Numerical simulation and artificial neural network modeling of exergy and energy of parabolic trough solar collectors equipped with innovative turbulators containing hybrid nanofluids

A turbulator, as a parabolic trough collector with internal helical axial fines, is numerically analyzed in this paper. The governing equations are quantitatively evaluated using the finite volume technique and the RNG-k-epsilon turbulence model. This research aims to create a novel absorber tube shape with axial helical fins. Enhancing Nu and decreasing pressure drop (Delta P) lead to an increment in eta, especially at high Reynolds numbers (Re). The thermal and operational features of a Cu - Al2O3/water hybrid nanofluid are investigated for varied volume fractions (1, 3, and 5%) and a Re range of 6000-18,000. As a result, utilizing a greater number of rotations is preferable from the standpoint of thermal fluid dynamics. Case 2 has a higher exergy than case 1; when Re = 5000 and phi = 2%, the maximum exergy of case 2 and case 1 is 0.073 and 0.06%, respectively. An artificial neural network (ANN) was constructed to decrease processing expenses as a powerful apparatus. The turbulator effect, heat transfer sensitivity, pressure drop to Reynolds, and nanoparticle concentration may all be predicted using the train ANN structure. The ANN had an error of 0.51% and 1.46% in low and high turbulence intensity situations, respectively, indicating its great accuracy.