Computational analysis of turbulent flow characteristics in nanofluids containing 1-D and 2-D carbon nanomaterials: grid optimization and performance evaluation

1D and 2D carbon nanomaterials such as multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were investigated numerically. The thermophysical properties of water and nanofluids using MWCNTs in different outer diameters (ODs) and GNPs in different surface areas (SSA) were measured at an inlet temperature of 303.15 K and 0.1wt.%. The 3D geometry was solved under a fully developed turbulent flow of 6000 ≤ Re ≤ 16,000 using the model of k-ω SST via (ANSYS FLUENT 2022R2) software. Four numerical networks, Polyhedra, Polyhexacore, Hexacore, and Tetrahedral, were optimized. Moreover, seven parameters were discussed, namely wall surface temperature (T_w), heat transfer coefficient (h_tc), average Nusselt number (Nu_avg), friction factor (f), pressure drop (ΔP), and total thermal performance index (PI_th). Polyhexacore was the main grid over Polyhedra, Hexacore, and Tetrahedral with the average error (Dittus-Boelter: 2.754%, Gnielinski: 2.343%, Blasius: 1.441%, and Petukhov: 0.640%). Heat transfer increased by 18.38% with GNPs-300, 22.05% with GNPs-500, 23.25% with GNPs-750, 13.63% with CNT < 8 nm, and 11.42% with CNT 20–30 nm, relative to H₂O at Re = 16,000. Pressure drop increased by about 42.01% with GNPs-300, 45.16% with GNPs-500, 44.84% with GNPs-750, 36.72% with CNT < 8 nm, and 34.39% with CNT 20-30 nm.