Application of Artificial Neural Networks in Investigations of Steam Turbine Cascades

Abstract

We present the results of numerical tests of artificial neural networks (ANNs) applied in the investigations of flows in steam turbine cascades. Typical constant cross-sectional blades, as well as high-performance blades, were both considered. The obtained results indicate that ANNs may be used for estimating the spatial distribution of flow parameters, such as enthalpy, entropy, pressure, velocity, and energy losses, in the flow channel. Finally, we remark on the application of ANNs in the design process of turbine flow parts, as an extremely fast complementary method for many 3D computational fluid dynamics calculations. By using ANNs combined with evolutionary algorithms, it is possible to reduce by several orders of magnitude the time of design optimization for cascades, stages, and groups of stages.