On the Role of Low Energy Modes of the Flow on Sub-Grid Scale Parameter Prediction

TL;DR

This study investigates how low energy flow modes influence subgrid scale parameter prediction in Large Eddy Simulation, using POD and reconstructed PIV data to improve model accuracy by removing dominant energy modes.

Contribution

It demonstrates that removing high energy modes enhances the accuracy of SGS stress prediction in mixed models, highlighting the importance of low energy modes.

Findings

SGS stress prediction improves after removing high energy modes.

Mixed models outperform similarity models in SGS prediction.

Reconstruction of PIV data with gappy POD enables detailed flow mode analysis.

Abstract

Large Eddy Simulation is based on decomposition of turbulent flow structures to large energy containing scales and small subgrid scales. The present study captures all flow low energy modes of a sample shear layer using the proper orthogonal decomposition (POD) method. In order to analyze the role of flow low energy modes on subgrid scale parameter prediction, the a-priori approach is chosen on a stereoscopic particle image velocimetry (SPIV) data that its missing/erroneous data is reconstructed using the gappy POD method. Particularly, similarity and mixed models are used to evaluate the SGS parameters. The results of the mixed model are compared, before and after removing the high energy modes. It is shown that SGS stress is predicted more accurately in the mixed model after removing the highest energy mode.