上海科技大学知识管理系统

[Background] Molten salt reactors (MSRs) are fourth-generation advanced nuclear energy systems that exhibit characteristics such as high safety, high economy, nonproliferation, and sustainability. To ensure the safe operation of MSRs, identifying transient conditions promptly and accurately is crucial. However, current system transient identification methods rely on manual identification by operators, introducing significant human factors seriously affecting nuclear power safety. [Purpose] This study aims to establish a transient identification model for an MSR system based on the K-nearest neighbor (KNN) method, so as to reduce human factors introduced during the traditional system transient identification process, and improve the operational safety of the MSR. [Methods] Datasets for the system transient identification model were generated by using the RELAP5-TMSR code to simulate 11 operating conditions of the molten salt reactor experiment (MSRE) built and operated at Oak Ridge National Laboratory in the United States. Subsequently, a system transient identification model based on the KNN method was developed by training, optimizing, and validating these datasets. Four metrics, i.e., accuracy, precision, recall, and F1-score were applied to evaluating the system transient identification model. Finally, the robustness of the model was tested and optimized under noisy conditions. [Results] The results demonstrate that the KNN-based transient identification model for the MSR system achieves a 99.99% F1-score on the test datasets. The system transient identification model also exhibits high robustness, with an F1-score of 94.32% under noisy conditions. The optimized system transient identification model achieves a 99.73% F1-score when identifying transient conditions under noise, accurately identifying the transient conditions of the MSRE. [Conclusions] The KNN-based transient identification model for the MSR system can satisfy the requirements of transient identification of the MSR system, hence be applied to intelligent MSR operations and maintenance, ensuring safe MSR operation. © 2023 Science Press. All rights reserved.