Projection-Based Reduced Order Model and Machine Learning Closure for Transient Simulations of High-Re Flows

TL;DR

This paper introduces an enhanced projection-based reduced-order model combined with machine learning closures to accurately simulate high-Reynolds turbulent flows, capturing complex features efficiently.

Contribution

It develops a novel PBROM framework with turbulence modeling and residual closures, improving accuracy in high-Re flow simulations.

Findings

Significant accuracy improvements with turbulence and residual closures

Effective modeling of complex flow features

Maintains computational efficiency

Abstract

The paper presents a Projection-Based Reduced-Order Model for simulations of high Reynolds turbulent flows. The PBROM are enhanced by incorporating various models of turbulent viscosity and residual closures to model the effects of interactions among the modes and energy dissipations. Remarkable improvements in prediction accuracies are achieved with a suitable turbulent viscosity model and a residual closure. The enhanced PBROM models are demonstrated for high-Re flows past a cylinder in two- and three- dimensions. These enhancements have shown capable of capturing complex flow features and removing unnecessary ones, while not affecting the efficiency of the overall model.