Predicting the critical heat flux in concentric-tube open thermosiphon: a method based on support vector machine optimized by chaotic particle swarm optimization algorithm

Abstract

This study presents a method based on support vector machine (SVM) optimized by chaotic particle swarm optimization algorithm (CPSO) for the prediction of the critical heat flux (CHF) in concentric-tube open thermosiphon. In this process, the parameters C, e and δ2 of SVM have been determined by the CPSO. As for a comparision, the traditional back propagation neural network (BPNN), radial basis function neural network (RBFNN), general regression neural network (GRNN) are also used to predict the CHF for the same experimental results under a variety of operating conditions. The MER and RMSE of SVM–CPSO model are about 45% of the BPNN model, about 60% of the RBFNN model, and about 80% of GRNN model. The simulation results demonstrate that the SVM–CPSO method can get better accuracy.