Development of correlations for steam condensation over a vertical tube in the presence of noncondensable gas using machine learning approach. (February 2023)

• Record Type: Journal Article
• Authors: Tang, Jiguo; Yu, Shengzhi; Liu, Hongtao
• Is Part Of: International journal of heat and mass transfer. Volume 201:Part 2(2023)
• Publication Date: 2023-02
• Subjects: Condensation -- Noncondensable gas -- Machine learning -- Multi-gene genetic programming; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2022.123609
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: A large database for stream-air condensation heat transfer coefficient was compiled. A new empirical correlation was proposed using the new database with MAE of 17.4%. A new correlation developed using MGGP showed superior predictions with MAE of 14.8%. Abstract: Steam condensation is an important phenomenon encountered in nuclear reactor under severe accidents. Even though many correlations for predicting steam condensation heat transfer coefficient (HTC) in the presence of noncondensable gas (NCG) have been proposed over the past decade, a more reliable and accurate model is still required. Thus, in this study, multigene genetic programming (MGGP), a biologically inspired machine learning method, is applied to develop new correlations for condensation HTC of steam-NCG mixture over a vertical tube in turbulent free convection regime. To this end, a consolidated database with 1440 data points from 18 sources is compiled. Then, using the database, both a new empirical correlation and a MGGP model are developed for better comparison. The performance of the MGGP-based correlation selected using Pareto tournaments strategy is compared with the new developed empirical correlation and another 20 relevant correlations. The results reveal the superiority of the MGGP-based correlation. In addition, it is found that the tube length is excluded in the best-trained correlation, even though it is used as the input of MGGP, which agrees well with the results of previous theoretical and experimental studies. The present study demonstrates that MGGP is promising in developing explicit, accurate, and compact models for the complex heat transfer and multiphase flow phenomena such as steam condensation in the presence of NCG.