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 f...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^(2)of 0.9951 and a tenfold cross-validation R^(2) 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.展开更多
基金provided by The Science,Technology&Innovation Funding Authority(STDF)in cooperation with The Egyptian Knowledge Bank(EKB).
文摘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^(2)of 0.9951 and a tenfold cross-validation R^(2) 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.
