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Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations.
Yannick Schubert1, Moritz Sieber1, Kilian Oberleithner1
1Laboratory for Flow Instabilities and Dynamics, Technische Universität Berlin, Berlin, Germany.
Theoretical and Computational Fluid Dynamics
|June 27, 2022
Summary
This study introduces a new method to create accurate reduced-order models (ROMs) for turbulent flows. The approach effectively identifies key flow dynamics from measurement data, minimizing user bias for better insights.
Area of Science:
- Fluid Dynamics
- Computational Science
- Data Analysis
Background:
- Turbulent flows exhibit complex coherent dynamics that are challenging to model.
- Data-driven approaches offer potential for understanding these dynamics.
- Reduced-order modeling (ROM) aims to simplify complex systems using minimal variables.
Purpose of the Study:
- To develop a robust method for identifying data-driven reduced-order models (ROMs) of turbulent flows.
- To minimize the impact of user-selected parameters on model identification.
- To gain insight into coherent flow dynamics from measurement data.
Main Methods:
- Utilized spectral proper orthogonal decomposition (SPOD) for efficient separation of coherent dynamics.
- Employed a two-stage cross-validation procedure (conservative and restrictive sparsification) to identify library functions.
- Defined flow dynamics using polynomial combinations of modal coefficients in nonlinear ordinary differential equations.
Main Results:
- Successfully developed a ROM that reproduces average flow dynamics.
- The method robustly identified nonlinearities and modal interactions.
- Demonstrated the approach using particle image velocimetry (PIV) data from vortex-induced vibration (VIV).
Conclusions:
- The presented method provides a robust way to build ROMs for turbulent flows.
- The identified models reveal interactions between coexisting flow dynamics.
- This data-driven technique enhances understanding of complex fluid phenomena.
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