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Data-driven kinematics-consistent model order reduction of fluid-structure interaction problems: application to

Claire Dupont1, Florian De Vuyst2, Anne-Virginie Salsac1

  • 1Biomechanics and Bioengineering Laboratory (UMR 7338), Université de Technologie de Compiègne - CNRS, 60203 Compiègne, France.

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Summary

This study introduces a data-driven model order reduction technique for fluid-structure interaction. The reduced order model accurately predicts capsule deformation in various flow conditions, enabling faster simulations.

Keywords:
Fluid-structure interactiondata-drivendeformable capsuledynamic mode decompositiondynamical systemnon-intrusivereduced order model

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Area of Science:

  • Computational fluid dynamics
  • Fluid-structure interaction modeling
  • Reduced order modeling

Background:

  • High-fidelity simulations of 3D fluid-structure interaction (FSI) are computationally expensive.
  • Model order reduction (MOR) techniques are crucial for efficient simulation of complex dynamical systems.
  • Dynamical data-driven approaches offer a promising avenue for developing accurate and efficient reduced order models (ROMs).

Purpose of the Study:

  • To present a generic, dynamical data-driven model order reduction technique for 3D fluid-structure interaction problems.
  • To develop a reduced order model capable of predicting capsule dynamics across a range of non-dimensional parameters.
  • To establish a foundation for real-time simulation of FSI problems.

Main Methods:

  • Identification of a low-order continuous linear differential system from high-fidelity solver snapshots.
  • Integration of proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and Tikhonov regularization.
  • Application of an interpolation method for parameter space exploration and prediction of capsule dynamics.
  • Numerical analysis of accuracy and stability properties of the developed ROM.

Main Results:

  • The reduced order model accurately predicts the time-evolution of capsule deformation for various parameter values.
  • Numerical experiments demonstrate very good agreement between full-order and reduced-order models using modified Hausdorff distance.
  • The method shows effectiveness for both confined and unconfined flow scenarios.
  • The developed ROM achieves high accuracy and stability.

Conclusions:

  • The presented dynamical data-driven MOR technique is effective for 3D FSI problems involving capsule dynamics.
  • This approach significantly reduces computational cost while maintaining high prediction accuracy.
  • The work serves as a key step towards real-time simulation of FSI, with potential for extension to non-linear systems.
  • The technique offers a valuable tool for rapid design and development of innovative devices.