Modeling blood flow in networks of viscoelastic vessels with the 1D augmented fluid-structure interaction system

01/29/2021
by   Francesco Piccioli, et al.
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A noteworthy aspect in blood flow modeling is the definition of the mechanical interaction between the fluid flow and the biological structure that contains it, namely the vessel wall. It has been demonstrated that the addition of a viscous contribution to the mechanical characterization of vessels brings positive results when compared to in-vivo measurements. In this context, the implementation of boundary conditions able to keep memory of the viscoelastic contribution of vessel walls assumes an important role, especially when dealing with large circulatory systems. In this work, viscoelasticity is taken into account in entire networks via the Standard Linear Solid Model. The implementation of the viscoelastic contribution at boundaries, namely inlet, outlet and junction, is carried out considering the natively hyperbolic nature of the mathematical model. A non-linear system is established based on the definition of a specific Riemann Problem at junctions. Basic junction test cases are analyzed, such as a trivial 2-vessels junction, for both a generic artery and a generic vein, and a simple 3-vessels junction, considering an aortic bifurcation scenario. The chosen IMEX-SSP2 Finite Volume scheme is demonstrated to be second-order accurate in the whole domain and well-balanced, even when including junctions. Two benchmark arterial networks are then implemented, differing in number of vessels and in viscoelastic parameters. Comparison of the results obtained in the two networks underlines the high sensitivity of the model to the chosen viscoelastic parameters. In these numerical tests, the network-wise conservation of the contribution provided by the viscoelastic characterization of vessel walls is assessed. Finally, numerical results of pressure waveforms in the common carotid artery and in the femoral artery of the second network simulated are compared with in-vivo data.

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