Machine learning for RANS turbulence modeling of variable property flows

2DV Nonlinear k-ε Turbulence Modeling of Stratified Flows

The commonly used linear k-ε turbulence model is shown to be incapable of accurate prediction of turbulent flows, where non-isotropy is dominant. Two examples of non-isotropic flows, which have a wide range of applications in marine waters, are saline water flow and the stratified flows due to temperature gradients. These relate to stratification and consequently, variation of density through...

Progress in Modeling 3D Shear Flows Using RANS Equations and Advanced Turbulence Models

This article reviews recent work in the area of advanced turbulence modeling for complex three-dimensional flows of engineering interest. The term “advanced” refers herein to models that resolve the near-wall flow and account for the anisotropy of the Reynolds stresses. Representative such models, including full Reynolds-stress transport and non-linear, two-equation models, that have already be...

Machine Learning Methods for Data-Driven Turbulence Modeling

As part of a larger effort on data-driven turbulence modeling, this paper investigates machine learning models in their capability to reconstruct the functional forms of spatially distributed quantities extracted from high fidelity simulation and experimental data. Such datasets typically involve very high dimensional feature spaces with sparsely populated and noisy data. A new multiscale Gauss...

Turbulence Modeling Validation for Afterbody Flows

The aerothermodynamics phenomena over space launch vehicles or missiles are a challenging problem on space and aeronautical applications. These physical phenomena can strongly affect the engine's aerodynamic performances. The physical problem met on this geometry is essentially the result of the interaction of two merging flows; one issued from a propulsive jet at high speed and high temperatur...

Modeling unsteady turbulent flows over ripples: RANS versus LES