Multi-source aerodynamic data fusion modeling with XGBoost

Flight tests and CFD (Computational Fluid Dynamics) are frequently used to obtain aerodynamic data. The aerodynamic data obtained by flight tests are usually highly accurate but expensive, while those obtained by CFD are relatively less accurate, but cheap and abundant in data. Therefore, to obtain aerodynamic parameters with higher accuracy at the lowest possible cost, this paper proposes a grafted XGBoost (eXtreme Gradient Boosting) integrated model framework, combining aerodynamic data obtained by CFD and flight tests for fusion modeling. First, a large amount of CFD aerodynamic data are used to establish a low-precision model in the fusion modeling framework regarding the trend of aerodynamic data. Then, a small number of design variables of flight tests are put into the low-precision model to obtain the corresponding aerodynamic output. Finally, the secondary modeling is carried out by integrating the information from other flight test aerodynamic data characteristics to predict the exact aerodynamic output. In order to verify the effectiveness of the proposed algorithm, relevant comparative experiments are conducted. The results show that: 1) Multi-source aerodynamic data fusion modeling has higher prediction accuracy than its single-source counterparts; 2) The integrated learning model has a better generalization performance than traditional machine learning models, among which the XGBoost model has the best generalization ability and can realize the accurate prediction of aerodynamic parameters.