非定常气动载荷场融合建模方法探索及验证

The virtual flight test of aircraft involves multi-physical field simulation of many high-precision discipline models such as aerodynamics and structure. Accurate and fast unsteady aerodynamic load field calculation is the key constraint. At present，the calculation cost of unsteady aerodynamics based on computational fluid dynamics is very expensive. In order to improve the calculation efficiency of high-precision unsteady aerodynamic load field and ensure the calculation accuracy， this paper proposes an efficient unsteady aerodynamic load field prediction method based on multi-source data fusion based on Co-Kriging model and POD field reduction. Taking the 4% thickness arc wing as the test object， the unsteady aerodynamic load field is constructed by integrating the low-precision load data calculated by the local flow piston theory and the high-precision simulation data obtained by computational fluid dynamics. The unsteady aerodynamic load and flutter boundary under different flight conditions are analyzed. The results show that the proposed unsteady aerodynamic load field prediction method based on data fusion has a surface load prediction accuracy of no less than 99.41% when interpolated， a surface load prediction accuracy of no less than 83.32% when interpolated， and a flutter analysis result error of no more than 0.637%. The computational efficiency is improved by 285.89 times.