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Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model

Article dans une revue avec comité de lecture
Auteur
URCUN, Stéphane
SCIUMÈ, Giuseppe
1002421 Institut de Mécanique et d'Ingénierie [I2M]
BORDAS, Stéphane P.A.
ccROHAN, Pierre-Yves
1001017 Institut de Biomécanique Humaine Georges Charpak [IBHGC]

URI
http://hdl.handle.net/10985/21321
DOI
10.1016/j.jmbbm.2021.104952
Date
2021
Journal
Journal of the Mechanical Behavior of Biomedical Materials

Résumé

This paper investigates the complex time-dependent behavior of cortex tissue, under adiabatic condition, using a two-phase flow poroelastic model. Motivated by experiments and Biot’s consolidation theory, we tackle time-dependent uniaxial loading, confined and unconfined, with various geometries and loading rates from 1 µm/s to 100 µm/s. The cortex tissue is modeled as the porous solid saturated by two immiscible fluids, with dynamic viscosities separated by four orders, resulting in two different characteristic times. These are respectively associated to interstitial fluid and glial cells. The partial differential equations system is discretised in space by the finite element method and in time by Euler-implicit scheme. The solution is computed using a monolithic scheme within the open-source computational framework FEniCS. The parameters calibration is based on Sobol sensitivity analysis, which divides them into two groups: the tissue specific group, whose parameters represent general properties, and sample specific group, whose parameters have greater variations. Our results show that the experimental curves can be reproduced without the need to re-sort to viscous solid effects, by adding an additional fluid phase. Through this process, we aim to present multiphase poromechanics as a promising way to a unified brain tissue modeling framework in a variety of settings.

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    Article dans une revue avec comité de lecture
    URCUN, Stéphane; ROHAN, Pierre-Yves; SCIUMÈ, Giuseppe; BORDAS, Stéphane P.A. (Elsevier BV, 2021)
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