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

Ronghua Chen, Gunaghui Su, Suizheng Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationInternational Conference on Nuclear Engineering, Proceedings, ICONE
Pages689-696
Number of pages8
Volume4
EditionPARTS A AND B
DOIs
Publication statusPublished - 2010
Event18th International Conference on Nuclear Engineering, ICONE18 - Xi'an
Duration: 2010 May 172010 May 21

Other

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

ASJC Scopus subject areas

  • Nuclear Energy and Engineering

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  • Cite this

    Chen, R., Su, G., & Qiu, S. (2010). Analysis of CHF characteristics of concentric-tube open thermosyphon by using artificial neural network. In International Conference on Nuclear Engineering, Proceedings, ICONE (PARTS A AND B ed., Vol. 4, pp. 689-696) https://doi.org/10.1115/ICONE18-29707