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

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, 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%. 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 (d_o,opt). The d_o,opt is correlated with the working fluid and may decrease with the increase of ρ_l/ρ_v. CHF decreases with the increase of L/D_i, 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.