Computational modelling and sensitivity analysis of heat transfer in ternary nanofluids using response surface methodology

Abstract

This study investigates the thermal optimization of ternary nanofluids, especially focusing on sensitivity analysis of the physical parameters. This study provides an efficient thermal management system that is essential in sophisticated cooling systems, such as electric vehicle battery packs and aerospace engines, to avoid overheating and maintain uniform temperature distribution. A statistical approach is used to analyze the skin friction and heat transfer rate via Response Surface Methodology and Analysis of Variance. Furthermore, irreversibility analysis is also calculated, arising due to Joule heating and viscous dissipation. A non-similar transformation is used to convert the boundary layer equations into dimensionless partial differential equations. The system of partial differential equations is converted into an ordinary differential equation using a local non-similar method up to second-order truncation. These systems of ordinary differential equations are solved numerically via bvp4c. Sensitivity analysis is performed for drag force and heat transfer rate for input parameters. The correlations between input factors and output responses are created via the use of analysis of variance tables, which is beneficial for regression analysis. The high values of \({{R}^2} = 99.84\% ,\ {{R}^2}( {\mathrm{ Adj}} ) = 99.70\% \) for drag force and \({{R}^2} = 99.97\% ,\ {{R}^2}( {\rm Adj} ) = 99.94\% \) for heat transfer rate show that high validity of analysis of variance results is obtained to perform sensitivity analysis. The results conclude that the Hartmann number is the most impactful factor among other parameters for friction and heat transfer rate at the surface. The Eckert number and volume fraction coefficient are caused to rise in entropy generation.