Application of Artificial Neural Networks in Tangent Hyperbolic Nanofluid Flow Over a Riga Plate with Bioconvection and Nonlinear Thermal Radiation

Abstract

The study of tangent hyperbolic nanofluids in the presence of bioconvection and nonlinear thermal radiation over Riga plates within porous media addresses critical challenges in enhancing heat transfer and fluid dynamics in advanced engineering systems. This research fills a significant gap by applying artificial neural networks (ANNs) to model the complex behavior of tangent hyperbolic nanofluids, which exhibit non-Newtonian characteristics, under these conditions. By introducing appropriate similarity transformations, the governing equations in partial differential form are reduced to ordinary differential equations. These resulting equations are then integrated numerically using the Runge-Kutta method of order four. This innovative approach offers both precision and computational efficiency in addressing highly nonlinear systems. The findings have substantial real-world applications, particularly in the optimization of heat transfer technologies, such as thermal management systems, bio-microfluidic devices, and enhanced oil recovery in porous media. The integration of ANNs with classical numerical techniques provides a robust framework for solving fluid flow problems in complex environments, offering new avenues for improving energy systems and industrial processes where precise thermal control is crucial.