With the development of GEN-IV nuclear reactor technology, the supercritical carbon dioxide (SCO₂) Brayton cycle has attracted wide attention for its simple structure and high efficiency. Correspondingly, a series of research has been carried out to study the characteristics of the cycle. The control flexibility of the power generation system has rarely been studied. This paper carried out a dynamic performance of the 20 MW-SCO₂ recompression cycle based on the Simulink software. In the simulation, the response characteristics of the system main parameters under the disturbances of cooling water temperature, split ratio, main compressor inlet temperature and pressure were analyzed. The results show that the turbine inlet temperature is most affected by the disturbances, with a re-stabilization time of 2500–3000 s. According to the response characteristics of the system after being disturbed, this study proposed a stable operation control scheme. The scheme is coordinated with the main compressor inlet temperature and pressure control, recompressor outlet pressure control, turbine inlet temperature control and turbine load control. Finally, the control strategy is verified with the disturbance of reduced split ratio, and the results show that the control effect is good.

Fouling in heat exchanger tubes is a common problem in the operation of condensers. The deposition of fouling can affect the thermal efficiency and safety of the condenser. Therefore, it is necessary to predict the impact of fouling on time and carry out scientific treatment. Firstly, fault prognosis methods require a significant amount of historical fault data, which is often lacking in practical applications. This paper proposes a method based on dynamically adjusting parameters of the fouling thermal resistance empirical equation to establish a fouling thermal resistance digital twin model. It is combined with simulation tools to rapidly generate a large amount of fault data for the research of prognosis methods. Secondly, in the research of fault prognosis methods, prognosis accuracy relies on establishing a reliable and accurate model that describes the behavior of faults. The uncertainty in the modeling process significantly affects the results. Classic modeling methods do not effectively quantify uncertainty. Therefore, this paper proposes a method that applies differential modeling to predict fouling faults in condensers, automatically obtaining uncertain parameters while establishing a reliable model. By calculating the performance evaluation indicator, the accuracy error indicator of the differential modeling-based prognosis method is further reduced to 0.35. The results demonstrate that this method can provide effective reference opinions for handling fouling faults in condensers.

Nuclear power plant (NPP) is a highly complex engineering system which has typical internal feedback and strong component coupling. With these features, most NPP systems have high risk of radioactive release, which makes it essential to perform fault detection (FD) to the NPP systems. To address this challenge, this paper proposes a FD mechanism named characteristic time‐series convolutional neural network (CT‐CNN) based on principal component analysis (PCA), time‐series analysis, and convolutional neural network (CNN) mechanisms. First, the models of NPP FD system are formulated. Then, the PCA mechanism is applied to extract the features of the NPP system. Next, the time‐series analysis and CNN approaches are applied to realize FD to the NPP system. With the above mechanisms, the proposed approach has not only shown strong stability and become adaptive to different data set, but also preserves both time and state characteristics of the NPP system. In experiment, it shows the proposed approach can achieve better performance in both detection accuracy and variance than the classic back propagation, LSTM method, and standard CNN algorithms. More significantly, its optimal accuracy can be as high as 99.8%.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.