Weimin Deng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Weimin Deng has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Guangming Ren
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2 shared publications - Ming Ni
Department of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2 shared publications - Xiaohua Gan
Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2 shared publications - Zuojun Wei
Department of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China
2 shared publications - Haibo Tao
Department of Mechanics and Aerospace Engineering, College of Engineering, Southern University of Science and Technology, Shenzhen 518055, China
1 shared publication
Latest Publications
- Open Access
Enhancing Multi-Hole Pressure Probe Data Processing in Turbine Cascade Experiments Using Structural Risk Minimization Principle
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 values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a 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.Open AccessEnhancing Multi-Hole Pressure Probe Data Processing in Turbine Cascade Experiments Using Structural Risk Minimization Principle
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 values. Novel insights from SHAP analysis revealed subtle but significant differences in aerodynamic parameters, including a 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. - Open Access
Modified Zweifel Coefficient and Lift Coefficient Definition Considering Compressible Effect
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 (Zw) and lift coefficient (CL), 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 Zw and CL 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 Zw and CL. The analysis reveals that the averaged flow angle (αm), 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.
Open AccessModified Zweifel Coefficient and Lift Coefficient Definition Considering Compressible Effect
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 (Zw) and lift coefficient (CL), 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 Zw and CL 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 Zw and CL. The analysis reveals that the averaged flow angle (αm), 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.