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The pressure relief valves are equipped in the primary circuit loop to release steam to prevent high temperature core melting in severe accident. When severe accident occurs, whether the valve can be opened smoothly and worked reliably are crucial for ensuring the safety of the reactor. In this study, the analysis and evaluation of operability and integrity of the pressure relief valve are performed. The 3D thermo-mechanical coupling simulation is conducted for closed gate valve to predict its operability by taking account of the contact nonlinearities, temperature load and mechanical load. Besides, the structural integrity analysis of the fully-open gate valve under transient thermal shock is investigated by transient fluid flow and heat transfer simulation, instantaneous stress analysis and creep analysis. The instantaneous mechanical performances at critical locations and the creep characteristics of the valve during the pressure releasing process are analyzed and then evaluated according to the RCC-MR criterion. The results show that the valve can be opened smoothly, and the integrity with negligible creep and significant creep can be ensured in the studied severe accident case. The systematic evaluation method of the structural operability and integrity in severe nuclear accident is achieved in this work.

Rapid analysis of components in complex matrices has always been a major challenge in constructing sensing methods, especially concerning time and cost. The detection of pesticide residues is an important task in food safety monitoring, which needs efficient methods. Here, we constructed a machine learning-assisted synchronous fluorescence sensing approach for the rapid and simultaneous quantitative detection of two important benzimidazole pesticides, thiabendazole (TBZ) and fuberidazole (FBZ), in red wine. First, fluorescence spectra data were collected using a second derivative constant-energy synchronous fluorescence sensor. Next, we established a prediction model through the machine learning approach. With this approach, the recovery rate of TBZ and FBZ detection of pesticide residues in red wine was 101% ± 5% and 101% ± 15%, respectively, without resorting complicated pretreatment procedures. This work provides a new way for the combination of machine learning and fluorescence techniques to solve the complexity in multi-component analysis in practical applications.