Application of Artificial Neural Network for Modeling of Motile Microorganism-Enhanced MHD Tangent Hyperbolic Nanofluid across a vertical Slender Stretching Surface

Abstract

The Levenberg-Marquardt back propagation artificial neural networks (LM-BP-ANNs) procedure is used in this analysis to show the computational strategy of neural networks for the simulation of MHD Tangent hyperbolic nanofluid (THNF). The THNF flow comprised of motile microorganism has been considered across a vertical slender stretching surface. The fluid flow has been examined under the significance of chemical reaction, magnetic field, activation energy, and heat source. The modeled equations are simplified to the ordinary system of differential equations using similarity variables substitution. The Lobatto IIIA formula based on the finite difference method (FDM) is employed for the nano liquid flow problem with an accuracy up to 5 decimal points. The robustness of Lobatto IIIA is its straightforward executing of very nonlinear coupled differential equations. The outcomes of FDM are manipulated to set up the reference datasets for LM-BP-ANNs technique. Several operations involves testing, authentication, and training are carried out by developing a scheme for different fluid problem elements using reference datasets. The accurateness of LM-BP-ANNs is tested through mean square error, error histogram, curve fitting figures and regression plot. Moreover, the examination of flow model factors for concentration, mass and momentum outlines are expressed through graphs.