensive values. The findings confirm the transformer model’s efficacy in assessing these values, evidencing a significant correlation between the EEG features and human factors fuzzy synthetic values. Integrating FAHP with machine learning methodologies, this model proficiently estimated operators’ cognitive states during various cognitive processes, significantly enhancing human-machine interface design and the operational safety and efficiency at nuclear power plants.

An enhanced implementation of the random ray method (TRRM), a stochastic adaptation of the method of characteristics, is presented. This implementation generalizes from a traditional flat source (FS) approximation to a linear source (LS) approximation, and from the standard isotropic scattering approximation to an anisotropic approximation, up to P3. On the two-dimensional C5G7 benchmark, LS alone enables a 1.4× and 1.7× enhancement in run time for parallel and serial executions, respectively, without compromising accuracy. Further testing on the three-dimensional C5G7 Rodded B benchmark demonstrates that LS enables significant axial coarsening and reduction in ray populations. This results in a 5.3× speedup in run time while still offering comparable accuracy. On the Babcock and Wilcox 1484 Core II benchmark, incorporating anisotropic scattering in TRRM up to P3 with a FS decreased keff errors (~110 pcm) and maximum and average pin power errors. LS with anisotropic scattering (LSPN), showed similarly improved keff errors while benefiting from a coarser mesh. The linear isotropic flat anisotropic (LIFA) method, relative to LSPN, offers further run time and memory savings of 4.1× and 1.6×, respectively, for P3, while maintaining comparable accuracy to anisotropic FS on more refined meshes.

Nuclear performance analysis is of great significance for fuel design optimization and the safe operation of a reactor. Considering the design requirements of the China initiative Accelerator Driven System (CiADS) project, it is necessary to develop relevant fuel performance analysis tools to predict fuel performance evolution during service. The Fuel Element Analysis Tool for Undercritical Reactor Engineering (FEATURE) code, based on the advanced multiphysics coupling platform COMSOL software, is under development in this context, with a current focus on oxide fuel for fast reactors. This paper mainly conducts preliminary comparative verification of the oxygen redistribution, plutonium redistribution, and fission gas release modules of the FEATURE code through model examples, code-to-code evaluation, and experimental data. The results indicate that the FEATURE code exhibits good accuracy and reliability in simulating oxygen and plutonium redistribution, as well as fission gas release, making it suitable for integrated coupling calculations.

This work aims to prepare a new series of polyepoxide resins reinforced with a mixture of WO3 and Bi2O3 compounds. The effect of replacing WO3 with by Bi2O3 on the radiation shielding parameters was experimentally evaluated using a NaI (Tl) detector and many radioactive sources, including 22Na, 133Ba, 137Cs, 60Co, and 152Eu. The linear attenuation coefficient for the prepared new polyepoxide-based composites was improved when substituting increasing amounts of WO3 with Bi2O3. When increased from 2.164 to 2.943 cm−1 (at 33 keV) and from 0.073 to 0.104 cm−1 (at 1332 keV), the Bi2O3 concentration in the prepared composites varied from 0 to 10 wt%, respectively. The substitution of WO3 with Bi2O3 greatly improved the shielding parameters of the fabricated composites. The half-value thickness, transmission factor, and lead-equivalent thickness were observed to decrease with increasing concentrations of Bi2O3 in the fabricated composites.

Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. The objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.

According to the International Atomic Energy Agency’s requirements for the minimization of radioactive waste, nuclear facility components need to be cut into smaller blocks and packaged in waste containers during decommissioning. Therefore, it is important to increase the space utilization (SU) of the cut blocks in the containers while reducing the cutting time (CT). In engineering practice, increasing SU often leads to longer CTs, which poses a great challenge to improve the efficiency of the nuclear decommissioning process. To overcome this challenge and to determine the optimal operational parameters, this paper introduces a multi-objective optimization design method for cutting and packaging nuclear facility components. The goal is to increase SU while concurrently reducing CTs, ultimately minimizing the waste and reducing the decommissioning cost. First, we project the intricate three-dimensional (3D) irregular parts packing problem onto a two-dimensional plane to simplify the complexity inherent in 3D parts packing and to formulate a packing optimization model. Second, we employ the Morris method to perform global sensitivity analysis on critical parameters, including cutting angle, height, component radius, and plate thickness parameters, throughout the cutting process of nuclear facility components. This analysis produces global sensitivity indicators for each parameter, facilitating a precise assessment of the sensitivity values associated with different operational parameters. Finally, we formulate a multi-objective design optimization model for cutting and packing nuclear facility components that yields a series of alternative operating parameter solutions upon solving it. This methodology offers a resolution for the selection of optimal operational parameters in the decommissioning process of nuclear facility components, thereby attaining optimal outcomes in waste disposal and furnishing guidance for subsequent decommissioning activities.

An approximate method for determining the maturity time is presented for applications in low neutron source nuclear reactor startup simulations. This new method relies only on the calculation of the mean neutron density and does not require the additional calculation of the variance in the neutron density as the traditional method does. The most accurate method for determining the safe neutron source strength, required to sufficiently mitigate the probability of a rogue transient during nuclear reactor startup, uses the Pál-Bell equations. However, as space and energy dependencies are included, the numerical computation become computationally demanding. Therefore, approximate methods that significantly reduce the computation time and improve the computational efficiency of the simulation while remaining very accurate are extremely useful. The approximate method for determining the maturity time presented in this study has shown excellent agreement with traditional methods while offering an order of magnitude reduction in computation time.

In iterative Monte Carlo calculations for nuclear reactors, the inactive cycles should be calculated first to ensure that the source distribution is converged, and then the tallies of various parameters in the active cycles can begin. In order to acquire the mesh-free distribution of the fission source, this research proposes the functional expansion tallies (FET) source convergence diagnosis method in the Reactor Monte Carlo code, which is a self-developed stochastic simulation code maintained by the Reactor Engineering Analysis Laboratory of Tsinghua University. Due to the randomness in Monte Carlo calculations and the difficulty in determining the precise source convergence, this paper proposes a diagnostic tool based on function curve similarity and moving average, and proposes an online real-time source convergence diagnosis method. The FET online source convergence method can terminate the calculation of the inactive cycle in real time according to the convergence diagnostic tool; thus it can greatly decrease the calculation time. The precise and effective transfer of data between different meshes is a difficult issue of thermal and physical coupling. Converting two separate meshes and transferring the data are exceptionally difficult and complex tasks within the conventional nuclear thermal-physics coupling approach. By applying the FET method to nuclear thermal-physics coupling, the mesh-free continuous-space fission source distribution can be obtained, which is suitable for more complex meshes. Additionally, computational memory can be minimized by replacing (transforming) the data from numerous mesh power distribution data points with the coefficients of the function.

In probabilistic risk assessment (PRA) of nuclear power plants (NPPs), human reliability analysis (HRA) is conducted to identify potential human failure events that could contribute to risk scenarios and estimate human error probabilities. Lessons learned from the 2011 Fukushima Daiichi NPP accident underscored that for PRA, it is critical to model external control room (Ex-CR) human actions. The state-of-practice HRA methods, historically developed for the main control room HRA, are limited in capturing the unique nature of Ex-CR human actions, such as location dependence (in addition to the time dependence) of human actions and spatiotemporal interactions of human performance with the surrounding physical environments, for instance, hazard propagation. To advance the Ex-CR HRA in the context of the fire PRA for NPPs, the authors’ team developed a simulation-based fire crew performance model using an agent-based modeling (ABM) technique. The ABM fire crew simulation was coupled with a fire progression model through a spatiotemporal interface using a geographic information system. This paper focuses on the validation of the ABM simulation, which is the key requirement for the simulation-based Ex-CR human performance model to be utilized in PRA. The existing validation approach, initially developed for physical models in the fire PRA of NPPs, is extended for validation of the simulation-based Ex-CR human performance model. Model uncertainty is used as a measure of model validity, which facilitates the incorporation of the validation result into the PRA. The degree of the model uncertainty is characterized by a lognormal error model whose parameters are quantified based on a pairwise comparison between empirical data and model predictions. The proposed validation approach is demonstrated using a case study of the fire PRA of NPPs. This study makes two research contributions: (1) it is the first to validate the simulation-based Ex-CR human performance model against empirical human performance data and incorporate the validation result into the PRA of NPPs, and (2) this study, for the first time, conducts a controlled experimental test to collect empirical data for fire crew performance at NPPs.

A nuclear reactor’s design and safety assessment relies on a calculation platform consisting of a series of calculations performed using different simulation tools, each dedicated to modeling a specific phenomenon. The European Union H2020 CAMIVVER Work Package 4 aims to establish lattice neutronics calculation methodologies for VVER and pressurized water reactor fuel assemblies employing the new-generation deterministic multipurpose neutron transport code APOLLO3®, developed by the CEA (Commissariat à l’Énergie Atomique et aux Énergies Alternatives) with the support of EDF (Electricité de France) and Framatome. The present work aims to present NEMESI, an industrial prototype of a flexible lattice calculation tool developed as part of the CAMIVVER project, showing the applicability of APOLLO3 for industrial research and development and proposing dedicated VVER calculation schemes. Given the intense focus on the industrial issues of the entire CAMIVVER project, the elements constituting the rationale behind the development of such a computational platform are flexible modeling and analysis options, compliance with a series of specified requirements, implementation of innovative algorithms with improved precision, and a modern software and architectural base.