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Reduced-order models for blood flow in networks of large arteries

Abstract : Every cardiac cycle, the heart contracts and ejects blood into the vascular network. This periodic inflow translates into the propagation of a pulse wave, which, through interactions with the elastic arterial wall, the blood and the complex arterial network, shapes itself into the pulsatile signal clinicians observe on a daily basis. Understanding these complex wave propagation dynamics is of great clinical relevance as large arteries are a breeding ground for many common cardiovascular pathologies which are often triggered by hemodynamical factors. Unfortunately, hemodynamics in large arteries are too complex to be apprehended using only non-invasive measurements and medical imaging techniques. Patient-specific numerical simulations of blood flow have therefore been developed to provide clinicians with valuable insights on pathogenesis and the outcome of surgeries. As three-dimensional models are usually used only in small portions of the cardiovascular system due to their high modeling and computational costs, we have used reduced-order models to reproduce complex wave propagation behaviors in large networks of arteries. We have first focused on one-dimensional models for blood flow and developed novel approaches that take into account the non-Newtonian behavior of blood and the viscoelastic response of the arterial wall. Next, we have proposed a fluid-structure interaction twodimensional blood flow model to capture the complex flow patterns in stenoses and aneurysms unavailable to classical one-dimensional models. Finally, we have applied these models to compute the flow in large arterial networks and to predict the outcome of bypass surgeries.
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Submitted on : Monday, April 23, 2018 - 1:10:07 PM
Last modification on : Sunday, October 25, 2020 - 7:19:54 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01666313, version 2


Arthur Ghigo. Reduced-order models for blood flow in networks of large arteries. Mechanics of the fluids [physics.class-ph]. Université Pierre et Marie Curie - Paris VI, 2017. English. ⟨NNT : 2017PA066422⟩. ⟨tel-01666313v2⟩



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