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

TL;DR

This paper introduces a model-based super-sampling method for turbulent flow sequences using empirical Galerkin models derived from POD, enabling accurate flow reconstruction from limited measurements.

Contribution

The method combines POD-based basis functions with Galerkin modeling and forward-backward integration to enhance temporal resolution of turbulent flow data.

Findings

Accurately reconstructs flow dynamics over multiple characteristic times

Outperforms simple interpolation of POD coefficients

Maintains small errors during flow evolution

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.