Recent developments in chemical reactive flow of CNTs-hybrid nanomaterial with Soret-Dufour effects using Levenberg-Marquardt technique.

In this study, heat and mass transmission in chemical reactive flow of hybrid nanomaterial flowing across a Riga plate with Soret-Dufour effects are studied using integrated numerical computing through artificial neural networks that are back propagated with the Levenberg-Marquardt algorithm (ANN-BPLMA). The suggested model for energy-efficient process cooling in porous media with chemical reaction and Soret-Dufour properties employs a Riga-actuated CNTs-water-based hybrid nanofluid. It has promising applications in current thermal systems. It can be used to improve cooling performance in nuclear reactors, heat exchangers, and electronic device cooling applications requiring high thermal conductivity and accurate heat management. In order to explore the hybrid nanofluid the governing equations provide partial differential equations (PDEs) that mathematically explain temperature, velocity and concentration. The ODEs are generated from these PDEs by using the suitable similarity transformations. The Bvp4c method is used to generate the graphical results. Graphs are employed to describe how various parameters affect temperature, velocity and thermal profiles. The temperature and concentration profiles become more pronounced as the Soret and Dufour numbers increase.