Motivated by the growth of global energy demand and the goal of carbon neutrality, lead-cooled fast reactors, which are core reactor types of fourth-generation nuclear energy systems, have become a global research hotspot due to their advantages of high safety, nuclear fuel breeding capability, and economic efficiency. However, its engineering implementation faces key challenges, such as material compatibility, closed fuel cycles, and irradiation performance of structures. This paper comprehensively reviews the latest progress in the core design of lead-cooled fast reactors in terms of the innovation of nuclear fuel, optimization of coolant, material adaptability, and design of assemblies and core structures. The research findings indicate remarkable innovation trends in the field of lead-cooled fast reactor core design, including optimizing the utilization efficiency of nuclear fuel based on the nitride fuel system and the traveling wave burnup theory, effectively suppressing the corrosion effect of liquid metal through surface modification technology and the development of ceramic matrix composites; replacing the lead-bismuth eutectic system with pure lead coolant to enhance economic efficiency and safety; and significantly enhancing the neutron economy and system integration degree by combining the collaborative design strategy of the open-type assembly structure and control drums. In the future, efforts should be made to overcome the radiation resistance of materials and liquid metal corrosion technology, develop closed fuel cycle systems, and accelerate the commercialization process through international standardization cooperation to provide sustainable clean energy solutions for basic load power supply, high-temperature hydrogen production, ship propulsion, and other fields.

Tritium (³H) is one of the most critical nuclides for environmental monitoring, yet it is challenging to measure. Its high natural mobility and its potential to enter the human body through the food chain underscore the importance of not overlooking the radiation safety risks associated with tritium. The need for the online measurement of tritium at low concentrations is becoming increasingly apparent. This review examines the two principal stages of current measurement methodologies: sample preparation and radiation signal detection. It provides a summary of the tritium sample preparation and detection techniques, highlighting advances in the research with potential applications in online monitoring. The review concludes with an analysis of the issues inherent in the current techniques and offers perspectives on possible technological enhancements and future trajectories for the development of online monitoring systems for trace tritium levels.

Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness.

Data-driven mechanical fault diagnosis has been successfully developed in recent years, and the task of training and testing data from the same distribution has been well-solved. However, for some large machines with complex mechanical structures, such as reciprocating pumps, it is often not possible to obtain data from specific sensor locations. When the sensor position is changed, the distribution of the features of the signal data also changes and the fault diagnosis problem becomes more complicated. In this paper, a cross-sensor transfer diagnosis method is proposed, which utilizes the sharing of information collected by sensors between different locations of the machine to complete a more accurate and comprehensive fault diagnosis. To enhance the model’s perception ability towards the critical part of the fault signal, the local attention mechanism is embedded into the proposed method. Finally, the proposed method is validated by applying it to experimentally acquired vibration signal data of reciprocating pumps. Excellent performance is demonstrated in terms of fault diagnosis accuracy and sensor generalization capability. The transferability of practical industrial faults among different sensors is confirmed.

Nuclear energy is a cornerstone of the global energy mix, delivering reliable, low-carbon power essential for sustainable energy systems. However, the safety of nuclear reactors is critical to maintaining operational reliability and public trust, particularly during accidents like a Loss of Coolant Accident (LOCA) or a Steam Line Break Inside Containment (SLBIC). This study introduces a Bayesian Network (BN) framework used to enhance nuclear energy safety by predicting accident severity and identifying key factors that ensure energy production stability. With the integration of simulation data and physical knowledge, the BN enables dynamic inference and remains robust under missing-data conditions—common in real-time energy monitoring. Its hierarchical structure organizes variables across layers, capturing initial conditions, intermediate dynamics, and system responses vital to energy safety management. Conditional Probability Tables (CPTs), trained via Maximum Likelihood Estimation, ensure accurate modeling of relationships. The model’s resilience to missing data, achieved through marginalization, sustains predictive reliability when critical energy system variables are unavailable. Achieving R² values of 0.98 and 0.96 for the LOCA and SLBIC, respectively, the BN demonstrates high accuracy, directly supporting safer nuclear energy production. Sensitivity analysis using mutual information pinpointed critical variables—such as high-pressure injection flow (WHPI) and pressurizer level (LVPZ)—that influence accident outcomes and energy system resilience. These findings offer actionable insights for the optimization of monitoring and intervention in nuclear power plants. This study positions Bayesian Networks as a robust tool for real-time energy safety assessment, advancing the reliability and sustainability of nuclear energy production.

Aiming at the difficult problem of predicting the running state of the rotor of a Control Rod Drive Mechanism (CRDM) in a floating nuclear reactor, this paper proposes a Remaining Useful Life (RUL) prediction method based on Variational Mode Decomposition and Bidirectional Long Short-Term Memory (VMD-BiLSTM). Firstly, a bench experiment of the CRDM is carried out to collect the full operational cycle (full-stroke) vibration signals of the CRDM. Secondly, the collected data are decomposed based on the VMD, and the typical vibration signals at different stages of the experiment are used to verify this method and comprehensively mine the degradation characteristics. At the same time, the time-frequency domain feature analysis is carried out on the original vibration data, and the changing trends of the extracted features are carefully analyzed. Five feature quantities closely related to the degradation trend of the rotor of the CRDM are screened out, and the corresponding health indicators are constructed in combination with the stroke. Finally, the life prediction of the rotor of the CRDM is realized through the BiLSTM method. Then, the comparison experiments with other methods are carried out, and the experimental results show that the method proposed in this paper has high accuracy and reliability and can effectively solve the RUL prediction problem of CRDM, which provides a strong support to ensure the safe and stable operation of floating nuclear reactors.