基于人工神经网络的堆芯两相流型预测模型开发  

Development of Prediction Model for Two-phase Flow Regime in Nuclear Reactor Core Based on Artificial Neural Network

作　　者：马翊超 孔德祥 田文喜[1] 章静[1] 巫英伟[1] 秋穗正[1] 苏光辉[1] Ma Yichao;Kong Dexiang;Tian Wenxi;Zhang Jing;Wu Yingwei;Qiu Suizheng;Su Guanghui(School of Nuclear Science and Technology,Xi’an Jiaotong University,Xi’an,710049,China)

机构地区：[1]西安交通大学核科学与技术学院,西安710049

出　　处：《核动力工程》2025年第2期156-163,共8页Nuclear Power Engineering

基　　金：国家自然科学基金(12175173)。

摘　　要：为了充分利用不断增加的流型实验数据来扩大模型适用范围、提高模型预测精度,本研究收集实验数据建立了训练数据库并对数据进行了预处理,基于人工神经网络(ANN)算法开发了两相流型预测模型,分析了模型对不同方向上流型的预测精度并与传统流型预测模型进行对比。结果表明,建立的新模型对训练集的平均准确率为88.56%,对测试集的平均准确率为87.86%,新模型能直接用于各种不同工况,不会发生不同方向流型混淆的情况,相比于Ishii模型、Mandhane模型、Taitel模型,新模型具有更好的预测精度。本研究为流型预测提供了一种新方法,随着训练数据的更新,模型的适用范围和精度可以不断提高。To fully leverage the increasing experimental data on flow regimes to expand model applicability and improve prediction accuracy,this study collected experimental data,established a training database,and performed data preprocessing.A two-phase flow regime prediction model was developed based on the artificial neural network(ANN)algorithm.The model's prediction accuracy in various flow directions was analyzed and compared with traditional flow regime prediction models.The results show that the new model achieves an average accuracy of 88.56%on the training set and 87.86%on the test set.The proposed model can be directly applied to various operating conditions without causing misclassification of flow regimes in different directions.Compared to the Ishii model,Mandhane model,and Taitel model,the ANN-based model demonstrates superior prediction accuracy.This study provides a novel method for flow regime prediction,and with the continuous updating of training data,the applicability and accuracy of the model can be further improved.

关 键 词：反应堆堆芯 两相流型 机器学习 人工神经网络(ANN) 

分 类 号：TL334[核科学技术—核技术及应用]