Research on Prediction of Subcooled Flow Boiling CHF for Spiral Flow Based on Machine Learning

被引：0

作者：

Yan J. ^{[1
]}

Zheng S. ^{[1
]}

Guo P. ^{[1
]}

Zhao L. ^{[2
]}

Wang S. ^{[1
]}

Liu K. ^{[1
]}

Zhu X. ^{[1
]}

机构：

[1] State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an

[2] School of Energy & Architecture, Xi’an Aeronautical Institute, Xi’an

来源：

Hedongli Gongcheng/Nuclear Power Engineering | 2023年 / 44卷 / 03期

关键词：

Critical heat flux (CHF); Heat transfer; Machine learning; Subcooled boiling; Two-phase flow;

D O I：

10.13832/j.jnpe.2023.03.0065

中图分类号：

学科分类号：

摘要：

Subcooled boiling is widely used in cooling applications with high heat fluxes represented by International Thermonuclear Experimental Reactor (ITER). In this paper, predictions on the critical heat flux (CHF) of subcooled water boiling under high heat flux and swirl flow conditions are focused and a database of subcooled boiling CHF is established. Machine learning methods are applied, in which four typical machine learning models are adopted, namely Back Propagation (BP) neural network, Genetic Algorithm (GA)-BP neural network, Radial Basis Function (RBF) neural network and Extreme Learning Machine (ELM). The results indicate that machine learning models can effectively predict subcooled boiling CHF with swirling flow, and the prediction performances are obviously promoted, comparing with traditional empirical correlations. Among typical machine learning models, the ELM model possesses the best performance, with the MAE and RMSE are equal to 2.79% and 4.22%, respectively. The results provide a new path to making accurate predictions on the CHF for subcooled boiling under high heat flux and swirling flow conditions. © 2023 Yuan Zi Neng Chuban She. All rights reserved.

引用

页码：65 / 73

页数：8

共 31 条

[11] YAN J G, BI Q C, ZHU G, Et al., Critical heat flux of highly subcooled water flow boiling in circular tubes with and without internal twisted tapes under high mass fluxes, International Journal of Heat and Mass Transfer, 95, pp. 606-619, (2016)
[12] 41, 10, pp. 2479-2483, (2020)
[13] 41, 2, pp. 160-167, (2020)
[14] LIANG X, XIE Y Q, DAY R, Et al., A data driven deep neural network model for predicting boiling heat transfer in helical coils under high gravity, International Journal of Heat and Mass Transfer, 166, (2021)
[15] PARK H M, LEE J H, KIM K D., Wall temperature prediction at critical heat flux using a machine learning model, Annals of Nuclear Energy, 141, (2020)
[16] QIU Y, GARG D, KIM S M, Et al., Machine learning algorithms to predict flow boiling pressure drop in mini/micro-channels based on universal consolidated data, International Journal of Heat and Mass Transfer, 178, (2021)
[17] 42, 4, pp. 91-95, (2021)
[18] GAMBILL W R, BUNDY R D, WANSBROUGH R W., Heat transfer, burnout, and pressure drop for water in swirl flow through tubes with internal twisted tapes: ORNL-2911, (1960)
[19] TONG W, BERGLES A E, JENSEN M K., Critical heat flux and pressure drop of subcooled flow boiling in small-diameter tubes with twisted-tape inserts, Journal of Enhanced Heat Transfer, 3, 2, pp. 95-108, (1996)
[20] HATA K, MASUZAKI S., Heat transfer and critical heat flux of subcooled water flow boiling in a SUS304-tube with twisted-tape insert, Journal of Thermal Science and Engineering Applications, 3, 1, (2011)

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