Bioconvection MHD casson hybrid nanofluid (Ag-TiO2) flow with dissipative heat transfer and slip effects: A neural network-assisted computational study for biomedical applications.

The biomedical engineering and polymer processing industries experience rising needs for thermal management systems because of continuous technology improvements. The study investigates how a Casson fluid magnetohydrodynamic (MHD) stagnation-point flow behaves when used with an expanding surface while including viscous dissipation, chemical reaction, Joule heating, and thermal radiation through the Rosseland approximation. The model uses velocity slip and convective boundary conditions to create more realistic physical behavior. The governing nonlinear partial differential equations(PDEs) are transformed into a system of coupled ordinary differential equations(ODEs) using similarity transformations and solved numerically via the shooting method with a fourth-order Runge-Kutta scheme implemented in MATLAB. The results show that increasing the Casson parameter leads to a 15 to 25 percent improvement in the velocity profile because it decreases the resistance from yield stress. The temperature profile rises significantly (about 20-30%) with an increasing magnetic parameter as a result of intensified Joule heating effects. Higher Schmidt number values result in a significant reduction in concentration profiles which decreases by almost 10 to 20 percent because of decreased mass diffusivity. The Peclet number shows an inverse relationship with the motile microorganism density which causes a decrease of about 12 to 18 percent when it rises. The findings provide valuable insights which help industries and biomedical fields to improve their thermal systems that use non-Newtonian fluids.