Numerical investigation on ultra-high-lift low-pressure turbine cascade aerodynamics at low Reynolds numbers using transition-based turbulence models

ABSTRACT

The performance of ultra-high-lift (UHL) low-pressure turbine (LPT) is subject to complex flow phenomena (e.g. separation, transition and reattachment) which require advanced modelling for accurate numerical predictions. The feasibility and fidelity of three widely used transition-based turbulence models are evaluated in the Reynolds-Averaged Navier-Stokes (RANS) prediction of low-Reynolds number flows in linear UHL LPT cascade (T106C). All three transition models prove to capture the tendency that the size of separation bubble decreases with the increase of Reynolds number or inlet turbulence intensity. It turns out that intermittency factor-transition momentum thickness Reynolds number based shear stress transport turbulence model is the most accurate among the three models, expect for the clean inlet case at an isentropic outlet Reynolds number of $8\times {10}^4$. It is suggested that different viscosity ratios should be prescribed at the inlet for various models to mimic the effect of turbulence intensities precisely. In order to take into account the periodic wakes in computation, a moving cylindrical bar is added to the cascade inlet. The assessment of the capability of three models in predicting unsteady wake induced transition is carried out for selected Reynolds numbers. Some practical suggestions are given for the use of transition models based on RANS equations in simulation of the ultra-high-lift LPT cascade flows at low Reynolds numbers.

Keywords:

• Transition model
• low-pressure turbine cascade
• ultra-high-lift blade
• RANS
• separation induced transition

Acknowledgments

Numerical simulations were carried out on the Polaris computing platform of Peking University in Beijing and the Tianhe-2 supercomputing facility at National Supercomputer Center in Guangzhou, China. We acknowledge the financial supports provided by National Natural Science Foundation of China (Grants No. 91852112 and No. 11988102) and National Key Project (Grant No. GJXM92579).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

We acknowledge the financial supports provided by National Natural Science Foundation of China (Grants No. 91852112 and No. 11988102) and National Key Project (Grant No. GJXM92579).