Analysis of biomagnetic blood Carreau hybrid nanofluid flow in stenotic arteries with motile gyrotactic microorganisms: Response surface optimisation

Abstract

Cardiovascular diseases remain a leading cause of death globally, with stenosis playing a significant role in their development. Blood flow dynamics within stenosed arteries are intricate and influenced by various factors, including nanoparticles and microorganisms. This study actively investigates the flow behaviour of biomagnetic blood Carreau tetra hybrid nanofluid within stenotic arteries containing motile gyrotactic microorganisms through response surface optimisation. The formulation is rooted in the Carreau model, elucidating non-Newtonian fluid behaviours, incorporating the Lorentz force to address the magnetic field effects, and integrating the convective-diffusion equation to model microorganism transport. We derived numerical solutions using the built-in solver in MATLAB, i.e. ODE45. The computational domain encompasses a two-dimensional stenosed artery featuring a sinusoidal stenosis profile. We characterise the inlet conditions with fully developed Carreau fluid flow accompanied by a uniform magnetic field, while the outlet conditions involve a zero-pressure gradient. The arterial walls are assumed to be rigid and non-slip. Response surface optimisation employs the central composite design method to minimise pressure drop across the stenosis, yielding optimal values for input parameters, including magnetic field strength, nanoparticle concentration, and microorganism concentration. The study discerns the substantial influence of nanoparticles and microorganisms on flow behaviour within stenosed arteries, underscoring the efficacy of response surface optimisation in flow behaviour optimisation. Limitations include assumptions of steady flow and simplifications in artery geometry, suggesting avenues for future exploration, particularly in transient flow dynamics and three-dimensional artery geometries. The validation of the numerical simulations demonstrates strong alignment with published data.