Uncertainty analysis of URANS simulations coupled with an anisotropic pressure fluctuation model

TL;DR

This paper assesses how uncertainties in turbulence modeling affect pressure and velocity fluctuation predictions in turbulent flows, using sensitivity analysis, surrogate models, and Bayesian inference to improve model calibration.

Contribution

It introduces a combined approach of sensitivity analysis, surrogate modeling, and Bayesian inference to quantify and reduce turbulence model uncertainties in URANS simulations.

Findings

Calibrated parameters improve agreement with reference data in channel flow.

Parameter identifiability issues are observed in annular flow but predictions remain consistent.

The study highlights both the potential and limitations of model calibration in wall-bounded turbulence.

Abstract

Accurate prediction of pressure and velocity fluctuations in turbulent flows is essential for understanding flow-induced vibration and structural fatigue. This study investigates the role of turbulence model parameter uncertainty in such predictions using a combination of global sensitivity analysis, surrogate modeling, and Bayesian inference. The methodology is applied to two fluid-only flow cases: turbulent channel flow and turbulent annular flow. In the channel flow case, calibrated parameter distributions lead to improved agreement with reference data. In the annular case, limited parameter identifiability is observed, though predictions remain consistent with high-fidelity trends. The results demonstrate both the potential and limitations of model calibration strategies in wall-bounded turbulent flows.