Prediction of multi-parameters of molten salt reactor based on deep learning networks

Accurate prediction of its key operating parameters can not only reveal the operating status of the reactor, but also provide early warning of abnormal working conditions, providing decision support and technical guidance for operators. Therefore, this article conducts predictive research on key parameters of molten salt reactors based on deep learning algorithms, in order to optimize the monitoring of the operating status of molten salt reactors and assist in the decision-making process.

The RELAP5-TMSR program was used to establish a safety analysis model for the molten salt reactor system and generate a dataset. A multi parameter prediction model for the molten salt reactor was established based on deep learning methods such as autoencoder (AE), long short-term memory network (LSTM), and gated recurrent unit (GRU). The robustness of the model under noise was analyzed and optimized.