Publication

Abstract : The present study develops a hybrid analytical-computational approach to the thermal transport study of Reiner–Rivlin nanofluid flow with Arrhenius activation energy effects, aligning with UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 12 (Responsible Consumption and Production). The governing nonlinear partial differential equations are reduced to a coupled system of ordinary differential equations via Lie group transformations and solved numerically. An artificial neural network (ANN), trained using the Levenberg–Marquardt algorithm, is integrated with a modified Garson sensitivity analysis to quantify the effect of important parameters on the heat transfer. The ANN model exhibits excellent prediction accuracy with an overall correlation coefficient . Results show that the thermal Biot number yields the highest positive impact, increasing the heat transfer rate by 54.61% per unit increment, while the cross-viscous parameter has the least effect. The framework presented offers not only accurate modeling but also interpretable parameter sensitivity information for high-end energy, biomedical, and microfluidic systems.