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, ρl/ρv, 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, Dhe/[σ/g(ρl−ρv)]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/Di increases. In the influence scope of pressure, the CHF decreases with increasing pressure for R22, while increases with increasing pressure for R113.

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