Modeling thermal conductivity augmentation of nanofluids using diffusion neural networks

In the present investigation, neural network method is employed to estimate thermal conductivity of nanofluids consisting of multi-walled carbon nanotubes (MWCNTs) suspended in oil (α-olfin), decene (DE), distilled water (DW), ethylene glycol (EG) and also single-walled carbon nanotubes (SWCNTs) in epoxy and poly methylmethacrylate (PMMA). The results obtained have been compared with other theoretical models as well as experimental values. The predicted thermal conductivities are in good agreement with the literature values.