Unsteady Steam Turbine Optimization Using High-Fidelity Computational Fluid Dynamics

Abstract

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.