Mathematical modelling for viscoinelastic nanofluid flow over a stretching sheet with machine learning: an application to tissue adhesive

The medicated tissue adhesive on a stretching surface through Prandtl–Eyring nanofluid flow is emphasized in the current article to estimate the heat transfer rate and optimize the adhesion process. The effects of Brownian motion and thermophoresis on electrically conducting viscoinelastic nanofluid are taken into consideration with the sight of convective states. The modeled governing equations are nondimensionalized by operating similarity variables to stimulate the optimization process. The result of an executed model is solved scientifically by employing the NDSolve technique. The influence of various parameters on the fluid momentum, thermal, and concentration distributions is accentuated through graphs. Furthermore, the machine learning approach is enhanced to analyze the physical quantities of interest in the entire region. The outcomes are validated to those that have already been published in the pertinent area literature to determine the effectiveness of viscoinelastic nanofluid. The findings revealed that the Prandtl–Eyring fluid parameter enhances the momentum, while the Hartmann number indicates the reverse trend. In addition, it delivers that the proposed machine learning model is capable of forecasting the physical quantities with lower error (10−3) and is a powerful engineering tool that can be effectively employed in a viscoinelastic nanofluid.