High-Dimensional Aerodynamic Modeling Prediction Based on Modified RBF Neural Network with Data Assimilation

Abstract

In this paper, the radial basis function (RBF) neural network was modified by data assimilation method to improve the modeling accuracy of high-dimensional aerodynamics. A correction factor γ was introduced into the kernel function of the traditional RBF neural network. The correction factor was corrected by using the EnKF data assimilation algorithm, and it was applied to the high-dimensional aerodynamic modeling prediction of the CRA309 rotor airfoil. The data assimilation method adopts a non-intrusive method to correct only the correction factor of the kernel function without destroying the overall architecture of the neural network, which greatly reduces the optimization parameters and variables, and significantly improves the modeling accuracy and efficiency of the RBF neural network. The modified RBF neural network model was then applied to high-dimensional aerodynamic modeling, and the aerodynamic parameters were predicted by simulation data instead. The design results verify the feasibility of the prediction model. In the case of less wind tunnel test data, it has certain engineering practical value to improve the utilization efficiency of test data.

Keywords

• rbf neural network
• data assimilation
• aerodynamic modeling
• generalization capability