AUTHOR=Dong Xiaomeng , Chen Haoxian , Li Changwei , Yang Ming , Yu Yang , Huang Xi 

TITLE=An Evaluation of the Data-Driven Model for Bubble Maximum Diameter in Subcooled Boiling Flow Using Artificial Neural Networks

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.903464

DOI=10.3389/fenrg.2022.903464

ISSN=2296-598X

ABSTRACT=In the subcooled boiling flow under low-pressure condition, bubble characteristic diameter is of great influence on the surface heat transfer coefficient. However, there still exists large errors in the calculations using traditional mechanistic models or empirical correlations, especially for the wide experimental condition. In this paper, we try to propose a widely appliable data-driven model by using Artificial Neural Networks to predict the bubble maximum diameter and investigate the effect of experimental conditions. After a series of analyses on structural parameters and input parameters, the ANN model is established and validated based on six available experimental databases. The result shows that the relative error is around 14%. Then, uncertainty analysis is carried out for the four experimental conditions and two structural conditions. Results show the measuring accuracy of pressure is one of the most sensitive parameters on the prediction of bubble maximum diameter in the subcooled boiling flow under 1.0MPa, especially for the bubble size larger than 0.5mm. According to the result of uncertainty analysis, a new correlation is proposed for the coefficient C and φ which are used to express the effect of pressure and fluid dynamic. The new correlation works well for all the experimental databases and the error for bubble datasets of large size is also modified. Furthermore, another independent validation with low relative error to 14%. is provided to prove the accuracy of the new correlation.