Regions of three-dimensional separations are an inherent flow feature of the corner formed by the suction surface and endwall of axial compressors. RANS turbulence models, common in industrial CFD codes, often struggle in these regions. This paper investigates the use of two hybrid RANS/LES methods as alternatives to pure RANS methods.
SA and SST based Zonal DES (ZDES) are applied to a linear blade cascade case, studied experimentally by Gbadebo [1]. The time-averaged results are compared to steady SA, SST and RSM RANS results. SA model corrections for streamline curvature, anisotropy and non-equilibrium effects are also examined. For the ZDES computations the solver is modified to reduce dissipation at low Mach numbers.
Significant uncertainty is observed in the RANS results, with the origin of the suction surface corner separation occurring too far upstream, and the extent of the corner separation significantly over-predicted. The laminar separation bubble and the turbulent reattachment are also missed. Consequently the surface pressure distribution, exit flow angle and total pressure loss predictions are poor. Conversely, the ZDES results were encouraging; with much better predictions of the pressure distribution, exit flow angle and trailing edge boundary layer displacement thickness. Some RANS corrections proved effective, such as the SA model with Rotation/Curvature correction (SA-RC), however all had deficiencies in some areas.
Although the ZDES results are encouraging it is noted that these computations were two orders of magnitude more computaionally expensive due to the high mesh densities and small time-steps required. For the ZDES results quality indexes are examined in order to determine whether the computational mesh used is sufficient in different flow regions. Mesh generation strategies based on using a pre-cursor RANS solution to obtain a modelled energy spectrum and various turbulent length scales to guide mesh refinement are considered. These can provide a quick estimate of the potential computational cost of LES or hybrid RANS/LES computations from a RANS solution.
