A Cross-Fluid Heat Transfer Analysis Using Neural Networks Over Porous Rotating Disk.

Abstract The primary focus of this study is on the heat and mass transfer properties of incompressible cross-fluid flow across a porous rotating disk. The research involves the use of sophisticated mathematical methods, such as similarity transformations and an ANN algorithm (backpropagation Levenberg-Marquardt Scheme), to convert governing partial differential equations into thoroughly non-linear ordinary differential equations. The Nusselt number, the Sherwood number, and the skin friction coefficient are all mathematically calculated for quantitative study. The findings are ensured to be reliable and consistent since the BVP4C method is used for numerical validation. The research is centered on the fluid mechanics and transport phenomena of this intricate system.