Accident parameter prediction method for lead-bismuth cooled reactor based on a multivariate LSTM network coupled with an optimization algorithm

Accurate acquisition of key parameters of lead-bismuth cooled reactors under accident conditions is a prerequisite for reactor safety analyses. In this work, four optimization algorithms(Particle Swarm Algorithm, Genetic algorithm, Quantum genetic algorithm, Whale optimization algorithm) are used to improve the forecast efficiency of a long short-term memory (LSTM) neural network using hyperparameter optimization, and a method is developed to predict the parameters of a lead-bismuth cooled reactor (MARS-3) during a loss of flow accident. A comprehensive evaluation of the proposed method is performed using the TOPSIS technique based on the data samples generated using the sub-channel code SUBCHANFLOW. The results show that the prediction performance of the multivariate LSTM neural network coupled with the particle swarm optimization method is optimal, and its computational efficiency is 438 times that of SUBCHANFLOW. Overall, the study findings can help improve the prediction efficiency for key thermal parameters of lead-bismuth cooled reactors and enhance the emergency response capability of such reactors.