Data association and fusion aerodynamic modeling method based on efficient sampling

Aerodynamic analysis of aircraft design often requires a large amount of high-fidelity (HF) aerodynamic data to improve the performance of aircraft design. However, the acquisition cost is very high. In order to alleviate the contradiction between modeling cost and accuracy, this paper constructs a multi-fidelity aerodynamic data fusion model by associating data with different fidelity. Furthermore, an optimal correlation point selection method and a uniformly enhanced sequential sampling method are proposed to achieve the efficient initialization and fastest convergence of variable-fidelity models based on co-Kriging. As a validation, standard numerical examples are selected to carry out modeling study, and the accuracy of the method is checked by comparing the statistical variables. Finally, the framework is successfully applied in the transonic aerodynamic engineering case of the NACA0012 airfoil. The results show that compared with the traditional model, the proposed method can greatly improve the convergence accuracy and modeling efficiency of the variable-fidelity model with only a small number of high-fidelity samples, which effectively reduces the sampling cost. Compared to the high-fidelity single precision sequence modeling, the error can be reduced by more than a half.