The internal threat to nuclear security is one of the most serious problems in the physical protection supervision of spent fuel reprocessing plants. Both insiders and nuclear security departments have obvious characteristics of situational decision making and even irrational decision making. Combined with Game theory and RDEU theory, the RDEU Game model of insiders and nuclear security departments was constructed to analyze the existence of equilibrium solutions of two-way strategies under different emotional states. From a dynamic point of view, the influence and change process of emotion on participants’ decision-making behavior were analyzed. Then, the model was numerically simulated to verify its accuracy and effectiveness, which showed that different emotional states and intensities would not only affect the final result of evolutionary equilibrium, but also change the evolution speed of the strategies. In addition, compared with insiders, the intensity of pessimism in the nuclear security department had a greater impact on the game equilibrium. Finally, we present some reasonable recommendations to prevent and protect nuclear security events at spent fuel reprocessing plants by strengthening the emotional supervision and guidance of insiders and the nuclear security department.

Studying the migration and diffusion of radionuclides plays an important role in emergency decision making and accident mitigation of floating nuclear power plants. Based on the CALPUFF model, this paper simulates the spatial distribution and concentration distribution of airborne radionuclides ¹³¹I diffusion under the conditions of sailing and power supply under LOCA (Loss-of-Coolant Accident) of the floating nuclear power plant, and the influence of four meteorological parameters, namely wind speed, cloudiness, temperature and air pressure, on the migration was analyzed using sensitivity analysis. The results show that the wind direction affects the diffusion direction of ¹³¹I, and the concentration of ¹³¹I decreases with the increase in the diffusion distance; under the same conditions, the radionuclides diffuses farther and the affected area is larger under the sailing condition. Wind speed is the dominant factor affecting the diffusion of radionuclides, followed by the cloud amount parameter, temperature parameter, and air pressure parameter. The research results can provide theoretical support for emergency responses to nuclear accidents in offshore floating nuclear power plants.

This paper presents the response of a commercial off-the-shelf complementary metal oxide semiconductor (CMOS) active pixel sensor (APS) module to γ-rays delivered from Cobalt-60 at a high dose rate, and evaluates its performance as a potential high-dose-rate and wide range γ-ray detection device. The experimental results show that the rate of noise generation is not linear with respect to the total ionizing dose, and the radiation damage sustained by the sensor modules exhibits a total ionizing dose threshold. A conservative value of this threshold for the test sensor is 38 Gy. In addition, the average pixel gray level value and the proportion of pixels responding to radiation increase with increasing dose rate, and linear responses are obtained for both in the dose rate range of 5.24 to 69.24 Gy/h. the upper limit of the linear radiation response range depends on the capability of the COTS sensor module, while the lower limit depends on the number of radiation response events in a frame, and the upper limit of the linear response range can be increased by reducing the integration time. This work provides valuable suggestions for adopting CMOS-APSs for high-dose-rate and wide range radiation detection, and it is an important complement to our previous studies focused on the linear response of the CMOS-APS under low-dose-rate irradiation.

Shearing machines are the key pieces of equipment for spent–fuel reprocessing in commercial reactors. Once a failure happens and is not detected in time, serious consequences will arise. It is very important to monitor the shearing machine and to diagnose the faults immediately for spent–fuel reprocessing. In this study, an intelligent condition monitoring approach for spent nuclear fuel shearing machines based on noise signals was proposed. The approach consists of a feature extraction based on wavelet packet transform (WPT) and a hybrid fault diagnosis model, the latter combines the advantage on dynamic–modeling of hidden Markov model (HMM) and pattern recognition of artificial neural network (ANN). The verification results showed that the approach is more effective and accurate than that of the isolated HMM or ANN.