Artificial neural network modeling to examine spring turbulators influence on parabolic solar collector effectiveness with hybrid nanofluids

Abstract

Numerical simulation and artificial neural network modeling of turbulent flow inside a pipe equipped with two spring turbulator samples with two different scales and a segmental cross-section have been investigated. Increased heat transfer rate (HTR) due to the use of a spring turbulator is predicted for the TiO2Cu-Water hybrid nanofluid based on the single-phase model, feed-forward artificial neural network (ANN) and fitting method. The role of Reynolds number (Re), scale and volume fraction (ϕ) on Nusselt number (Nu), pressure drop (ΔP), performance evaluation coefficient (PEC), solar collector efficiency (η), and the field synergy principle (FSP), compared to simple pipe, is considered using the finite volume method. The results show that increasing the spring turbulator scale increased the contact surface of the working fluid and the spring turbulator. As a result, the flow turbulence is increased, which leads to better mixing of the nanofluid as the operating fluid of the solar collector absorber pipe. Finally, ANN outputs and fitting results are compared, and it has been observed that the obtained ANN could predict the targets accurately.