Robust Numerical Operating-Map Prediction through Systematic Selection of Turbulence Models

Authors

Pascal Franke  - Leibniz Universität Hannover, Germany
Malte Nyhuis - Leibniz Universität Hannover, Germany
Philipp Sauer - Leibniz Universität Hannover, Germany
Lars Wein - Leibniz Universität Hannover, Germany
Dajan Mimic - Leibniz Universität Hannover, Germany

Abstract

The successful design of highly efficient and operationally flexible axial-flow compressors requires reliable CFD predictions. The selection of a suitable turbulence model, including turbulence and transition model extensions, is crucial for an accurate prediction of the integral compressor performance. Often, however, the impact of different models and extensions, such as stagnation-point anomaly fixes and streamline-curvature models depends on the flow field investigated and cannot be determined a priori. Rather, suitable model combinations are selected on a case-to-case basis after extensive sensitivity studies. In order to provide a foundation for a systematic selection of turbulence models, this paper aims to quantify the sensitivities of performance-map predictions with respect to the choice of mesh, turbulent boundary conditions, and various modeling approaches for turbulence and transition. For this purpose, steady-state simulations based on the k- and k--SST turbulence models combined with different transition and turbulence extensions are conducted for a four-stage axial compressor. It is shown that the prediction sensitivity increases for aerodynamically highly loaded operating points.