Analysis of CHF in saturated forced convective boiling on a heated surface with impinging jets using artificial neural network and genetic algorithm

In this paper, a three-layer Back Propagation (BP) algorithm artificial neural network (ANN) for predicting critical heat flux (CHF) in saturated forced convective boiling on a heated surface with impinging jets was trained successfully with a root mean square (RMS) error of 17.39%. The input parameters of the ANN are liquid-to-vapor density ratio, ρl/ρv, the ratio of characteristic dimension of the heated surface to the diameter of the impinging jet, L/d, reciprocal of the Weber number, 2σ/ρ_lu²(L − d), and the number of impinging jets, N_j. The output is dimensionless heat flux, qco/ρvHfgu. Based on the trained ANN, the influence of principal parameters on CHF has been analyzed as follows. CHF increases with an increase in jet velocity and decreases with an increase in L/d and N_j. CHF increases with an increase in pressure at first and then decreases. Besides, a new correlation was generalized using genetic algorithm (GA) as a comparison with ANN to confirm the advantage of ANN.