Nonlinear fractional hemodynamic modeling of trihybrid Jeffrey nanofluid in stenosed arteries with artificial neural networks and K-fold cross-validation
Chandrakanta Parida, Ganeswar Mahanta, Sachin Shaw
Abstract
This study presents an integrated nonlinear, memory-driven model to analyze the hemodynamic and thermal behavior of blood flow through multi-stenosed arteries under physiological conditions. Blood is modeled as a viscoelastic, non-Newtonian fluid using the fractional Jeffery fluid model with Caputo–Fabrizio derivatives to capture memory-dependent characteristics. A ternary hybrid nanofluid with gold (Au), silver (Ag), and multi-walled carbon nanotubes is employed to enhance thermal performance, with physiological factors, such as magnetic fields, thermal radiation, body acceleration, and metabolic heat generation incorporated to ensure biological relevance. The governing equations are solved analytically via Laplace and Hankel transforms to derive exact expressions for velocity and temperature fields. To complement and validate the analytical model, an artificial neural network (ANN) trained using the Levenberg–Marquardt algorithm is employed to predict key hemodynamic parameters, including skin friction and Nusselt number, under varying conditions. The ANN model is rigorously assessed through K-fold cross-validation, demonstrating high accuracy and generalization. Results reveal a significant influence of fractional-order and viscoelastic parameters on flow resistance and heat transfer, highlighting the potential of hybrid modeling in cardiovascular diagnostics and thermal therapies. This study exemplifies the synergy of theoretical modeling and intelligent data-driven methods in addressing complex problems in biomedical engineering.