关键词: SA (Spalart-Allmaras) 湍流模型, 敏感度, 极端梯度提升(extreme gradient boost-ing, XGBoost), 线性回归, 系数修正

Abstract: This paper presents a combined approach integrating the modified Morris classification and screening method with extreme gradient boosting (XGBoost), driven by computational fluid dynamics (CFD) data. The methodology is applied to modify the closure coefficient of the Spalart-Allmaras (SA) turbulence model. The utilization of the classification and screening method effectively narrows the research scope of the closure coefficient. Using the XGBoost method, a highly accurate fitting model can be obtained even with a small-scale data set, leading to effective improvements in the efficiency of coefficient modification. Employing this method, numerical experiments are conducted for the flow over the three-dimensional (3D) DLR-F6-WB configuration. The experimental results demonstrate the method’s capability to rectify coefficients on complex 3D models based on small sample data. Consequently, the accuracy of the modified lift-drag coefficients has been significantly improved.

Key words: Spalart-Allmaras (SA) turbulence model, sensitivity, extreme gradient boosting (XGBoost), linear regression, coe?cient modi?cation