Search Results (39)

Sodium hydride (NaH) is the main by-product generated during the operation of the cold traps in a sodium-cooled fast reactor. Its thermal decomposition releases hydrogen gas, posing a safety hazard. However, understanding the atomic-scale decomposition mechanism remains challenging because conventional simulation methods are limited in the accessible length and time scales. This study developed a deep neural network potential (DP) for the NaH system using the DP-GEN active learning framework. Benchmark tests show that the DP model accurately reproduces density functional theory (DFT) reference energies, forces, equations of state, elastic properties, and phonon spectra. In particular, the DP-predicted bulk modulus and lattice constant are in good agreement with DFT results and close to experimental values, significantly outperforming the empirical ReaxFF potential. Subsequently, we conducted large-scale Deep Potential Molecular Dynamics (DPMD) simulations to investigate the thermal decomposition behavior of NaH clusters and a slab model. The simulation results reveal model-dependent thermal responses of NaH. In the original Na₄₈H₄₈ cluster simulation heated from 100 to 1200 K, a structural transition and disordering were observed, but no H₂ formation occurred within the simulation time. In contrast, the slab model heated from 300 to 1500 K exhibited surface disordering, Na-H bond cleavage, H-H bond formation, cluster detachment, and H₂ formation and release at elevated temperatures. A supplementary higher-temperature Na₄₈H₄₈ cluster simulation further showed cluster dissociation in the 1000–1500 K range. These results provide atomistic insight into the model- and temperature-dependent decomposition behavior of NaH and suggest that the DP model is a useful tool for studying hydrogen-related processes in alkali metal hydrides. Full article

This study presents the radiological characterization of 15 solid radioactive waste samples originating from the BN-350 sodium-cooled fast reactor (Kazakhstan) during its decommissioning phase. The studied materials include components of ventilation and wastewater treatment systems, pipeline elements and various operational tools. The objective of the study was to determine the activity levels of the samples and to assess appropriate pathways for their subsequent management and processing. The results indicate that all studied materials fall within the category of low-level radioactive waste. Gamma spectrometric analysis shows that Cs-137 is the dominant and only identified gamma-emitting radionuclide contributing to the activity of the studied samples. Full article

During the decommissioning of the BN-350 reactor, the spent nuclear fuel (SNF) was transferred to long-term dry storage in TUK-123 transport and storage cask systems designed for transportation and long-term storage with a design service life of approximately 50 years. The TUK-123 system consists of a UKKh-123 storage package, which is a sealed metal-concrete cask (MCC), and a protective-damping cover (PDC) used only during transportation. Radiation characteristics are a key quantitative criterion for assessing the safety of long-term storage in the absence of direct access to fuel and cask components. This paper presents the results of a computational study of the radiation characteristics of BN-350 SNF under dry storage conditions as of 1 January 2025. Spatial distributions of the ambient dose equivalent rate were determined for normal storage conditions and for accident scenarios involving partial failure of fuel assembly (FA) canisters and fuel redistribution. It was established that in the near-field region, the dose fields are formed predominantly by long-lived fission products and activation nuclides, whereas the neutron contribution is determined mainly by the spontaneous fission of actinides and (α, n) reactions. The results obtained provide a quantitative basis for assessing the radiation safety of long-term BN-350 SNF dry storage. Full article

A Decay Heat Removal System (DHRS) is an essential passive safety feature in pool-type Sodium-Cooled Fast Reactors (SFRs), maintaining core temperatures within design limits via natural circulation after reactor scram. Operation of the DHRS is regulated by the damper of the Air Heat Exchanger (AHX), which controls its activation and shutdown. In the current design guidelines, it is typically recommended to initiate the Decay Heat Exchanger (DHX) at 600 s after a Station Blackout (SBO) event. However, this activation timing requires minor dynamic adjustment based on the transient response of the system, which can be obtained by either real-reactor experiments or numerical simulations. Since full-scale real-reactor experiments are not easy to conduct, numerical simulations are effective ways to enhance the passive safety performance of pool-type SFRs under SBO conditions, clarify the regulatory mechanism of DHX activation timing on system behavior, and optimize DHRS operational strategies. This study developed an integrated full-reactor three-dimensional numerical model that comprehensively incorporated key components such as the core, sodium pools, and DHX. Transient variations in power and boundary conditions were precisely controlled via User-Defined Functions (UDFs). The impact of different DHX activation strategies on the reactor’s decay heat removal capability was systematically analyzed. Three-dimensional numerical simulations were performed for three representative DHX operational strategies, immediate activation post-accident (0 s), delayed activation per the standard strategy (600 s), and complete DHX non-activation, yielding detailed temperature and flow field distributions within the reactor. Results demonstrate that under the standard strategy, not only can the temperature in the pool be controlled below the safety limit (550 °C) in the early stage but the temperature can also drop in the subsequent stage while retaining a 600 s safe operation threshold. Notably, the results reveal that “sooner is not always better”. Immediate DHX activation accelerates internal circulation and drives hot fluid downwards, paradoxically heating the cold pool faster than delayed activation, thereby resulting in a higher core outlet temperature. This study contributes to enhancing the credibility of passive safety in SFRs and provides reliable data to support the development of optimized reactor operation protocols. Full article

Potassium superoxide (KO₂) can form during the oxidation of residual potassium in NaK-contaminated cold traps of sodium-cooled fast reactors. Its strong oxidizing nature, combined with limited thermal stability, raises safety concerns during shutdown and maintenance. Here, we integrate first-principles calculations with experiments to clarify the facet stability, temperature-driven surface evolution, and stepwise thermal decomposition of KO₂. Guided by the tetragonal I4/mmm crystal symmetry of bulk KO₂, symmetry-non-equivalent low-index facets and relevant surface terminations were systematically evaluated to identify physically meaningful exposed surfaces. Ab initio molecular dynamics (AIMD) simulations further show that heating induces progressive surface amorphization and enhanced oxygen mobility, accompanied by the emergence of shortened O-O bonds and outward migration of oxygen species. Kinetic analysis using the climbing-image nudged elastic band (CI-NEB) method indicates that oxygen evolution is preferentially mediated by O₂ release rather than atomic oxygen escape. Differential scanning calorimetry (DSC) reveals two endothermic events consistent with sequential decomposition, while X-ray diffraction (XRD) confirms the transformation of KO₂ into K₂O. Collectively, these results provide an atomistic-to-macroscopic understanding of KO₂ decomposition, offering practical guidance for defining safer preheating windows and handling strategies for NaK-contaminated components. Full article

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F₁ score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F₁ scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively. Full article

As a representative Generation IV sodium-cooled fast reactor (Gen-IV SFR), neutron physics characteristics studies of the China Experimental Fast Reactor (CEFR) core are crucial for its safety case. In this study, a three-dimensional core model of the CEFR was developed using the Monte Carlo-based MCNP5 code, with its reliability validated through five neutronics benchmark experiments. Based on this model, the fundamental neutronics characteristics of minor actinide (MA) transmutation in the sodium-cooled fast reactor were investigated. The results demonstrate that as the minor actinide (MA) loading fraction in the core increases from 0% to 8%, the effective multiplication factor (K_eff) exhibits a significantly nonlinear decrease, accompanied by a corresponding reduction in neutron flux, necessitating increased fuel enrichment to maintain core criticality. Opposite impacts on reactivity are observed for different MA nuclides: ²³⁷Np, ²⁴¹Am, ²⁴³Am and mixed MA reduce K_eff, whereas ²⁴⁴Cm and particularly ²⁴⁵Cm significantly enhance K_eff. The reactivity change rate sharply decreased from −1242.5 to −312.7 pcm/wt%, clearly demonstrating saturation effects in MA neutron absorption. Crucially, reactivity remained deeply negative across all operational scenarios, with safety requirements being satisfied even at maximum MA loading levels, confirming the inherent safety of the proposed approach. Full article

A negative value of the void reactivity coefficient (α_V) is one of the most important passive safety properties for the operation of nuclear reactor. Herein, are presented calculated values of the void reactivity coefficient for different geometries of reactors cooled by liquid lead (LFR) and sodium (SFR) with U-238-Pu-239 and Th-232-U-233 fuels. The calculations were carried out for the reactors filled with either one or two types of fuel assemblies. The most interesting results are obtained for reactor filled with two different types of fuel assemblies (hybrid reactor). Hybrid reactors consist of central and peripheral types of fuel assemblies using low enrichment fuel and high enrichment fuel, respectively. Both hybrid reactors based on the uranium cycle (U-cycle) and the thorium cycle (Th-cycle) can maintain a negative void reactivity coefficient value for wide range of reactor parameters. The calculation results of the hybrid reactor matched those from FBR-IME reactor. Full article

This study presents a comprehensive uncertainty and sensitivity analysis of the effective neutron multiplication factor ($k_{\mathrm{eff}}$) in a large-scale sodium-cooled fast reactor (SFR) modeled after the European Sodium Fast Reactor. Utilizing the Serpent Monte Carlo code and the ENDF/B-VII.1 cross-section library, this research investigates the impact of cross-section perturbations in key isotopes (²³⁵U, ²³⁸U, and ²³⁹Pu for both mixed oxide (MOX) and metal fuels. Particular focus is placed on the capture, fission, and inelastic scattering reactions, as well as the effects of fuel temperature on reactivity through Doppler broadening. The findings reveal that reactivity in MOX fuel is highly sensitive to the fission cross sections of fissile isotopes (²³⁹Pu and ²³⁸U, while capture and inelastic scattering reactions in fertile isotopes such as ²³⁸U play a significant role in reducing reactivity, enhancing neutron economy. Additionally, this study highlights that metal fuel configurations generally achieve a higher ($k_{\mathrm{eff}}$) compared to MOX, attributed to their higher fissile atom density and favorable thermal properties. These results underscore the importance of accurate nuclear data libraries to minimize uncertainties in criticality evaluations, and they provide a foundation for optimizing fuel compositions and refining reactor control strategies. The insights gained from this analysis can contribute to the development of safer and more efficient next-generation SFR designs, ultimately improving operational margins and reactor performance. Full article

The computational demand of neutron Monte Carlo transport simulations can increase rapidly with the spatial and energy resolution of tallied physical quantities. Convolutional neural networks have been used to increase the resolution of Monte Carlo simulations of light water reactor assemblies while preserving accuracy with negligible additional computational cost. Here, we show that a convolutional neural network can also be used to upsample tally results from Monte Carlo simulations of sodium-cooled fast reactor assemblies, thereby extending the applicability beyond thermal systems. The convolutional neural network model is trained using neutron flux tallies from 300 procedurally generated nuclear reactor assemblies simulated using OpenMC. Validation and test datasets included 16 simulations of procedurally generated assemblies, and a realistic simulation of a European sodium-cooled fast reactor assembly was included in the test dataset. We show the residuals between the high-resolution flux tallies predicted by the neural network and high-resolution Monte Carlo tallies on relative and absolute bases. The network can upsample tallies from simulations of fast reactor assemblies with diverse and heterogeneous materials and geometries by a factor of two in each spatial and energy dimension. The network’s predictions are within the statistical uncertainty of the Monte Carlo tallies in almost all cases. This includes test assemblies for which burnup values and geometric parameters were well outside the ranges of those in assemblies used to train the network. Full article

The loading and unloading elevators are the primary equipment in the refueling system, used for transferring fuel assemblies in the sodium-cooled fast reactors. The guideway friction pairs are the critical components of these elevators in the refueling system. With the excellent hardness and wear resistance in air, nitrided W18Cr4V steel is a promising material for the guideway friction pairs. In order to assess the feasibility of using nitrided W18Cr4V steel, it is essential to understand the aging behavior of nitrided W18Cr4V steel in high-temperature sodium. Aging tests were conducted on nitrided W18Cr4V steel in sodium and in argon environments at various temperatures for different exposure times. The results showed that the nitrogen atoms in the nitrided layer exhibited bidirectional diffusion behavior in the sodium or argon environment at 540 °C. Compared to the argon environment, cracks formed within the nitrided layer and the diffusion of nitrogen into the sodium was accelerated in the nitrided layer. As a significant number of nitrogen atoms had diffused into the sodium, there was little difference in the hardness between nitrided W18Cr4V steel and non-nitrided W18Cr4V steel after long-term exposure to 540 °C sodium. Full article

In sodium-cooled fast reactors, the wettability of sodium with materials is closely related to sodium-related operations and the detection accuracy of instruments and meters, so how to achieve the selection of materials with different wettability requirements is a key problem in engineering design. To meet these requirements, the wetting behaviors of liquid sodium with nine transition metals were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and molecular dynamics (MD) simulations. The results show that metals such as zinc and gold, which react with sodium to form intermetallic compounds at the interface, exhibit superior wettability. Followed by the metals that have strong interatomic interactions even though they do not react with sodium or dissolve each other, such as cobalt, nickel and copper, while the wettability of these systems tends to be poor at low temperatures. Systems that do not react with each other or have strong interatomic affinities proved to be the most difficult to wet. Notably, metals with the closest-packed crystal structures of fcc and hcp generally have better wettability than those with a bcc structure. They can be a valuable guide for experimental research and technical control. Full article

Investigating tellurium (Te) corrosion on structural materials is crucial for sodium-cooled fast reactors (SFRs) due to radionuclide presence and knowledge gaps. In this study, Type 304/304L stainless steel (SS304), chromium (Cr), iron (Fe), and nickel (Ni) samples were immersed in low-oxygen environments with Te in liquid sodium at 773 K for 30 days. At 10 ppm oxygen, SS304 showed multiple oxide layers, including a compact NaCrO₂ interlayer and porous Na-Fe-Ni-O outer layers. Tellurium penetrated through the porous layers but was hindered by the NaCrO₂ interlayer. At 0.01 ppm oxygen, Cr had no oxide layer, while Fe and Ni had unstable ones. Tellurium-induced pitting was deeper in Fe and Ni compared to Cr. Oxygen levels and Cr composition are critical factors affecting stable oxide compound layer formation and mitigating Te-induced pitting. Full article

For a sodium-cooled fast reactor, the capability for stable cooling and avoiding re-criticality on the debris bed is essential for achieving in-vessel retention when severe accidents occur. However, an unexploited uncertainty still existed regarding the compound effect of the heterogeneous configuration and dynamic particle redistribution for the debris bed’s criticality and cooling safety assessment. Therefore, this research aims to develop a numerical tool for investigating the effects of the different transformations of the heterogeneous configurations on the debris bed’s criticality/cooling assessment. Based on the newly proposed methodology in this research, via integrating the Discrete Element Method (DEM) with Computational Fluid Dynamics (CFD) and Monte-Carlo-based Neutronics (MCN), the coupled CFD–DEM–MCN solver was constructed with the originally created interface to integrate two existing codes. The effects of the different bed configurations’ transformations on the bed safety assessments were also quantitively confirmed, indicating that the effect of the particle-centralized fissile material had the dominant negative effect on the safety margin of avoiding re-criticality and particle re-melting accidents and had a more evident impact than the net bed-centralized effect. This coupled solver can serve to further assess the debris bed’s safety via a multi-physics simulation approach, leading to safer SFR design concepts. Full article

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1. Full article