Artificial Neural Network Approach for Homogeneous‐Heterogeneous Catalytic Reaction in Off‐Centered Stagnation Flow

ABSTRACT The aerodynamic design of automobiles and airplanes is one of the real‐world applications of off‐centered stagnation point (OSP) flow. A vehicle's stagnation points change, and the airflow becomes unstable as it collides with crosswinds or curves. Engineers must comprehend and anticipate this behavior to optimize surface forms for increased stability, less drag, and higher fuel economy. This study demonstrates the OSP flow of Casson fluid (CF) on a spinning disk influenced by heterogeneous‐homogeneous chemical reactions. The effects of quadratic thermal radiation (TR) and the magnetic field on the fluid flow are also taken into account. The governing equations are reduced by appropriate similarity transformations to a system of ordinary differential equations, which are then numerically solved using the Runge–Kutta–Fehlberg fourth‐fifth‐order technique. Additionally, the properties of heat and mass transfer, and fluid flow are evaluated using the artificial neural network approach. Investigations of error histogram, mean square error (MSE), regression analysis, and transition dynamics investigations provide significant evidence of the efficiency of the proposed ANN model for solving the current problem. The graphs represent the consequences of dimensionless parameters on the fluid's various profiles. The enhanced rotational parameter elevates the radial velocity profile. The magnetic field parameter diminishes the velocity profiles. The thermal profile increases with a rise in the radiation parameter. An increase in heterogeneous and homogeneous reaction parameters diminishes the concentration profile.