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Towards robust data-driven reduced-order modelling for turbulent flows: application to vortex-induced vibrations.

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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.

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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.