A Time-Relaxation Reduced Order Model for the Turbulent Channel Flow

TL;DR

This paper introduces a new time-relaxation reduced order model (TR-ROM) for turbulent channel flow, demonstrating improved accuracy over existing models through numerical simulations and sensitivity analysis.

Contribution

The paper proposes the TR-ROM, a novel Reg-ROM that filters marginally resolved scales, and compares its performance with existing Reg-ROMs in turbulent flow simulations.

Findings

TR-ROM outperforms other Reg-ROMs in accuracy with optimal parameters.

Differential filter generally yields the most accurate results.

Optimal parameters in the reproduction regime are also effective in the predictive regime.

Abstract

Reg-ROMs are stabilization strategies that leverage spatial filtering to alleviate the spurious numerical oscillations generally displayed by the classical G-ROM in under-resolved numerical simulations of turbulent flows. In this paper, we propose a new Reg-ROM, the time-relaxation ROM (TR-ROM), which filters the marginally resolved scales. We compare the new TR-ROM with the two other Reg-ROMs in current use, i.e., the L-ROM and the EFR-ROM, in the numerical simulation of the turbulent channel flow at $Re_{\tau} = 180$ and $Re_{\tau} = 395$ in both the reproduction and the predictive regimes. For each Reg-ROM, we investigate two different filters: (i) the differential filter (DF), and (ii) a new higher-order algebraic filter (HOAF). In our numerical investigation, we monitor the Reg-ROM performance for the ROM dimension, $N$, and the filter order. We also perform sensitivity studies of the three Reg-ROMs for the time interval, relaxation parameter, and filter radius. The numerical results yield the following conclusions: (i) All three Reg-ROMs are significantly more accurate than the G-ROM and (ii) more accurate than the ROM projection, representing the best theoretical approximation of the training data in the given ROM space. (iii) With the optimal parameter values, the TR-ROM is more accurate than the other two Reg-ROMs in all tests. (iv) For most $N$ values, DF yields the most accurate results for all three Reg-ROMs. (v) The optimal parameters trained in the reproduction regime are also optimal for the predictive regime for most $N$ values. (vi) All three Reg-ROMs are sensitive to the filter radius and the filter order, and the EFR-ROM and the TR-ROM are sensitive to the relaxation parameter. (vii) The optimal range for the filter radius and the effect of relaxation parameter are similar for the two $\rm Re_\tau$ values.