COOLING PERFORMANCE PREDICTION OF A METAL FOAM INTERNAL HEAT EXCHANGER: AN ARTIFICIAL NEURAL NETWORK APPROACH

Abstract

Although HFC refrigerants have high global warming potential (GWP) values, they are preferred due to their satisfactory cooling performance and A1 fire protection classification. If possible, alternatives of HFC-type refrigerants should be used; if not, they should be used with the least charge value. In this study, the effect of metal foam heat exchanger was investigated to reduce the amount of refrigerant in the refrigeration system. The performance of the metal foam incorporated internal heat exchanger (IHX) was estimated by trained artificial neural networks (ANNs) using the correlations given in the literature, and the results were compared with the experimental data presented in the literature. For the same cooling capacity, a higher performance is achieved by using IHX with metal foam additives. Although the developed correlation has been extracted for IHX, it could be applied for all HE with gas flow.