Performance enhancement of latent thermal energy system under alternating magnetic field

The functionality of latent thermal energy storage is significantly constrained by the low thermal conductivity of phase change materials (PCMs). Various methods focusing on conduction enhancement have been proposed to improve the heat transfer rate. However, the methods weaken the heat transfer in some cases, due to the significant suppression of convection. In this study, an enhancement technique for the PCM melting based on convection intensification is proposed. Magnetic particles driven by an alternating magnetic field in liquid PCM reciprocate between the heating surface and the solid-liquid interface, inducing the forced convection in liquid PCM. To validate the method, an experimental setup of PCM melting in the bottom-heated cylinder is constructed, and heat transfer characteristics of the PCM under alternating magnetic field are investigated. The results indicate that the proposed method reduces the total melting time of PCM by 15.8% when the heating surface temperature is 55 degrees C, and the enhancement effect is comparatively slight at first and becomes gradually remarkable. The maximum increment of the overall heat transfer coefficient between the heating surface and the solid-liquid interface is 25.4%. Meanwhile, both a higher alternating magnetic field frequency and a larger particle mass fraction create a more evident enhancement within the range of this study. Besides, a universal correlation with a relative deviation of +10% is obtained for the prediction of Nusselt number.

Automated summary

This study proposes an enhancement technique for PCM melting based on convection intensification, where magnetic particles driven by an alternating magnetic field induce forced convection in liquid PCM. Experimental results show a total melting time reduction of 15.8% and a 25.4% increment in overall heat transfer coefficient between the heating surface and solid-liquid interface. A universal correlation with a relative deviation of +10% is obtained for Nusselt number prediction.