Computational analysis and data-driven optimization of radiative magnetohydrodynamic Williamson ternary hybrid nanofluid at separated stagnation-point flow

Abstract

The goal of this study is to analyze, predict, and optimize the thermal and frictional properties of the unsteady separated stagnation-point flow of a radiative magnetohydrodynamic (MHD) Williamson ternary hybrid nanofluid to minimize skin friction and maximize heat transfer. The nonlinear governing boundary-layer equations were derived to account for the combined effects of unsteady separated stagnation point flow of MHD Williamson ternary hybrid nanofluid over a stretching surface, incorporating thermal radiation and mass suction, and were solved numerically using MATLAB's bvp4c method. Using the data, a scaled conjugate gradient-based Artificial Neural Network (SCG-ANN) demonstrated exceptional predictive performance, with MSE ranging from 10#8315;⁶ to 10#8315;⁷ and correlation coefficient (R) exceeding 0.999, indicating high model accuracy and generalization potential. To determine the best operating conditions, the effect and interaction of unsteadiness ([Formula: see text]), nanoparticle volume fraction [Formula: see text], Weissenberg number ([Formula: see text]), and radiation ([Formula: see text]) on the skin friction coefficient and local Nusselt number were quantified using the Response Surface Methodology (RSM) with a Central Composite Design (CCD). The ANOVA findings showed significant models with R2 = 95.09% for skin friction and 95.46% for Nusselt number. With a composite desirability of 100%, the RSM optimization projected the minimal skin friction (0.4686) at [Formula: see text], and [Formula: see text], and the highest Nusselt number (129.68) at [Formula: see text], and [Formula: see text]. Research shows that increasing the Weissenberg number and flow deceleration (negative [Formula: see text]) reduce drag, while increasing nanoparticle volume fraction and radiation intensity enhance heat transfer. For designing advanced cooling, energy, and drag-reduction applications, the integrated SCG-ANN-RSM framework predicts and optimizes thermo-hydrodynamic transport in radiative non-Newtonian nanofluid systems using a computationally efficient and powerful hybrid modelling strategy.