Energy and exergy evaluation of the evacuated tube solar collector using Cu₂O/water nanofluid utilizing ANN methods

In this paper, the improvement of 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 enhanced by using copper oxide/water (Cu₂O/W) nanofluid, and a parabolic concentrator. Moreover, the effect of different volume fractions of the utilized nanofluid on the energy and exergy efficiencies, convective heat transfer coefficient, Nusselt number, and useful heat gain of the solar collector was experimented. Finally, the accuracy of the experimentations was verified via Artificial Neural Networks (ANNs). The Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) models were investigated to predict the performance of the constructed tubular collector, and their results were compared to one another. The results demonstrated that the MLP method can make a more accurate prediction of the collector performance than the RBF one. The highest error rate for the MLP model was less than that of the RBF model. It was also concluded that the increase in both the flow rate and concentration of the nanofluid leads to an increase in the thermal performance of the solar collector.