Effects of various types of nanomaterials on PCM melting process in a thermal energy storage system for solar cooling application using CFD and MCMC methods

被引:53
作者
Alazwari, Mashhour A. [1 ]
Algarni, Mohammed [2 ]
Safaei, Mohammad Reza [3 ,4 ]
机构
[1] King Abdulaziz Univ, Fac Engn, Mech Engn Dept, Jeddah 21589, Saudi Arabia
[2] King Abdulaziz Univ, Fac Engn Rabigh, Mech Engn Dept, POB 344, Rabigh 21911, Saudi Arabia
[3] Florida Int Univ, Dept Mech Engn, Miami, FL 33199 USA
[4] China Med Univ, China Med Univ Hosp, Dept Med Res, Taichung, Taiwan
关键词
Thermal energy storage; Nano-enhanced phase-change materials (NePCM); Latent heat storage; Shell and tube heat exchanger; Bayesian modeling; Markov chain Monte Carlo simulation; PHASE-CHANGE MATERIALS; HEAT-TRANSFER; BAYESIAN PREDICTION; D-MANNITOL; NATURAL-CONVECTION; MULTIPLE PCMS; TUBE; PERFORMANCE; SUSPENSIONS; FLOW;
D O I
10.1016/j.ijheatmasstransfer.2022.123204
中图分类号
O414.1 [热力学];
学科分类号
摘要
Latent heat thermal energy storage (LHTES) systems are attractive for bridging the energy supply and de-mand gap. In such systems, reducing storage time is critical, especially for solar applications. Accordingly, this study mainly aims to employ various nano-additives, including metal (Ag and Cu) and metal-oxide (Al2O3, CuO, and TiO2) nanoparticles and carbon-based nanomaterials (GNP, MWCNT, SWCNT), to improve the thermophysical properties of pure phase change materials (PCM) to accelerate the melting process. For this purpose, the energy storage performance was numerically analyzed in a vertical shell and tube LHTES unit where D-mannitol was utilized as the PCM on the shell side. Dynaleneht was employed as a heat transfer fluid (HTF) in the tube. Using computational fluid dynamics (CFD) modeling, transient variations in liquid fraction, PCM temperature, and total melting time were investigated under the impact of the following parameters: the thermophysical properties and volume fraction of nanomaterials, Re and the inlet temperature of HTF. In addition, a methodology based on Bayesian inference was adopted by coding the Bayesian MCMC simulation to create proper models for predicting the melting time. The numerical results showed that adding carbon-based nanomaterials to pure PCM reduced the melting time by about 50%, while metal nanoparticles impaired the melting performance. It was also observed that adding metal oxide nanoparticles did not add any essential advantage to the LHTES system. This research will help design TES applications in the operating temperature range of 160-200 degrees C, especially in solar cooling systems. (C) 2022 Elsevier Ltd. All rights reserved.
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页数:19
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