Simulation of nanofluid flow in a solar panel cooling system to investigate the panel's electrical-thermal efficiency with artificial neural network

Background For the last decades, the applications of nanofluids (Nfs), which are a mixture of common fluids and sub-micron-sized particles, are hyper-increased in solar panel (SP) systems. This was due to the enhanced properties of Nfs against common fluids, especially the cooling ability of Nfs. This research aim was to simulate the Nfs flow for different types of Nfs in varied types of SP tubes to study the ability of these Nfs in cooling the SP system. Methods An empirical study is done to examine both the thermal and electrical efficiency of the SP system containing common fluids against Nfs. After gathering empirical data, an artificial neural network (ANN) was trained to find the dataset equation by the 3D-curve fitting method to decrease the simulation time and also empirical study costs. Results The maximum temperatures for 0.1 flow rate-outlet fluid, for X, Y, and Z tubes, for common working fluid-nanofluid are 70.77?C (2pm)-67.56 ?C (2pm), 52.88 ?C (4pm)-81.79?C (2pm), and 56.52 ?C (5pm)-76.47 ?C (2pm), individually. This study proved that using Nfs instead of common working fluids enhances both the electrical-thermal properties of SPs. Also, the estimated equation can be used for further research studies related to applications of Nfs in SP systems.