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

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, rho (l)/rho (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)/[sigma/g(rho (l)-rho (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.