LOW-REYNOLDS-NUMBER COMPRESSOR DEVIATION ANGLE MODEL BASED ON PHYSICS-ENHANCED TWO-STAGE SYMBOLIC REGRESSION APPROACH

Abstract

Traditional compressor deviation angle models, primarily developed for NACA and double-circular-arc airfoils, cannot accurately predict the deviation characteristics of modern aerodynamically optimized blades and generally neglect Reynolds number (Re) and Mach number (Ma) effects, limiting their applicability to low-Re compressor designs. To improve prediction accuracy for high-loading compressors under varying Reynolds and Mach numbers, this study develops a physics-enhanced two-stage symbolic regression (PE-TSR) model based on a data-fusion framework. The first-stage symbolic regression model captures the primary effects of blade geometry and aerodynamic loading on deviation angle, while the second-stage model introduces physics-based correction terms associated with Mach-geometry coupling and viscous flow development. The proposed PE-TSR model achieves an average relative error of 2.17% on the test set, representing a 73.9% improvement over the classical Lieblein empirical model. On an independent experimental dataset outside the training set, the model yields a mean absolute error of 1.68° in deviation angle prediction. Sobol global sensitivity analysis indicates that inlet metal angle, loading distribution, and blade camber angle dominate the primary deviation trend, whereas Mach number, Reynolds number, and maximum reverse-flow velocity mainly act as corrective factors that compensate for systematic biases of the primary model under extreme operating conditions. Furthermore, analysis of the PE-TSR analytical formulation reveals that the influence of flow compressibility on the deviation angle is strongly dependent on the blade geometric loading state.