Optimization of an Axial Turbine Stage for Aerodynamic Inlet Blockage Available to Purchase

This paper presents a practical and effective optimization approach to minimize 3D-related flow losses associated with high aerodynamic inlet blockage by re-stacking the turbine rotor blades. This approach is applied to redesign the rotor of a low speed subsonic single-stage turbine that was designed and tested in DLR, Germany. The optimization is performed at the design point and the objective is to minimize the rotor pressure loss coefficient as well as the maximum von Mises stress while keeping the same design point mass flow rate, and keeping or increasing the rotor blade first natural frequency. A Multi-Objective Genetic Algorithm (MOGA) is coupled with a Response Surface Approximation (RSA) of the Artificial Neural Network (ANN) type. A relatively small set of high fidelity 3D flow simulations and structure analysis are obtained using ANSYS Workbench Mechanical. That set is used to train and to test the ANN models. The stacking line is parametrically represented using a quadratic rational Bezier curve (QRBC). The QRBC parameters are directly related to the design variables, namely the rotor lean and sweep angles and the bowing parameters. Moreover, it results in eliminating infeasible shapes and in reducing the number of design variables to a minimum while providing a wide design space for the blade shape. The aero-structural optimization of the E/TU-3 turbine proved successful, the rotor pressure loss coefficient was reduced by 9.8% and the maximum von Mises stress was reduced by 36.7%. This improvement was accomplished with as low as four design variables, and is attributed to the reduction of 3D-related aerodynamic losses and the redistribution of stresses from the hub trailing edge region to the suction side maximum thickness area. The proposed parametrization is a promising one for 3D blade shape optimization involving several disciplines with a relatively small number of design variables.