Statistical evaluation of stability margin of a multi-stage compressor with geometric variability using adaptive polynomial chaos-Kriging model

Compressed air energy storage systems must promptly adapt to power network demand fluctuations, necessitating a high surge margin in the compression system ensure safety. It is challenging completely eliminate blade geometric variations caused by limited machining precision, important effects of which should be considered during aerodynamic shape design and production inspection. The present paper explores uncertainty impact deviations on stability multi-stage axial compressor at low rotational speed. Initially, an adaptive polynomial chaos expansion-based universal Kriging model introduced, its superior response performance addressing high-dimensional quantification problems validated through rigorous analytical engineering tests. Then, this used statistically evaluate improvement (SMI) due Gaussian realistic variabilities separately. results show that mean standard deviation SMI are −0.11% 0.5% under variability, while those 0.33% 0.39% variability. For both variabilities, stagger angle maximum thickness first-stage rotor most influential parameters controlling margin. Finally, underlying mechanism investigated. variation primarily from alteration inlet incidences, affecting size tip leakage vortex blockage boundary-layer separation regions near rotor.