Numerical and machine learning based evaluation of ethylene glycol based hybrid nano-structured (TiO2-SWCNTs) fluid flow.

A better mechanical resistance and improved thermal conductivity, as compared to mono nano-liquids, can be attained by the ethylene glycol-based hybrid nanofluids. These fluids have substantial uses in several engineering systems. The main focus, in the recent work, is to assess the dynamics of the ethylene glycol-based hybrid nano-structured fluid via the computational fluid dynamics (CFD) and machine learning (ML) approaches. The nano-composition of single-walled carbon nanotubes (SWCNTs) and titanium dioxide (TiO2) in the ethylene glycol causes the hybrid mixture SWCNTs-TiO2/EG. A CFD model, for the simulation procedure, is developed by incorporating the similarity coordinates to the governing partial differential equations. This model comprises of a dimensionless system having prime parameters of the problem. The numerical results are appraised by means of a comparison between the present and the existing results. The levenberg marquardt (LM) technique is a powerful tool to predict the flow and thermal properties. The complex correlations between the input parameters and fluid flow properties can be interpreted with the help of CFD as well as LM neural network. The results of this work might provide a basis for the design and development of high-performance heat exchangers and thermal management systems.