Design optimization of a multi-stage axial compressor using throughflow and a database of optimal airfoils

Abstract

The basic tool set to design multi-stage axial compressors consists of fast codes for throughflow and blade-to-blade analysis. Detailed blade row design is conducted with 3D CFD, mainly to control the end wall flow. This work focuses on the interaction between throughflow and blade-to-blade design and the transition to 3D CFD. A design strategy is presented that is based on a versatile airfoil family. The new class of airfoils is generated by optimizing a large number of airfoil shapes for varying design requirements. Each airfoil geometry satisfies the need for a wide working range as well as low losses. Based on this data, machine learning is applied to estimate optimal airfoil shape and performance. The performance prediction is incorporated into the throughflow code. Based on a throughflow design, the airfoils can be stacked automatically to generate 3D blades. On this basis, a 3D CFD setup can be derived. This strategy is applied to study upgrade options for a 15-stage stationary gas turbine compressor test rig. At first, the behavior of the new airfoils is studied in detail. Afterwards, the design is optimized for mass flow rate as well as efficiency. Selected configurations from the Pareto-front are evaluated with 3D CFD.