2023
DOI: 10.1017/jfm.2022.1005
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Data-driven kinematics-consistent model-order reduction of fluid–structure interaction problems: application to deformable microcapsules in a Stokes flow

Abstract: In this paper, we present a generic approach of a dynamical data-driven model-order reduction technique for three-dimensional fluid–structure interaction problems. A low-order continuous linear differential system is identified from snapshot solutions of a high-fidelity solver. The reduced-order model uses different ingredients, such as proper orthogonal decomposition, dynamic mode decomposition and Tikhonov-based robust identification techniques. An interpolation method is used to predict the capsule dynamics… Show more

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Cited by 2 publications
(1 citation statement)
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References 42 publications
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“…According to the literatures, hydrodynamic forces and boundary confinement lead to a large deformation of capsules, which can take either a parachute or a slug shape. [ 60–62 ] In their studies, deformations of the capsules in flow (inside a microfluidic device) have been estimated and confirmed dependent on capillary number change (Ca = µV / σ , where µ is a dynamic viscosity of the liquid, V is a characteristic velocity, and σ is a surface tension or interfacial tension between the two fluid phases.) at the same diameter of the capsules and the width of the microfluidic channels.…”
Section: Resultsmentioning
confidence: 98%
“…According to the literatures, hydrodynamic forces and boundary confinement lead to a large deformation of capsules, which can take either a parachute or a slug shape. [ 60–62 ] In their studies, deformations of the capsules in flow (inside a microfluidic device) have been estimated and confirmed dependent on capillary number change (Ca = µV / σ , where µ is a dynamic viscosity of the liquid, V is a characteristic velocity, and σ is a surface tension or interfacial tension between the two fluid phases.) at the same diameter of the capsules and the width of the microfluidic channels.…”
Section: Resultsmentioning
confidence: 98%