Dissipation-optimized Proper Orthogonal Decomposition

TL;DR

This paper introduces a dissipation-optimized POD formalism for turbulent flow data, enabling better reconstruction of velocity fields and dissipation across scales, with applications demonstrated on turbulent channel flow data.

Contribution

The paper develops a novel dissipation-optimized POD method with an inverse spectral SRT operator, allowing complete dissipation-focused flow reconstruction and comparison with traditional TKE-optimized POD.

Findings

Dissipation-optimized POD captures a wider range of small-scale structures.

D-POD reconstructs dissipation more efficiently near walls.

E-POD better reconstructs TKE in the bulk flow.

Abstract

We present a formalism for dissipation-optimized decomposition of the strain rate tensor (SRT) of turbulent flow data using Proper Orthogonal Decomposition (POD). The formalism includes a novel inverse spectral SRT operator allowing the mapping of the resulting SRT modes to corresponding velocity fields, which enables a complete dissipation-optimized reconstruction of the velocity field. Flow data snapshots are obtained from a direct numerical simulation of a turbulent channel flow with friction Reynolds number $Re_{\tau}=390$. The lowest dissipation-optimized POD (d-POD) modes are compared to the lowest conventional turbulent kinetic energy (TKE) optimized POD (e-POD) modes. The lowest d-POD modes show a richer small-scale structure, along with traces of the large-scale structure characteristic of e-POD modes, indicating that the former capture structures across a wider range of spatial scales. Profiles of both TKE and dissipation are reconstructed using both decompositions, and reconstruction convergences are compared in all cases. Both TKE and dissipation are reconstructed more efficiently in the dissipation-rich near-wall region using d-POD modes, and in the TKE-rich bulk using e-POD modes. Lower modes of either decomposition tend to contribute more to either reconstructed quantity. Separating each term into eigenvalues and factors relating to the inherent structures in each mode reveals that higher e-POD modes tend to encode more dissipative structures, whereas the structures encoded by d-POD modes have roughly constant inherent TKE content, supporting the hypothesis that structures encoded by d-POD modes tend to span a wide range of spatial scales.