New horizons in magnetic refrigeration using artificial intelligence

Abstract

A number of magnetic refrigeration prototypes have been developed in recent years. Nevertheless, their optimization remains a challenging process. This paper presents an efficient approach based on artificial intelligence for optimizing the cooling performance of multi-layer active magnetic regenerators. To this end, a validated numerical model was used to predict the temperature difference between the hot and cold sources of a four-layer active magnetic regenerator. By using a nanofluid as a heat transfer fluid, the maximum temperature span of the device can be increased by approximately 20%. More importantly, by simultaneously optimizing a set of 10 key parameters, including the geometric parameters and the working conditions, the thermodynamic performance of the four-layer active magnetic regenerator prototype can be markedly enhanced by almost 40%. The newly established approach will be of considerable practical importance to both scientists and engineers since it will enable them to avoid costly experimental trials by optimizing a wide number of parameters.