Numerical simulation accompanied by an intelligent computing system for the chemical reaction of Casson nanofluid and radiative heat flux on a nonlinear stretching surface

Abstract

This study investigates the impact of radiative heat flux phenomena and magnetized Casson nanofluid flow on boundary layer flow along a non-linear stretching surface. Brownian motion and thermophoresis effects are used to highlight the properties of the nanofluid, including electrical conductivity. The flow curves and fluid behavior of particle suspensions in lithographic coatings used to make printing paints are well-described by the nonlinear Casson framework. Suction, chemical reaction, and a homogeneous magnetic field are all considered. An effective Levenberg-Marquardt Methodology (LMM) for artificial neural networks (ANNs) method is presented in this article. The LMM is one of the back-propagation algorithms with the lowest nonlinear latency. The required changes are necessary to convert a quasi-network of partial differential equations (PDEs) expressing the chemical reaction of the Casson nanofluid and radiative heat flux into a set of standards. The couple system regarding the model are facilitated via numerical shooting algorithm The compared to prior findings, the current ones have a very high degree of accuracy. The effects of different factors on the rate of heat transfer, the skin friction coefficient, the concentration of nanoparticles, the Sherwood number, the velocity, and the temperature profiles are illustrated in a tabular and visual form. The temperature and concentration fields exhibit the opposite effect on velocity as magnetic parameters change. The fluid's velocity is slowed down by a Lorentz force that is stronger at greater values of the magnetic field. Due to the suction parameter, the thickness of the momentum, temperature, and concentration boundary layers decreases. In physical terms, pressure forces are generated in the flow when the suction parameter is positive, and the opposite occurs when the suction parameter is negative. It is revealed that enhancement in thermal and concentration phenomenon is predicted by increasing Casson fluid parameter. For ANN analysis, the gradient numerical values reduced upon enhancing the number of epoch.