Turbulence Modeling

The vast majority of industrial flows are turbulent, so ANSYS CFX software has always placed special emphasis on providing leading turbulence models to capture the effects of turbulence accurately and efficiently.

For statistical turbulence models, ANSYS CFX provides numerous common two-equation models and Reynolds–stress models. However, particular focus is placed on the widely tested shear stress transport (SST) turbulence model, as it offers significant advantages for non-equilibrium turbulent boundary layer flows and heat transfer predictions. The SST model is as economical as the widely used k-ε model, but it offers much higher fidelity, especially for separated flows, providing excellent answers on a wide range of flows and near-wall mesh conditions. ANSYS CFX complements the SST model with numerous other turbulence modeling innovations, including an automatic wall treatment for maximum accuracy in wall shear and heat transfer predictions and a number of extensions to capture effects like streamline curvature.

ANSYS CFX also has innovative capabilities in the area of laminar-to-turbulent transition modeling. Using CFD to predict the location where the laminar boundary layer becomes turbulent is critical to improving efficiency and/or longevity of equipment in turbomachinery, aerospace, marine and many other industries. The Menter–Langtry γ–θ laminar–turbulent transition model™ gives users a powerful tool to capture various types of transition mechanisms in CFD simulation.

In addition, ANSYS CFX provides a number of scale-resolving turbulence models, such as large- and detached-eddy simulation (LES and DES) models. The development of the novel scale-adaptive simulation (SAS) model is a highlight. This model provides a steady solution in stable flow regions while resolving turbulence in transient instabilities, such as massive separation zones without an explicit grid or time-step dependency. The SAS model has shown excellent results on numerous validation cases. It provides a good option for applications in which resolution of turbulence is required.

View Larger ImageHeated jet in cross flow. Predictions using the SAS turbulence model provide much improved predictions compared to the RANS model.