Numerical optimization of a stator vane setting in multistage axial-flow compressors

Abstract

This paper presents a numerical methodology for optimizing a stator stagger setting in a multistage axial-flow compressor environment. The method involves simultaneous resetting of several blade rows, which influences overall performance in a complex manner. The paper is presented in four parts: modelling the effect of stagger setting on individual stage performance, overall performance including surge point prediction, stagger setting optimization and numerical examples. The stage performance model is a one-dimensional meanline method where correlations are used to introduce real flow effects. The method uses (experimental or predicted) stage characteristics at design (nominal) setting to generate characteristics at other settings. A stage-by-stage model is used to ‘stack’ the stages together with a dynamic surge prediction model. A direct search method incorporating a sequential weight increasing factor technique (SWIFT) was then used to optimize stagger setting. The objective function in this optimization is penalized externally with an updated factor which helped to accelerate convergence. The methodology has been incorporated into a FORTRAN program and validated using data from a seven-stage aircraft compressor with hypothetical variable stagger vanes. Results have demonstrated that variable stagger is a powerful method to rematch stages which can be used to improve desired overall performance. Parametric studies on the optimization algorithm have also been conducted where it showed numerical stability and fast convergence.