2013
DOI: 10.1080/00036811.2013.839780
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Effective equations for fluid-structure interaction with applications to poroelasticity

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Cited by 31 publications
(55 citation statements)
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“…To permit analysis, the authors of [54,61] (and other similar studies) exploited asymptotic restrictions on the underlying model, considering slow (quasi-static) growth and linearised deformation. Collis et al [17] relaxed such assumptions to consider a macroscale representation of finite volumetric nutrient-limited growth of a hyperelastic solid, employing the arbitrary Lagrangian-Eulerian approach [12] to ameliorate the challenges involved in applying two-scale asymptotics to such a system.…”
Section: Introductionmentioning
confidence: 99%
“…To permit analysis, the authors of [54,61] (and other similar studies) exploited asymptotic restrictions on the underlying model, considering slow (quasi-static) growth and linearised deformation. Collis et al [17] relaxed such assumptions to consider a macroscale representation of finite volumetric nutrient-limited growth of a hyperelastic solid, employing the arbitrary Lagrangian-Eulerian approach [12] to ameliorate the challenges involved in applying two-scale asymptotics to such a system.…”
Section: Introductionmentioning
confidence: 99%
“…Although, in this paper, we do not describe behaviour at the pore level, the proposed formulation is coherent with the homogenization procedure which can provide the desired effective material parameters at the deformed configuration. In relationships with existing works [39,37] and [11], this is an important aspect which justifies the proposed formulation as an improved modelling framework for a more accurate multi-scale computational analysis of the porous media.…”
Section: Introductionmentioning
confidence: 71%
“…We will now consider nonlinear solve in space after time discretization by the fully coupled scheme (7). One could rewrite (7) as a nonlinear system each time step and use a Newton solver, however, for our GMsFEM we prefer to use a linearization based on Picard iteration.…”
Section: Fine-scale Discretizationmentioning
confidence: 99%