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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.
Journal of Fluid Mechanics
|March 20, 2023
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.
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.
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