Physics-informed neural networks for the repercussions of angular velocity of the cone and the free flow of chemically reactive ternary nanofluid

The interplay between the angular velocity of a revolving cone and the free flow of a chemically reactive ternary nanofluid results in enhanced shear-driven interaction, increased thermal conductivity, and faster reaction kinetics owing to the synergistic effects of rotation and nanoparticle distribution. Inspired by this, the present study investigates the influence of thermal radiation on the angular velocities of the free flow and the cone’s arbitrary temporal fluctuations, resulting in an unsteady stream over a rotating cone in a rotating ternary nanofluid. The flow and heat transfer processes influenced by thermal radiation are significant in scientific research because of their many applications. Moreover, thermal radiation-based heat transfer is essential in renewable energy systems. To solve the reduced equations, a physics-informed neural network integrated with the Hermite polynomial is utilized. The results of the Hermite polynomial neural network (H-PINN) algorithm demonstrate substantial consistency with the numerical finite difference method (FDM) results, with the absolute error falling between 10−4 and 10−6. As the ratio of the angular velocity of the cone to the angular velocity of the free-stream increases, the velocity profile decreases. Increasing the chemical reaction parameter decreases the concentration profile.