哈尔滨工程大学学报2026，Vol.47Issue(7)：1457-1465,9.DOI:10.11990/jheu.202602010
基于贝叶斯神经网络的冷却剂丧失事故不确定性预测方法
Research on bayesian neural network-based uncertainty prediction method for LOCA accident transient conditions
摘要
Abstract
For traditional best estimate plus uncertainty(BEPU)methods are constrained by long computational times and limited sampling sizes.In this regard,a simulation model of AP1000 under cold leg break accident conditions is used by RELAP5 to generate training data.A Bayesian neural network(BNN)is established as a surrogate model for predicting uncertainties in peak cladding temperature(PCT)and core water level variations during loss of coolant accident(LOCA)accidents.Additionally,DAKOTA coupled with RELAP5 is employed for uncertainty analysis to validate the feasibility and advantages of the BNN model in this context.The analytical results demonstrate that:the error between the PCT pre-dicted by the BNN model and the actual value is within 6%,while the error in core water level predictions is below 1%;the training and computational time of the BNN model(5 minutes)is significantly shorter than that of traditional BEPU methods(17 hours),indicating high computational efficiency;the uncertainty analysis results obtained from BNN predic-tions are in close agreement with those from DAKOTA-RELAP5 coupled calculations,confirming the reliability of BNN in uncertainty prediction;using standard deviation to quantify output uncertainty,the magnitude of uncertainty predicted by BNN does not exceed 5%of the mean value,indicating low uncertainty in the established simulation model.The findings of this study provide a reference for subsequent direct application of neural network models in uncertainty prediction.关键词
贝叶斯神经网络/最佳估算加不确定性方法/代理模型/先进压水反应堆/冷却剂丧失事故/包壳峰值温度/堆芯液位/不确定性分析Key words
bayesian neural network/best estimate plus uncertainty(BEPU)/surrogate models/AP1000/loss of coolant accident(LOCA)/the peak cladding tempera-ture/the core liquid level/uncertainty analysis分类
能源科技引用本文复制引用
方嘉辉,国文慧,高新力,章静,王明军,巫英伟,田文喜,苏光辉,秋穗正..基于贝叶斯神经网络的冷却剂丧失事故不确定性预测方法[J].哈尔滨工程大学学报,2026,47(7):1457-1465,9.基金项目
国家自然科学基金面上项目(12175173). （12175173）
