Assessing turbulence sensitivity using stochastic Monte Carlo analysis

TL;DR

This paper investigates the inherent sensitivity of RANS turbulence models to their coarse-grained description, analyzing how flow parameters influence prediction variability and implications for model formulation.

Contribution

It introduces a stochastic Monte Carlo approach to quantify turbulence sensitivity in RANS models based on internal flow structure characterization.

Findings

Prediction intervals depend on flow topology and initial Reynolds stresses.

Variability increases with normalized strain rate.

Insights into turbulence model formulation and limitations.

Abstract

Reynolds Averaged Navier Stokes (RANS) models represent the workhorse for studying turbulent flows in industrial applications. Such single-point turbulence models have limitations in accounting for the influence of the non-local physics and flow history on the evolution of the turbulent flow. In this context, we investigate the sensitivity inherent to single-point models due to their characterization of the internal structure of homogeneous turbulent flows solely by the means of the Reynolds stresses. For a variety of mean flows, we study the prediction intervals engendered due to this coarse-grained description. The nature of this variability and its dependence on parameters such as the mean flow topology, the initial Reynolds stress tensor and the normalized strain rate is identified, analyzed and explicated. The ramifications of this variability on the formulation of classical RANS closure models are highlighted.