Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning

TL;DR

This paper uses machine learning to predict the inaccuracies of RANS turbulence models in transitional flows by analyzing discrepancies with LES simulations, improving understanding of model uncertainties.

Contribution

It introduces a machine learning framework to quantify and predict RANS model discrepancies based on flow features, enhancing turbulence modeling accuracy.

Findings

ML accurately predicts RANS-LES discrepancies

Significant uncertainties identified in RANS models for transitional flows

Flow features effectively inform discrepancy predictions

Abstract

This work determines the inaccuracy of using Reynolds averaged Navier Stokes (RANS) turbulence models in transition to turbulent flow regimes by predicting the model-based discrepancies between RANS and large eddy simulation (LES) models and then incorporates the capabilities of machine learning algorithms to characterize the discrepancies which are defined as a function of mean flow properties of RANS simulations. First, three-dimensional CFD simulations using k-omega Shear Stress Transport (SST) and dynamic one-equation subgrid-scale models are conducted in a wall-bounded channel containing a cylinder for RANS and LES, respectively, to identify the turbulent kinetic energy discrepancy. Second, several flow features such as viscosity ratio, wall-distance based Reynolds number, and vortex stretching are calculated from the mean flow properties of RANS. Then these flow features are regressed onto the discrepancy using a Random Forests regression algorithm. Finally, the discrepancy of the test flow is predicted using the trained algorithm. The results reveal that a significant discrepancy exists between RANS and LES simulations and ML algorithm successfully predicts the increased model uncertainties caused by the employment of k-omega SST turbulence model for transitional fluid flows.