Data Sheet 1_ANN-driven analysis of Cattaneo–Christov heat transfer and entropy generation in Maxwell hybrid nanofluid flow over a vertical cone.docx

Introduction<p>This research investigates Cattaneo-Christov non-Fourier heat transfer and entropy generation in Maxwell hybrid nanofluid flow over a non-Darcy porous conical surface using numerical, sensitivity, and Artificial neural networks (ANN) approaches.</p>Methods<p>A coupled momentum-energy model that includes fluid relaxation, magnetic field effects, nanoparticle loading, porous resistance, and thermal relaxation is transformed using similarity variables and solved by a boundary-value numerical method.

Sobol' global sensitivity analysis is employed to quantify the direct and interaction effects of the governing parameters on skin friction, Nusselt number, and entropy generation.</p>Results<p>The analysis identifies the porous-medium parameter as the dominant contributor to skin friction and heat transfer, while its contribution to entropy generation remains substantial at both buoyancy conditions. The total-order indices further reveal increased parameter interactions in entropy generation at higher buoyancy.