Artificial neural network prediction of nonlinear radiative heat transfer and entropy generation in tangent hyperbolic tetra hybrid nanofluid flow over a porous wedge.

An artificial neural network (ANN)-based surrogate modelling approach for forecasting entropy production and heat transfer properties in a tetra-hybrid tangent-hyperbolic nanofluid flow over expanding and contracting wedge surfaces under nonlinear thermal radiation is presented in this work. The generated numerical data are subsequently employed to develop an ANN surrogate model for the rapid prediction of the skin-friction coefficient and local Nusselt number. Numerical analysis demonstrates that increasing the wedge index reduces free-stream acceleration and, in turn, the velocity and temperature fields, while a larger Weissenberg number increases flow elasticity and heat transfer. Nonlinear thermal radiation significantly impacts heating, whereas magnetic field and porous-medium effects increase temperature and entropy generation by impeding convective heat transfer. The developed ANN model achieved excellent predictive accuracy, with test ([Formula: see text]) values of 0.9815 for [Formula: see text] and 0.9983 for [Formula: see text], demonstrating excellent agreement with the numerical solutions. The proposed numerical-ANN framework provides a computationally efficient approach for analyzing complex thermal transport in porous wedge flows, with potential applications in electronic cooling, and high-performance heat process.