Data-driven prediction model for the impact of slotted stator on compressor aerodynamic performance

Abstract Slotted blade technology is a passive flow control strategy that can effectively suppress the boundary layer separation within compressors. To reduce the iteration time of the traditional Design-Experiment-Design method, this study innovatively proposes a fast and universal three-dimensional design method for the slotted blade technology, enabling slot modeling completion within 1 s. Furthermore, combined with machine learning (ML), the mapping relationships between eight design parameters and two key aerodynamic performances – compressor design point efficiency ( $\eta$ DE ) and stator total pressure recovery coefficient at the near-stall point ( $\sigma ^{*}_{NS}$ ) – were pioneeringly established. In this study, the prediction performances of six models were compared: one-dimensional convolutional neural network (1D-CNN), random forest (RF), support vector regression (SVR), Gaussian process regression (GPR), multi-layer perceptron (MLP) and long short-term memory network (LSTM). The results indicate that 1D-CNN achieves the highest prediction accuracy: for the $\eta$ DE , the mean absolute error (MAE) and coefficient of determination ( R 2 ) are 0.041 and 0.987, respectively; for the $\sigma ^{*}_{NS}$ , the MAE and R ² are 0.479 × 10 −3 and 0.955, respectively. Notably, the computational time of the 1D-CNN model is 99.11% less than that of the computational fluid dynamics (CFD). The Shapley Additive exPlanations (SHAP) method was employed to reveal the effects of design parameters on the compressor aerodynamic performance. Notably, the slot outlet axial position ( Z out ) exerts the most significant influence on the $\eta$ DE , while the slot outlet radial position close to the casing ( R 1_out ) has the strongest impact on the $\sigma ^{*}_{NS}$ . This study provides theoretical support and valuable references for the intelligent design of slotted blade technology.