Why the study?
Computational hemodynamic methods are effective for controlling and preventing cardiovascular disease progression, motivating investigation of how non-Newtonian viscosity models affect cerebral blood vessels.
Computational hemodynamic simulations demonstrate that increasing Reynolds number in cerebral blood vessels leads to increased dimensionless temperature, pressure drop, and average wall shear stress across different non-Newtonian viscosity models.
These computational results should not guide clinical cerebrovascular management; leaves open Reynolds number effects in non-Newtonian cerebral flow models for validation.
Nowadays, cardiovascular illnesses are among the leading causes of death in the world. Thus, many studies have been performed to diagnose and prevention of these diseases. Studies show that the computational hemodynamic method (CHD) is a very effective method to control and prevent the progression of this type of disease. In this computational paper, the impression of five non-Newtonian viscosity models (nNVMs) on cerebral blood vessels (CBV) is investigated by CHD. In this simulation, blood flow is supposed steady, laminar, incompressible, and non-Newtonian. The parameters of Nusselt number (Nu), dimensionless temperature (θ), pressure drop (Δp), and dimensionless average wall shear stress (DAWSS) are also investigated by considering the effects of heat generated by the body. Utilizing the FVM and SIMPLE scheme for pressure–velocity coupling is a good approach to investigating CBVs for five different viscosity models. In the results, it is shown that the θ and Δp+ increase with increasing Reynolds number (Re) in the CBVs. By enhancing the Re from 90 to 120 in the Cross viscosity model, the Δp+ changes about 1.391 times. The DAWSS grows by increasing the Re in all viscosity models. This increase in DAWSS leads to an increasing velocity gradient close to the cerebral vessel wall.
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Li et al. (2023) studied this question.
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