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<p>This paper proposes a machine learning framework for accurately predicting the aerodynamic lift-to-drag ratio (CL/CD) of multi-stepped airfoils under varied flow conditions. Experimental wind-tunnel data were collected for multiple step configurations, and a stacked ensemble model combining XGBoost, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) with a Random Forest meta-learner was developed for prediction. The proposed model achieved a test R<sup>2</sup> of 0.9951 and a tenfold cross-validation R<sup>2</sup> of 0.9872 ± 0.0043, demonstrating superior accuracy compared to individual regressors. This approach provides a fast, data-driven alternative to conventional CFD simulations, enabling reliable prediction of aerodynamic performance and efficient airfoil optimization.</p>

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A machine learning framework for aerodynamic lift-to-drag ratio prediction of multi-stepped airfoils

  • 期刊论文

  • 发表日期：

    2025年12月1日

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  • Ahmed M. Elshewey,
  • Mohamed A. Aziz,
  • Shery Asaad Wahba Marzouk,
  • Ahmed M. Elsayed,
  • Hazem M. El-Bakry,
  • Ahmed M. Osman

摘要

This paper proposes a machine learning framework for accurately predicting the aerodynamic lift-to-drag ratio (CL/CD) of multi-stepped airfoils under varied flow conditions. Experimental wind-tunnel data were collected for multiple step configurations, and a stacked ensemble model combining XGBoost, Support Vector Regression (SVR), and K-Nearest Neighbors (KNN) with a Random Forest meta-learner was developed for prediction. The proposed model achieved a test R2 of 0.9951 and a tenfold cross-validation R2 of 0.9872 ± 0.0043, demonstrating superior accuracy compared to individual regressors. This approach provides a fast, data-driven alternative to conventional CFD simulations, enabling reliable prediction of aerodynamic performance and efficient airfoil optimization.