In this paper, the thermal characteristics of an evacuated tube solar collector for different volumetric flow rates of the fluid (10, 30 and 50 l/h) was experimentally improved by using copper oxide/distilled water (Cu2O/DW) nanofluid, and parabolic concentrator. Moreover, the effect of different volume fractions of the utilized nanofluid on the fluid properties, such as convective heat transfer coefficient, Nusselt number, and the useful gain of the collector was experimented. Finally, three artificial intelligence (AI) techniques namely, multi-variate adaptive regression spline (MARS), model tree (MT) and gene-expression programming (GEP) have been employed to predict the energy efficiency (nІ) and inlet-outlet water temperature difference (ΔT). The input variables were volume of the storage tank (V), volume fraction of the nanofluid (VF), and mass flow rate of the fluid (). The proposed AI methods presented robust formulations for prediction of nІ and ΔT with an acceptable level of precision. The statistical results of AI models demonstrated that the MARS method can make a more accurate prediction of the collector performance than GEP and MT. It was also concluded that increase in both flow rate, and concentration of the nanofluid, lead to an increase in the thermal performance of the solar collector.
- Evacuated tube solar collector
- Cu2O/DW nanofluid
- Energy efficiency
- Artificial intelligence techniques
- Regression based equations