Intelligent neural network–based framework for Maxwell nanofluid flow with Soret–Dufour coupling and nonlinear thermal radiation

Abstract

This study examined the optimization of the Levenberg–Marquardt backpropagation approach, which uses artificial neural networks, to calculate the interacting features of Soret and Dufour effects on convective Maxwell nanofluid flow through a porous surface with nonlinear thermal emission. The governing system of partial differential equations was first converted into ordinary differential equations, which were then simulated using the Levenberg–Marquardt algorithm and the numerical method BVP4c. Additionally, the consistency and stability of the technique were ensured. Lie symmetry group transformations were used to convert the partial differential equations into ordinary differential equations. Neural networks may also be used to map temperature, velocity, and concentration characteristics from input to output. Various profiles against the porosity parameter, Dufour number, and Soret number are explored. The numerical results depict thermal radiation, and a heat source were found to increase the heat output of the flow. It is also intriguing to note that the skin friction, Sherwood number, and Nusselt number are strongly influenced by the porosity and Dufour factors. Nonetheless, the present findings could have applications in the fields of nuclear waste disposal, steel industries, heat exchangers, cooling applications, and petroleum reservoirs, among others.