Radiative Hybrid Nanofluid Flow Over a Porous Riga Surface: A Fuzzy–ANN Modeling Approach

ABSTRACT This study proposes a fuzzy–ANN model to investigate the nonlinear thermal transport in a tangent hyperbolic (Tanh) hybrid nanofluid flow past a porous Riga surface, considering the effects of Rosseland diffusion, chemical reactions, and internal volumetric heating. The hybrid nanofluid is composed of a suspension of MgO nanoparticles mixed with Ag nanoparticles in an engine oil (EO) base fluid. To address the uncertainty associated with the nanoparticle volume fraction and thermophysical properties, triangular fuzzy numbers (TFNs) and membership functions are employed in the analysis. The results reveal a reduction in thermal transport uncertainty of up to 40% when the α‐cut level increases from 0.1 to 0.9. The ANN models trained using Bayesian regularization (BRS) and Levenberg–Marquardt (LMS) algorithms show high predictive accuracy with a correlation coefficient close to unity ( R = 1.0) and MSE less than 10 −2 . Furthermore, the Sherwood number increases by 33.9%, whereas the Nusselt number decreases by 39.8% for the selected parameter variations. The results reveal the effectiveness of the MgO‐Ag/EO hybrid nanofluid in enhancing heat transfer performance compared with MgO/EO and Ag/EO nanofluids. The proposed fuzzy–ANN model provides a computationally efficient and accurate technique for thermal management systems.