Abstract:
Wind-tunnel experiments, numerical simulations, and flight tests are the three major means for aircraft aerodynamic research. However, each method has its limitation, thus it is difficult to accurately predict aerodynamic characteristics of an aircraft in its full flight envelope by a single approach. Data fusion is important for improving accuracy and enhancing forecasting capabilities. In the parameter space, the fused data could be a supplement to high-fidelity data. Meanwhile, the fused data outside the parameter space can provide a reference for the variation trend of the data. Therefore, aerodynamic data obtained by different methods need to be fused. To this end, two data fusion algorithms are proposed. One is a weighted fusion algorithm based on uncertainty, which uses the Gaussian process regression to obtain the characteristics of aerodynamic data from different sources before performing a weighted fusion. The other is an agent fusion model which is established by using the CoKriging algorithm. Aerodynamic data of an aircraft are taken for comparative analyses. Results show that the prediction accuracy of the Kriging model using single-precision data can be improved by more samples. Both fusion algorithms have significant higher accuracy than the Kriging model using only single-precision data. But the accuracy of the CoKriging algorithm is roughly one order of magnitude higher than the fusion algorithm based on uncertainty.