In this paper, an unsteady investigation of the last two stages of a low-pressure steam turbine with supersonic airfoils near the tip of the last stage’s rotor blade is presented. Goal is the investigation of multistage effects and tip leakage flow in the last stage of the turbine and to provide insight on the stator-rotor flow interaction in the presence of a bow-shock wave. This study is unique in a sense of combining experimental data for code validation and comparison with a numerical simulation of the last two stages of a real steam turbine, including tip-cavity paths and seals, steam modelling and experimental data used as inlet and outlet boundary conditions. Analysis of results shows high unsteadiness close to the tip of the last stage, due to the presence of a bow-shock wave upstream of the rotor blade leading edge and its interaction with the upstream stator blades, but no boundary layer separation on stator is detected at any instant in time. The intensity of the shock wave is weakest, when the axial distance of the rotor leading edge from the upstream stator trailing edge is largest, since it has more space available to weaken. However, a phase shift between the maximum values of static pressure along the suction side of the stator blade is identified, due to the shock wave moving with the rotor blades. Additionally, the bow-shock wave interacts with the blade shroud and the tip leakage flow. Despite the interaction with the incoming flow, the total tip leakage mass flow ingested in the tip-cavity shows a steady behaviour with extremely low fluctuations in time. Finally, traces of upstream stage’s leakage flow have been identified in the last stage, contributing to entropy generation in inlet and outlet of last stage’s stator blade, highlighting the importance of performing multistage simulations.

This paper presents the computational methodology, and experimental investigations accomplished to enhance the efficiency of a turbine stage by applying non-axisymmetric profiling on the rotor hub wall. The experimental setup was a two-stage axial turbine, which was tested at “LISA” test facility at ETH Zurich. The goal was to optimize the interaction of the cavity leakage flow with the rotor passage flow to increase efficiency. The computational optimization was completed using a Genetic Algorithm coupled with an Artificial Neural Network. Unsteady time-accurate simulations were performed, using in-house developed “MULTI3” solver. Besides implementing all geometrical details from the experimental setup into the computational model, it was learned that the unsteady upstream effect could not be neglected. A novel approach was introduced by using unsteady inlet boundary conditions to consider the multistage effect while reducing the computational cost to half. Comprehensive steady and unsteady measurements were performed utilizing pneumatic, Fast Response Aerodynamic (FRAP), and Fast Response Entropy (FENT) probes, on the baseline and profiled test cases. The end-wall profiling was found to be successful in weakening the strength of the hub passage vortex by a 19% reduction in the under-over turning. As a result, the blockage was reduced near the hub region leading to more uniform mass flow distribution along the span. Furthermore, the improvements were confirmed by reductions in entropy, Secondary Kinetic Energy, and pressure unsteadiness. The accurate computational implementations led to an excellent agreement between the predicted and measured efficiency gain.

This paper presents the computational methodology, and experimental investigations accomplished to enhance the efficiency of a turbine stage by applying non-axisymmetric profiling on the rotor hub wall. The experimental setup was a two-stage axial turbine, which was tested at “LISA” test facility at ETH Zurich. The 1st stage was considered to create the flow history for the 2nd stage, which was the target of the optimization. The hub cavity of the 2nd stage was designed with large dimensions as a requirement of a steam turbine. The goal was to optimize the interaction of the cavity leakage flow with the rotor passage flow to reduce the losses and increase efficiency. The computational optimization was completed using a Genetic Algorithm coupled with an Artificial Neural Network on the 2nd stage of the test turbine. Unsteady time-accurate simulations were performed, using in-house developed “MULTI3” solver. Besides implementing all geometrical details (such as hub and tip cavities and fully 3D blade geometries) from the experimental setup into the computational model, it was learned that the unsteady upstream effect could not be neglected. A novel approach was introduced by using unsteady inlet boundary conditions to consider the multistage effect while reducing the computational cost to half. The importance of this implementation was tested by performing a steady simulation on the optimized geometry. The predicted efficiency gain from steady simulations was 4.5 times smaller (and negligible) compared to the unsteady approach. Excluding the cavity geometry was also assessed in a different simulation setup showing 3.9% over-prediction in the absolute efficiency value. Comprehensive steady and unsteady measurements were performed utilizing pneumatic, Fast Response Aerodynamic (FRAP), and Fast Response Entropy (FENT) probes, on the baseline and profiled test cases. The end-wall profiling was found to be successful in weakening the strength of the hub passage vortex by a 19% reduction in the under-over turning. As a result, the blockage was reduced near the hub region leading to more uniform mass flow distribution along the span. The flow angle deviations at the higher span position were also corrected due to better control of the flow angles. Furthermore, the improvements were confirmed by reductions in entropy, Secondary Kinetic Energy, and pressure unsteadiness. The accurate computational implementations led to an excellent agreement between the predicted and measured efficiency gain.

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