ANN-driven analysis of Cattaneo–Christov heat transfer and entropy generation in Maxwell hybrid nanofluid flow over a vertical cone
Abstract
Introduction 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. Methods 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. Results 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. The artificial neural network, trained using the numerically generated dataset, accurately predicts skin friction, Nusselt number, and entropy generation, with test R2 values of 0.99479, 0.99642, and 0.99033, respectively, along with low prediction errors. Discussion The combined numerical-Sobol' sensitivity-ANN framework enables rapid evaluation of nonlinear viscoelastic heat-transfer systems. The results underscore the utility of Maxwell hybrid nanofluids in thermally non-Fourier porous media from an engineering perspective and provide a computational framework for evaluating their thermal performance.