Numerical Modeling Prediction of Thermal Storage during Discharging Phase, PV- Thermal Solar and with Nanofluids


  • Samuel Sami Howard Research Center for Renewable Energy, Catholic University of Cuenca, Cuenca, Ecuador



PV-Thermal, Thermal storage, Phase discharge materials, Nanofluids, Numerical modeling, model validation.


This study is intended to present a numerical model that was established after the energy conservation equations coupled with the heat transfer equations to predict the discharge behavior of different phase change materials, paraffin under the effect of different operating conditions such as solar radiation, heat transfer fluid, using nanofluids; AI2O3, CuO, Fe304 and SiO2, at different concentrations, and heat transfer fluid temperatures. Besides, the effect of the aforementioned operating conditions on the thermal storage process using PV-Thermal hybrid system and the thermal energy conversion efficiency is presented and discussed.

It has been observed in this study that the nanofluid AI2O3 has the longest discharge duration elapse compared to other nanofluids and water as base heat transfer fluid. The nanofluid Ai2O3 as heat transfer fluid exhibited the longest time compared to other nanofluids and water as base heat transfer fluid. It was also shown that the higher the nanofluid volumetric concentrations, the longer the discharge process duration elapses. The data showed that nanofluid Al2O3 has the highest discharge time at different concentrations compared to the other nanofluids during the three regions solid, mushy, and liquid. The results clearly showed that by adding 5 % Fe304 nanoparticles, the melting time of paraffin could be saved by 16.5% over the water. It is also evident that the higher the heat transfer fluid temperature, the higher the hybrid system efficiency, and nanofluids CuO and SiO2 have the highest hybrid system efficiency compared to other nanofluids and water as heat transfer fluid. Finally, a good agreement has been obtained between the model and experimental data published in the literature.