耦合多变量LSTM与优化算法的铅铋反应堆事故参数预测方法研究

Study on Prediction Method for Accident Parameters of Leadbismuth Reactor Based on Coupling Multivariable LSTM and Optimization Algorithm

准确预测铅铋反应堆事故工况下关键参数是反应堆安全分析的重要内容,对于提高事故工况下反应堆的安全性有重要意义。本文使用优化算法对长短期记忆(LSTM)神经网络超参数优化来提高网络的预测性能,提出了一种基于多变量LSTM神经网络耦合优化算法的参数预测方法。针对铅铋反应堆MARS-3在无保护失流事故(ULOF)工况下的参数预测问题,通过子通道程序SUBCHANFLOW生成数据样本后,使用逼近理想解排序(TOPSIS)法对所述方法进行综合评价。结果表明,多变量LSTM神经网络耦合粒子群算法的预测性能是最优的,其计算效率可以提升至SUBCHANFLOW的438倍。相关研究成果有助于提高铅铋反应堆关键热工参数预测效率,提高铅铋反应堆的事故应急处置能力。

Accurate prediction of key parameters of lead-bismuth reactor under accident conditions is an important content of reactor safety analysis,which is of great significance to improve the safety of the reactor under accident conditions.In this work,an optimization algorithm is used to improve the prediction performance of the Long Short Term Memory(LSTM) neural network by hyperparameter optimization,and a parameter prediction method based on the coupled optimization algorithm of multivariate LSTM neural network is proposed.For the parameter prediction problem of lead-bismuth fast reactor MARS-3 under unprotected loss of flow accident conditions,a comprehensive evaluation of the proposed method is performed using Technique for Order Preference by Similarity to Ideal Solution(TOPSIS) method after data samples generated by 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 can be improved to 438 times that of SUBCHANFLOW.The relevant research results can help improve the efficiency of predicting key thermal parameters of lead-bismuth reactors and improve the emergency response capability of lead-bismuth reactors.