Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm - Shafiq, Anum; Çolak, Andaç Batur; Sindhu, Tabassum Naz (European physical journal plus, 2023)
The low heat efficiency of base fluids is a key problem among investigators. To address this issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to boost thermal performance of base solvents. A numerical investigation on the thermal application functioning of graphene oxide water/ethylene glycol-based nanofluids under the influence of the electromagnetohydrodynamic and Darcy–Forchheimer medium has been compiled in the present study via a machine learning algorithm. In the study of nanofluid flow, thermal radiation and a convective boundary condition are also used. The Runge–Kutta fourth-order shooting method was utilized to calculate the system of equations. The skin friction coefficient and Nusselt parameter were simulated with various variables, and two distinct artificial neural networks have been developed based on the findings. It is beneficial to estimate the fluid temperature with a large Biot number. R value above 0.99 was obtained for the developed artificial neural networks. The deviation rate was also calculated at very low values. The outcomes show that the proposed artificial neural network models can accurately predict the skin friction coefficient and Nusselt number.