Augmentation of magnetohydrodynamic nanofluid flow through a permeable stretching sheet employing Machine learning algorithm

An incompressible MHD nanofluid boundary layer flow over a vertical stretching permeable surface employing Buongiorno’s design investigated by considering the convective states. The Brownian motion and thermophoresis effects are used to implement the nanofluid model. Operating the similarity transmutations, to transform the governing partial differential equations into ordinary differential equations consisting of the momentum, energy, and concentration fields and later worked by using a program written together with the stiffness shifting in Wolfram Language. The consequences of various physical parameters on the velocity, temperature, and concentration fields are analyzed, such as magnetic parameter $M$, Brownian motion parameter $Nb$, thermophoresis parameter $Nt$, Lewis number $Le$, temperature Biot number $B{i}_{\theta }$, concentration Biot number $B{i}_{\varphi }$, and suction parameter $f_w$. Furthermore, the Skin friction coefficient, local Nusselt, and local Sherwood numbers concerning magnetic parameter for various values of physical parameters (i.e. $f_w$, $Nb$) are obtained graphically, then the outcome is validated with other recent works. Finally, introduced a new environment to employ machine learning by performing the sensitivity analysis based on the iterative method for predicting the Skin friction coefficient, reduced Nusselt number, and Sherwood number with respect to magnetic parameter for suction parameter and Brownian motion parameter. Machine learning algorithms provide a strong and quick data processing structure to enhance the actual research procedures and industrial application of fluid mechanics. These techniques have been upgraded and organized for fluid flow characteristics. The present optimization process has the potential for a new perspective on the metallurgical process, heat exchangers in electronics, and some medicinal applications.