To address the aerodynamic challenges associated with low-Reynolds-number operation typical of high-altitude cruise conditions in aero-engines, this paper presents an experimental investigation—conducted on a large-scale, low-speed, multi-stage axial turbine facility—in which both the velocity triangles and the reduced wake-passing frequency were held constant. Synchronized hot-film measurements were conducted on the blade surface, and the dynamic flow field at the second-stage stator (S2) exit plane was acquired using a triple- and hot-wire probe. The study examines the effects of Reynolds number (Re = 7.4 × 104 and Re = 8.5 × 104) on the attached-flow transition and wake mixing characteristics of the S2 suction surface. The results indicate that at the lower Reynolds number, the thickened boundary layer and enhanced viscous effects lead to shear sheltering of external disturbances, resulting in a delayed transition onset and attenuated wall shear stress fluctuations. Consequently, the merging and evolution of turbulent spots are restricted, causing the calmed regions at the trailing edge to exhibit stronger intermittency. This process leads to wake broadening, an elevation of global turbulence levels, and a significant increase in velocity deficit magnitude (>1.5%), ultimately resulting in a significantly deeper and wider exit wake profile, indicative of increased aerodynamic loss.
Yingqiang Zhang, Xiao-Dan Zhang, Miaoyi Zhu et al.· The Physics of Fluids· 0 citations
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.
Ruoyu Chen, Chengwu Yang, Lipan Yao et al.· Journal of turbomachinery· 0 citations