期刊
ENERGY REPORTS
卷 8, 期 -, 页码 10203-10218出版社
ELSEVIER
DOI: 10.1016/j.egyr.2022.07.178
关键词
Phase change material; Metal foam-fin hybrid structure; Inclination angle; Numerical simulation; Artificial neural network
资金
- National Natural Science and Hong Kong Research Grant Council Joint Research Funding Project of China [51861165105]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [51721004]
- Research Grants Council of Hong Kong, China
- National Natural Science Foundation of China [N_PolyU513/18]
This study investigates the effects of metal foam-fin hybrid structure and inclination angle on the phase change process using numerical simulation. The results show that the optimized heat transfer performance of the metal foam-fin hybrid structure can reduce melting time and increase heat transfer efficiency. Increasing the number of fins also improves heat transfer performance and reduces heat accumulation. In addition, artificial neural network predictions provide accurate results for liquid fraction and average Nusselt number during the phase change process.
Improving the heat transfer performance of phase change material (PCM) plays a crucial role in designing efficient latent heat thermal energy storage (LHTES) systems. The purpose of this study is to address and elucidate the effects of the metal foam-fin hybrid structure and the inclination angle on the phase change process by using the numerical simulation method. An experimental system for the validation of the numerical models is established. The solid-liquid phase interfaces, streamlines, liquid fraction (f), the dimensionless time (Fo x Ste), and average Nusselt number ((Nu) over bar) of PCM in the container enclosure at inclination angles of 0 degrees, 30 degrees, 60 degrees, and 90 degrees with six kinds of enhanced heat transfer structures, including fin, metal foam, and metal foam-fin hybrid structures, are compared. Besides, the liquid fraction and (Nu) over bar during the phase change process are predicted by the artificial neural network (ANN). Results demonstrate that the optimized heat transfer performance of the metal foam-fin hybrid structure could reduce the melting time. In addition, the increase in the number of fins can improve the heat transfer performance and reduce heat accumulation in the top area with the inclination angle increasing. Compared to pure PCM at the inclination angle of 90 degrees, the values of Fo x Ste of metal foam-1 fin and metal foam-5 fins hybrid structures are reduced by 52.69% and 60.02%, respectively. However, the energy storage density per unit volume decreases as a function of the increasing inclination angles and the number of fins within a case. Furthermore, the excellent predictions of f and (Nu) over bar are obtained by ANN with MSE and R-2 of 9.6480 x 10(-5), 0.9990 and 0.0150, 0.9937, respectively. (C) 2022 The Authors. Published by Elsevier Ltd.
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