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 l/ρ v; 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 l-ρ v)]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.
|ジャーナル||Heat and Mass Transfer/Waerme- und Stoffuebertragung|
|出版ステータス||Published - 2010 3|
ASJC Scopus subject areas
- Condensed Matter Physics
- Fluid Flow and Transfer Processes