A novel prediction model of frost growth on cold surface based on support vector machine

Abstract

This paper presents a novel prediction model of frost growth on cold surface based on the support vector machine. The dataset used to develop and validate the presented model is obtained from the public literature. The predicted results are found to be in good agreement with the experimental data, with mean relative error 1.82% for the total heat flux, 2.65% for the frost mass concentration, and 5.15% for the frost thickness. Then, a sensitivity analysis of the frost growth model is used to investigate the effects of the operating condition parameters that influence frost growth. Finally, the total heat flux prediction model is selected as an example to investigate the models’ roughness by adding white noise in the input vectors and output targets of the training set, respectively, and together. The results show that the presented model is very suited to the frost growth prediction with high accuracy and good robust against noise, and accordingly it may help the manufacturer design an effective and energy-saving defrosting control strategy.