4.4 Article

Nonlinear lumped-parameter models for blood flow simulations in networks of vessels

Journal

ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS
Volume 56, Issue 5, Pages 1579-1627

Publisher

EDP SCIENCES S A
DOI: 10.1051/m2an/2022052

Keywords

Blood flow; lumped-parameter models; nonlinearity; coupling; arterial networks

Funding

  1. University of Trento
  2. Italian Ministry of Education, University and Research (MIUR) [L. 232/2016]

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In order to improve the computational efficiency of blood flow modeling in complex networks, this study derives a family of nonlinear lumped-parameter models from a well-established one-dimensional model. The derived models preserve important nonlinear properties of the original model and accurately predict pressure and flow waveforms in blood vessels at a lower computational cost.
To address the issue of computational efficiency related to the modelling of blood flow in complex networks, we derive a family of nonlinear lumped-parameter models for blood flow in compliant vessels departing from a well-established one-dimensional model. These 0D models must preserve important nonlinear properties of the original 1D model: the nonlinearity of the pressure-area relation and the pressure-dependent parameters characterizing the 0D models, the resistance R and the inductance L, defined in terms of a time-dependent cross-sectional area subject to pressure changes. We introduce suitable coupling conditions to join 0D vessels through 0D junctions and construct 0D networks preserving the original 1D network topology. The newly derived nonlinear 0D models are then applied to several arterial networks and the predicted results are compared against (i) the reference 1D results, to validate the models and assess their ability to reproduce good approximations of pressure and flow waveforms in all vessels at a much lower computational cost, measured in terms of CPU time, and (ii) the linear 0D results, to evaluate the improvement gained by including certain nonlinearities in the 0D models, in terms of agreement with the 1D results.

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