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arXiv · 2502.14722

Model-based time super-sampling of turbulent flow field sequences

Qihong Lorena Li-Hu·Patricia García-Caspueñas·Andrea Ianiro·Stefano Discetti
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Abstract

We propose a novel method for model-based time super-sampling of turbulent flow fields. The key enabler is the identification of an empirical Galerkin model from the projection of the Navier-Stokes equations on a data-tailored basis. The basis is obtained from a Proper Orthogonal Decomposition (POD) of the measured fields. Time super-sampling is thus achieved by a time-marching integration of the identified dynamical system, taking the original snapshots as initial conditions. Temporal continuity of the reconstructed velocity fields is achieved through a forward-backwards integration between consecutive measured Particle Image Velocimetry measurements of a turbulent jet flow. The results are compared with the interpolation of the POD temporal coefficients and the low-order reconstruction of data measured at a higher sampling rate. In both cases, the results obtained show the ability of the method to reconstruct the dynamics of the flow with small errors during several flow characteristic times.

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Qihong Lorena Li-Hu, Patricia García-Caspueñas, Andrea Ianiro, Stefano Discetti. 2025-02-20. Model-based time super-sampling of turbulent flow field sequences. https://doi.org/10.1103/2lqd-g9mt

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