Multi-fidelity aerodynamic modeling method based on GPR model

Accurate aerodynamic characterization is crucial for optimizing aircraft design and enhancing flight performance. Multi-fidelity modeling approaches improve aerodynamic prediction accuracy and computational efficiency by integrating data from various fidelity levels. To better handle the complex mixed linear and nonlinear correlations coexisting between high- and low-fidelity data, this paper proposes a new multi-fidelity Gaussian process regression (MFGPR) model based on the nonlinear autoregressive Gaussian process (NARGP) framework. By integrating linear and nonlinear kernel functions, the proposed model extends the capabilities of NARGP, enabling it to simultaneously capture complex nonlinear relationships and linear dependencies within multi-fidelity data. To validate the effectiveness of the MFGPR method, two classes of classic analytical functions were selected for numerical testing, and a comparative analysis was performed against three traditional multi-fidelity methods: Cokriging, NARGP, and MFDNN. The results indicate that in handling linear correlations, the prediction performance of MFGPR is consistent with that of CoKriging. Conversely, in modeling nonlinear correlations, MFGPR demonstrates higher prediction accuracy than the other three methods, while offering a clear advantage in modeling efficiency. Furthermore, MFGPR was applied to predict the pressure distribution of the ONERA M6 wing and the drag coefficient of the NACA2414 airfoil, verifying its potential application and superior performance in aerodynamic modeling.