Intelligent fusion method of multi-source aerodynamic data for flight tests

Wind tunnel and flight tests are two of the most important methods for aerodynamic analyses and optimization design during the development of aircraft. However, under hypersonic flight conditions, the real gas effects, viscous interference effects, and multi-scale flow fields pose huge challenges to aerodynamic prediction. To improve the consistency of aerodynamic data between wind-tunnel and flight tests, an aerodynamic data fusion framework based on the random forest method for data mining is proposed and applied in the aerodynamic data fusion of a hypersonic aircraft. Feature analyses and ranking of aerodynamic data obtained by ground wind tunnel tests are conducted first. Then aerodynamic data in a flight envelop are cross-validated. Results show that the machine learning framework based on the random forest has good prediction and extrapolation capabilities for the correlation of aerodynamic data obtained by wind-tunnel and flight tests, and can effectively improve the prediction accuracy of aerodynamic data. The method provides a promising solution to the multi-source fusion of aerodynamic data in complex environments.