基于人工神经网络的竖直向上通道过冷沸腾流孔隙率预测模型与经验关联的性能比较

Ngoc Dat Nguyen, Van Thai Nguyen
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引用次数: 0

摘要

过冷沸腾流中空隙率参数的准确预测对核反应堆堆芯内的质量流量、两相流不稳定的发生以及传热特性都有重要影响,对核安全具有重要意义。在各种输入条件下建立了许多不同的模型和经验相关性;然而,由于模型参数和模型形式的不确定性,这种经典方法的预测结果并不理想。为了应对这些限制,人工神经网络(ANN)是一种强大的机器学习工具,用于建模和解决非线性和复杂的物理问题。因此,本工作旨在建立一个基于人工神经网络的模型来预测过冷沸腾流的局部空隙率。将基于人工神经网络的模型与经验关联模型用于垂直向上通道过冷沸腾的孔隙率预测的性能对比结果表明,基于人工神经网络的模型在计算流体力学(CFD)代码中具有准确模拟过冷沸腾现象的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Comparison of ANN-Based Model and Empirical Correlations for Void Fraction Prediction of Subcooled Boiling Flow in Vertical Upward Channel
The accurate prediction of void fraction parameter in subcooled boiling flow is very important for nuclear safety since it has significant influences on the mass flow rate, the onset of two-phase flow instability, and the heat transfer characteristics in a nuclear reactor core. Many different models and empirical correlations have been established over a variety of input conditions; however, this classical approach could lead to unsatisfactory prediction due to the uncertainties of model parameter and model forms. To cope with these limitations, Artificial Neural Network (ANN) is a powerful machine learning tool for modeling and solving non-linear and complicated physical problems. Therefore, this work is aim at developing an ANN-based model to predict the local void fraction of subcooled boiling flows. The comparison results of the performance between the ANN-based model and empirical correlations for the void fraction prediction of subcooled boiling in vertical upward channel showed the potential use of ANN-based model in the Computational Fluid Dynamics (CFD) codes to accurately simulate the subcooled boiling phenomena.
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