Multi-fidelity validation of variable-density turbulent mixing models

TL;DR

This study validates variable-density turbulent mixing models using experimental data and computational simulations, highlighting LES's effectiveness and RANS's limitations in complex geometries and flow conditions.

Contribution

Introduces an ensemble-based validation approach for turbulence models in complex geometries with a tightly coupled experimental and computational framework.

Findings

LES models show good agreement with experimental data

RANS models match data only at late flow stages

Initial condition sensitivity affects LES predictions

Abstract

In this study, ensembles of experimental data are presented and utilized to compare and validate two models used in the simulation of variable density, compressible turbulent mixing. Though models of this kind (Reynolds Averaged Navier Stokes and Large Eddy Simulations) have been validated extensively with more canonical flows in previous studies, the present approach offers novelty in the complexity of the geometry, the ensemble based validation, and the uniformity of the computational framework on which the models are tested. Moreover, all experimental and computational tasks were completed by the authors which has led to a tightly coupled experimental configuration with its digital twin. The experimental divergent shock tube facility and its data acquisition methods are described and replicated in simulation space. A 2D Euler model which neglects the turbulent mixing at the interface is optimized to experimental data using a Gaussian process. This model then serves as the basis for both the 2D RANS and 3D LES studies that make comparisons to the mixing layer data from the experiment. RANS is shown to produce good agreement with experimental data only at late flow development times. The LES ensembles generally show good agreement with experimental data, but display sensitivity to the characterization of initial conditions. Resolution dependent behavior is also observed for certain higher-order statistics of interest. Overall, the LES model successfully captures the effects of divergent geometry, compressibility, and combined non-linear instabilities inherent to the problem. The successful prediction of mixing width and its growth rate highlight the existence of three distinct regimes in the development of the instability, each with similarities to previously studied instabilities.