In this paper a multi-objective, aerodynamic optimization of a high-pressure steam turbine stage is presented. The overall optimization strategy relies on a neural-network-based approach, aimed at maximizing the stage’s efficiency, while at the same time increasing the stage loading. The stage under investigation is composed of prismatic blades, usually employed in a repeating stage environment and in a wide range of operating conditions. For this reason, two different optimizations are carried out, at high and low flow coefficients. The optimized geometries are chosen taking into account aerodynamic constraints, such as limitation of the pressure recovery in the uncovered part of the suction side, as well as mechanical constraints, such as root tensile stress and dynamic behavior. As a result, an optimum airfoil is selected and its performance are characterized over the whole range of operating conditions. Parallel to the numerical activity, both optimized and original geometries are tested in a linear cascade, and experimental results are available for comparison purposes in terms of loading distributions and loss coefficients. Comparisons between measurements and calculations are presented and discussed for a number of incidence angles and expansion ratios.

This content is only available via PDF.
You do not currently have access to this content.