Artificial neural network analysis of the flow of nanofluids in a variable porous gap between two inclined cylinders for solar applications

Copper (Cu) nanoparticles (NPs) and polyvinyl alcohol (PVA) are utilized to enhance heat transfer (HT) which is used in the efficiency of solar energy systems. Copper nanoparticles have excellent thermal conductivity (TC) properties that enable them to conduct heat efficiently. In this arrangement, the gap between the two cylindrical channels is settled for the hybrid nanofluids (HNFs) flow in an inclined position that is favourable to sunlight. The nanomaterials consist of a mixture of PVA and Cu nanoparticles (NPs), to execute HNFs. Solar radiation is present on the hot side of the system. The porous gap between the two channels is considered variable which plays a crucial role in enhancing heat transfer and energy conversion. The varying permeability of the gap is adjusted to control the flow resistance and improve the stability of the system. It is observed that higher porosity allows for better convective heat transfer and reduced pressure drop. The transformed equations are solved through an artificial neural network (ANN) while the control volume finite element method (CVFEM) is also used to handle the governing equations. The Cu and PVA (HNF) improves solar radiation absorption and protects components, ultimately increasing the performance and efficiency of the systems.