Multi-hole pressure probes are crucial for turbomachinery flow measurements, yet conventional data processing methods often lack generalization for complex flows. This study introduces an innovative approach by integrating machine learning techniques with the structural risk minimization (SRM) principle, significantly enhancing the generalization capability of regression models. A comprehensive framework has been developed, combining SRM-based machine learning regression methods, such as ridge regression and kernel ridge regression, with hyperparameter optimization and S-fold cross-validation, to ensure robust model selection and accuracy. Validated using the McCormick function and applied to VKI-RG transonic turbine cascade measurements, the SRM-based methods demonstrated superior performance over traditional empirical risk minimization approaches, with lower error ratios and higher $R^2$ values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a $0.63122°$ discrepancy in exit flow angle predictions, guiding the probe design and calibration strategies. This study presents a holistic workflow for improving multi-hole pressure probe measurements under high-subsonic conditions, representing a meaningful enhancement over traditional empirical methods and providing valuable references for practical applications.

The accurate prediction of blade loading is crucial to designing efficient turbomachinery, but traditional methods often neglect the impact of compressibility, leading to inaccuracies at high speeds. This study investigates the effect of compressibility on the blade loading parameters, particularly the Zweifel coefficient (Z_w) and lift coefficient (C_L), in turbine cascades. A novel intermediate method (IM), with averaged flow properties derived from both inlet and outlet conditions, is proposed to enhance the accuracy of Z_w and C_L calculations in compressible flow regimes. This method is based on the extended Kutta–Joukowski theorem for compressible flow and incorporates the Mach number directly into the modified definitions of Z_w and C_L. The analysis reveals that the averaged flow angle (α_m), calculated by using a velocity-weighted approach, serves as a crucial parameter for blade similarity studies. The proposed correction method is applied and validated based on CFD simulations of the VKI-RG turbine cascade. The IM and modified definitions provide a robust framework for accurately predicting blade loading at high speeds, enabling improved design and analysis of turbomachinery.

The calculation formula of the Reynolds number in cascade testing was derived in detail using different viscosity calculation methods, including the index method and the Sutherland method. The relationship between characteristic parameters and the law of characteristic Reynolds number changing with Mach number was obtained. The Reynolds number per unit length (Re/ L) was suggested to be used as a universal characteristic parameter of wind tunnel Reynolds number. Finally, a method to confirm the modular size of a cascade and blade number based on the constraints of aspect ratio, Reynolds number, and vent height was proposed. The error range of Reynolds number calculation method of the cascade wind tunnel test is acceptable in engineering. And the method has important reference value in the fields of wind tunnel construction, cascade experiment, and modular design.

This study focuses on the TM-141 supersonic compressor blade as the subject of investigation. Utilizing the Reynolds-averaged numerical simulation approach, the study examines the impact of the leading-edge sawtooth structure on the blade grid, flow field characteristics, and flow losses. Comparative analysis is conducted between numerical results and experimental data to assess the influence of the leading-edge sawtooth structure under various conditions, including fixed Mach numbers and variable static pressure ratios, fixed static pressure ratios and variable Mach numbers, and fixed Mach numbers and fixed static pressure ratios. The findings reveal that the leading-edge sawtooth structure effectively alters the distribution of total pressure loss coefficients within the blade grid channels, mitigates leading-edge spikes, and improves trailing-edge blade morphology, consequently reducing trailing-edge losses and total pressure loss compared to benchmark blade grids. These results offer insights for mitigating losses and enhancing efficiency in the transonic region of compressor blade operation, thereby providing a foundation for further investigation into the effects of leading-edge sawtooth structures on flow fields.