Numerical Design of Experiments for Repeating Low-Pressure Turbine Stages Part II: Effect of Reynolds Number on Different Blade Geometries

Abstract The trend towards more compact and efficient low-pressure turbine (LPT) designs can benefit significantly from advanced numerical predictive tools. Faithfully capturing the complex transitional and turbulent nature of unsteady flows in LPTs often necessitates high-fidelity methods such as Large Eddy Simulations (LES) for accurate predictions of turbine efficiency and loss generation. Integrating high-fidelity simulations into design cycles, which are currently predominantly driven by rapid low-fidelity Unsteady Reynolds-Averaged Navier-Stokes (URANS) calculations, requires cutting-edge numerical tools that leverage modern high-performance computing architectures. In Part II of this paper, we present results from a newly established, highly-resolved LES and state-of-the art URANS database for three newly designed LPT profiles. These are a conventional standard lift profile, a front-loaded high-lift profile, and an aft-loaded profile. These profiles are evaluated individually within a repeating 1.5-stage LPT configuration operating under engine-like conditions at an isentropic exit Mach number of 0.3. A Reynolds number sweep, ranging from 70,000 to 320,000, captures a broad spectrum of engine-relevant flow conditions. The LES study incorporates time-resolved, time-averaged, and phase-locked averaged results, enabling a detailed examination of unsteady flow dynamics driven by the rotor blade passing frequency. This analysis provides deep insights into blade-wake interactions, unsteady boundary layer evolution, and loss generation mechanisms across the profiles and Reynolds numbers, highlighting the influence of loading distribution on aerodynamic performance and stage efficiency. On top of that, state-of-the-art URANS of the same configurations are undertaken. While trends are largely recovered, there are important differences with the LES data. These are especially present for the aft-loaded profile, being a radical blade design compared to conventional profiles, as a result of the large flow separation on the blades’ suction side which cannot be adequately captured by URANS. This reveals regions where improvements in turbulence and transition modeling are necessary in a mean sense, and in a phase-locked averaged sense, relevant for capturing the by-nature unsteady flow phenomena in LPT stages. By establishing this high-fidelity database, the work advances the understanding of unsteady aerodynamic phenomena in realistic LPT modules and lays the foundation for the development of more accurate predictive models to improve URANS given the cost of LES. The findings contribute to the design of more compact, efficient, and high-performance turbine systems, addressing critical challenges in modern turbomachinery.