Computational Insights Into Nanoscale Heat Dynamics of Chemically Reactive and Magnetized Carreau Hybrid Bio‐Nanofluid Using a Multilayer Supervised Neural Computing Scheme

ABSTRACT The use of well‐designed nanoparticles in blood fluid can enhance heat transfer during medical interventions by improving thermophysical characteristics. It enables for targeted heat delivery to specific sites by increasing surface area for better heat exchange, which is crucial in more efficient treatments. The current attempt emphasizes on the enhanced thermal transport mechanism in an aluminium alloy suspended Copper‐based blood nanofluid over an inclined cylindrical surface containing motile gyrotactic microbes. The Carreau fluid viscosity model is implemented to expose the intricate nature of bio‐nanofluid, while the heating source is used to simulate the bio‐convective heat transport mechanism. In addition, the viscosity of hybrid bio‐nanofluids exhibits temperature effects that depend on nanoparticle volume friction dependencies related to the dynamics of spherical and cylindrical shapes with distinct shape factors. The physical generated system of partial differential equations (PDEs) is derived and then transformed into a dimensionless system of ordinary differential equations (ODEs) using similarity functions. The resulting system is reduced into first‐order differential equations and a numerical solution is obtained by using a hybrid computational procedure. The trend of fluid profiles is examined by mean of governing parameters. Results are interpreted via tabular data and MATLAB visualization. It is observed that gravity and surface friction impede the flow direction with inclined magnetic field orientation which causes a decrease in velocity and an increase in the temperature profile. A declining trend is noted in the microbe profile due to higher values of the Peclet number and numeric growth in the value of the motile microbe's factor. Heat transport rate and drag force coefficients for both spherical and cylindrical nanoparticles differ by reasonable amounts. The proposed results build a bridge between traditional computational‐based simulations and advanced ANN‐based approaches, establishing a robust foundation for advanced applications in biomedical engineering.