Numerical identification of separation bubble in an ultra-high-lift turbine cascade using URANS simulation and proper orthogonal decomposition

Abstract

The flow-field inside a gas turbine engine, especially in the low-pressure turbine, is very complicated as it is normally accompanied by unsteady flow structures, strong and rapidly changing pressure gradients, intermittent transition of boundary layer, and flow separation and reattachment, especially during off-design performance. In this article, flow separation and reattachment on the suction side of an ultra-high-lift low-pressure turbine blade is studied and characterized using 3D Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations. For turbulence modeling, transitional-SST method (γ-Reθ) is adopted. The simulations are performed at the exit Reynolds numbers of 200,000 and 60,000, and at a constant isentropic exit Mach number of 0.4. The shape and extent of the separation bubble are primarily dependent on large vortical structures due to the Kelvin-Helmholtz instability and spanwise vortex tube shedding. Therefore, a better prediction of these phenomena could result in a more realistic separation bubble identification and consequently more accurate profile loss assessment. In order to better capture the transitional flow characteristics, which are not often readily available from conventional computational fluid dynamics simulations, the method of Proper Orthogonal Decomposition (POD) is used in this study. Non-coherent structures in the main flow, such as separation bubble, are investigated and studied. The POD modes of pressure-field are analyzed to clarify the generation of spanwise vortex tubes after separation point. In the higher Reynolds number, low-energy small-scale structures in the separation zone and downstream of the trailing edge are observed from the POD analysis. In the lower Reynolds number, high-energy large-scale structures shed from the separated shear layer are identified, which are responsible for increasing turbulent kinetic energy as well as increasing profile losses. This study also shows that the combination of URANS and POD can successfully be used to identify the separation bubble.