Combination of support vector regression and artificial neural networks for prediction of critical heat flux

This paper presents a hybrid model that couples ν-support vector regression (ν-SVR) with radial basis function networks (RBFNs) for prediction of critical heat flux (CHF). The hybrid model is achieved in two steps. The first step is to determine the initial architecture and initial weights of the hybrid model by an ν-SVR. The second step is to adjust the initial weights using an annealing robust back propagation (ARBP) learning algorithm. Then the hybrid model is used to predict CHF, which is divided into two parts: prediction of CHF for water flow in vertical round tubes and prediction of dryout type CHF for deionized water upflowing through a narrow annular channel with 0.95 mm gap. The dataset used in this paper is taken from literature. In the first part, prediction of CHF and analysis of parametric trends of CHF are both carried out based on three conditions, fixed inlet conditions, local conditions and fixed outlet conditions. The predicted results agree better with the corresponding dataset than that of ε-SVR. In the second part, the predicted results are in better agreement with the experimental data than that of back-propagation network (BPN) employed in the literature. Therefore, the hybrid model presented in this paper is a potential tool for predicting CHF and has advantages over other methods.