Numerical Investigation of Ternary Hybrid Non-Newtonian Nanofluids and Heat Transport Over an Inclined Shrinking Sheet Utilizing Artificial Neural Network

The purpose of the study is to investigate the thermal proficiency of a trihybrid magnetized water-based cross nanofluid over an inclined shrinking sheet. Cross-fluid is the best model to investigate the fluid flow at a very high and very low share rate. There are three nanoparticles that are added in based fluid (water) to form the requisite posited ternary hybrid nanofluid. Moreover, heat transport analysis is scrutinized by incorporating the melting conditions. The obtained nonlinear system of partial differential equations (PDEs) from assumed physical assumption is converted into the nonlinear setup of ordinary differential equations (ODEs). These ODEs are passed under the boundary value problem of a fourth-order (bvp4c) MATLAB program for numerical results. With the help of bvp4c, data are further trained through an artificial neural network and results are predicted. Results are compared with both techniques and found smooth agreement. The obtained numerical results provide valuable insight for optimizing heat transfer processes involving nanoparticle-enhanced fluid on inclined shrinking sheets. From the results, it is concluded that the inclusion of nanoparticles enhances the viscosity and thermal conductivity of the fluid. High temperatures make rapid heat transfer scenarios.