Heat Transfer Impacts on Maxwell Nanofluid Flow over a Vertical Moving Surface with MHD Using Stochastic Numerical Technique via Artificial Neural Networks

The technique of Levenberg-Marquardt back propagation with neural networks (TLMB-NN) was used in this research article to investigate the heat transfer of Maxwell base fluid flow of nanomaterials (HTM-BFN) with MHD over vertical moving surfaces. In this study, the effects of thermal energy, concentration, and Brownian motion are also employed. Moreover, the impacts of a heat-absorbing fluid with viscous dissipation and radiation have been explored. To simplify the governing equations from a stiff to a simple system of non-linear ODEs, we exploited the efficacy of suitable similarity transformation mechanism. Through applicability of state-of-the-art Adams numerical technique, a set of data for suggested (TLMB-NN) is generated for several situations (scenarios) by changing parameters, such as the Thermophoresis factor N-t, Hartmann number M, Eckert number E-c, concentration Grashoff parameter G(c), Prandtl number P-r, Lewis number Le, thermal Grashof number G(T), and Brownian motion factor N-b. The estimate solution of different instances has validated using the (TLMB-NN) training, testing, and validation method, and the recommended model was compared for excellence. Following that, regression analysis, mean square error, and histogram explorations are used to validate the suggested (TLMB-NN). The proposed technique is distinguished based on the proximity of the proposed and reference findings, with an accuracy level ranging from 10(-9) to 10(-10).

Automated summary

In this study, the technique of Levenberg-Marquardt back propagation with neural networks was used to investigate the heat transfer of Maxwell base fluid flow of nanomaterials with MHD. Various parameters such as thermal energy, concentration, Brownian motion, heat-absorbing fluid, viscous dissipation, and radiation were considered. The proposed technique demonstrated high accuracy levels ranging from 10^(-9) to 10^(-10) through validation methods like regression analysis and mean square error comparisons.