A Novel Hybrid Computational Method for Multi-Stage Axial Flow Turbine Performance Prediction

Abstract

Axial flow gas turbines are used in many important applications including power production, gas transportation and jet propulsion. Computational tools are required to predict the performance of an existing turbine or to design a new one. Classical simple flow models, assisted with experimental correlations, provide useful and inexpensive tools for a limited number of configurations. Modern computational fluid dynamic (CFD) methods, on the other hand, are rather generally applicable but are not computationally affordable in many cases. This paper presents a hybrid approach in which a streamline curvature method employs both CFD results and correlated data to predict the performance of a multi-stage axial flow turbine. The proposed algorithm can be run on a personal computer and is not limited to standard blade profiles. Numerical test results are presented which show the applicability of the proposed method in both design and off-design working conditions.