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In this paper, we present a generic approach of a dynamical data-driven modelorder reduction technique for three-dimensional fluid-structure interactionproblems. A low-order continuous linear differential system is identified fromsnapshot solutions of a high-fidelity solver. The reduced order model (ROM)uses different ingredients like proper orthogonal decomposition (POD), dynamicmode decomposition (DMD) and Tikhonov-based robust identification techniques.An interpolation method is used to predict the capsule dynamics for any valueof the governing non-dimensional parameters that are not in the trainingdatabase. Then a dynamical system is built from the predicted solution.Numerical evidence shows the ability of the reduced model to predict thetime-evolution of the capsule deformation from its initial state, whatever theparameter values. Accuracy and stability properties of the resulting low-orderdynamical system are analyzed numerically. The numerical experiments show avery good agreement, measured in terms of modified Hausdorff distance betweencapsule solutions of the full-order and low-order models both in the case ofconfined and unconfined flows. This work is a first milestone to move towardsreal time simulation of fluid-structure problems, which can be extended tonon-linear low-order systems to account for strong material and flownon-linearities. It is a valuable innovation tool for rapid design and for thedevelopment of innovative devices.

journal of fluid mechanics

Data-driven kinematics-consistent model order reduction of  fluid-structure interaction problems: application to deformable microcapsules  in a Stokes flow