Metamodel-Interpreted Data Mining for Stability and Efficiency Enhancement of Multistage Axial-Flow Compressors

Abstract

Higher efficiency and wider stability of performance map for advanced axial flow compressor will be potentially achieved by using data mining to gain a deep insight into complex correlations between aerodynamic performance and three-dimensional geometry parameters with the rapid growth of computational resources and artificial intelligence. However, few research works have been found on using the data mining that is independent of design optimization to extract priori design guidelines for multi-stage axial flow compressor mainly due to the lack of proper data mining method focused on interpretation of metamodel with full use of limited time-consuming computational fluid dynamics dataset. To tackle this issue, a metamodel-interpreted data mining framework is developed in which extreme gradient boosting metamodel combined with Shapley additive explanation model are employed to locally interpret the feature importance of each sample in the computational fluid dynamics dataset and extract the design guidelines in terms of the most influential geometry parameters and their beneficial variation directions. The developed method is applied to data mining of design guidelines for efficiency and stability enhancement of a front 3.5-stage transonic axial-flow compressor. The results show that followed by the design guidelines, the stall margin at part speed is widened to 5.87% with adjustment of blade lean and twist and further to 23.31% with additional adjustment of variable stators. The peak adiabatic efficiency at design speed is improved by 0.06% in spite of extremely limited potential for efficiency enhancement of the original design.