Data-Driven-Based Forecasting of Two-Phase Flow Parameters in Rectangular Channel

Frontiers in Energy Research

Yaoyi Zhang¹  Di Chen¹  Zhixin Pang¹  Yafeng Wang¹  Bo Pang¹  Yang Yu²  Qingyu Huang³ 

[1] Chengdu, China;null;[e-mail];

关键词: data-driven method;    two-phase flow;    machine learning;    interfacial area concentration;    random forest regression;   

DOI  :  10.3389/fenrg.2021.641661

来源: Frontiers

【 摘 要 】

In the current nuclear reactor system analysis codes, the interfacial area concentration and void fraction are mainly obtained through empirical relations based on different flow regime maps. In the present research, the data-driven method has been proposed, using four machine learning algorithms (lasso regression, support vector regression, random forest regression and back propagation neural network) in the field of artificial intelligence to predict some important two-phase flow parameters in rectangular channels, and evaluate the performance of different models through multiple metrics. The random forest regression algorithm was found to have the strongest ability to learn from the experimental data in this study. Test results show that the prediction errors of the random forest regression model for interfacial area concentrations and void fractions are all less than 20%, which means the target parameters have been forecasted with good accuracy.

【 授权许可】

CC BY