将修正 Morris 分类筛选法与极端梯度提升 (extreme gradient boosting, XG-Boost) 相结合, 在计算流体动力学 (computational fluid dynamics, CFD) 数据驱动下,用于 SA(Spalart-Allmaras) 湍流模型闭合系数的修正. 利用分类筛选法有效缩小闭合系数研究范围, 同时依据 XGBoost 方法在小规模数据集下取得精度较高的拟合模型, 有效提升系数修正效率. 在三维 DLR-F6-WB 构型下进行了数值实验, 实验结果显示利用本方法能够在三维复杂模型上基于小样本数据进行系数修正, 修正后的升阻力系数计算精度得到了显著提升.

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.