Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm

R. H. Chen, G. H. Su, S. Z. Qiu, Kenji Fukuda

研究成果: Article査読

11 被引用数 (Scopus)

抄録

In this paper, an artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosiphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρ lv; the ratio of the heated tube length to the inner diameter of the outer tube, L/D i; the ratio of frictional area, d i/(D i + d o); and the ratio of equivalent heated diameter to characteristic bubble size, D he/ [σ/g(ρ lv)]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%. New correlations for predicting CHF were also proposed by using genetic algorithm (GA) and succeeded to correlate the existing CHF data with better accuracy than the existing empirical correlations.

本文言語English
ページ(範囲)345-353
ページ数9
ジャーナルHeat and Mass Transfer/Waerme- und Stoffuebertragung
46
3
DOI
出版ステータスPublished - 2010 3

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Fluid Flow and Transfer Processes

フィンガープリント 「Prediction of CHF in concentric-tube open thermosiphon using artificial neural network and genetic algorithm」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル