  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 (ROM)
uses different ingredients like proper orthogonal decomposition (POD), dynamic
mode decomposition (DMD) and Tikhonov-based robust identification techniques.
An interpolation method is used to predict the capsule dynamics for any value
of the governing non-dimensional parameters that are not in the training
database. Then a dynamical system is built from the predicted solution.
Numerical evidence shows the ability of the reduced model to predict the
time-evolution of the capsule deformation from its initial state, whatever the
parameter values. Accuracy and stability properties of the resulting low-order
dynamical system are analyzed numerically. The numerical experiments show a
very good agreement, measured in terms of modified Hausdorff distance between
capsule solutions of the full-order and low-order models both in the case of
confined and unconfined flows. This work is a first milestone to move towards
real time simulation of fluid-structure problems, which can be extended to
non-linear low-order systems to account for strong material and flow
non-linearities. It is a valuable innovation tool for rapid design and for the
development of innovative devices.
