Analysis of CHF characteristics of concentric-tube open thermosyphon by using artificial neural network

Ronghua Chen*, Gunaghui Su, Suizheng Qiu

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

An artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosyphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρlv, the ratio of the heated tube length to the inner diameter of the outer tube L/Di, the ratio of frictional area, di/(Di + do), and the ratio of equivalent heated diameter to characteristic bubble size, D he/[σ/g(ρlv)]0.5, the output is Kutateladze number, Ku. The predicted values of ANN are found to be in reasonable agreement with the actual values from the experiments with a mean relative error (MRE) of 8.46%. For a particular outer tube, the CHF increases initially and then decreases with increasing inner tube diameter, and has a maximum at an optimum diameter of inner tube (do,opt). The do,opt is correlated with the working fluid and may decrease with the increase of ρlv. CHF decreases with the increase of L/Di, and the decreasing rate decreases as L/D i increases. In the influence scope of pressure, the CHF decreases with increasing pressure for R22, while increases with increasing pressure for R113.

本文言語English
ホスト出版物のタイトルInternational Conference on Nuclear Engineering, Proceedings, ICONE
ページ689-696
ページ数8
4
PARTS A AND B
DOI
出版ステータスPublished - 2010
イベント18th International Conference on Nuclear Engineering, ICONE18 - Xi'an
継続期間: 2010 5 172010 5 21

Other

Other18th International Conference on Nuclear Engineering, ICONE18
CityXi'an
Period10/5/1710/5/21

ASJC Scopus subject areas

  • 原子力エネルギーおよび原子力工学

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