超/高超声速飞行器动态稳定性导数极快速预测方法

doi:10.7527/S1000-6893.2019.23545

Abstract

Abstract: In the early stage of aircraft design, it is necessary to predict the dynamic stability derivatives under a large number of operating conditions. An extremely efficient prediction technique of dynamic stability derivatives for high-speed flight vehicles is developed in this paper. Firstly, based on the local piston theory, the unsteady aerodynamics of high-speed flight vehicles can be divided into the non-disturbance aerodynamic component and the disturbance aerodynamic component. Secondly, the local surface inclination method and the isentropic flow relation after a shock wave are employed to solve the surface flow variables, and then the unsteady aerodynamic forces are calculated by combining the unsteady motion. Finally, the dynamic derivatives are extracted and identified from unsteady aerodynamic forces using the undetermined coefficient method. Our method overcomes the time-consuming of the traditional CFD method that is dependent and coupled with numerical simulation flow field variables. Typical test cases indicate that our method can predict the trend of dynamic derivatives well under both supersonic and hypersonic conditions. At last, our method is successfully applied to dynamic derivatives prediction of flight vehicles with complex shape, and the error sources with CFD method are discussed. The efficient dynamic derivatives prediction technique in this paper can be used to screen layout of high-speed flight in conceptual design stage.

Key words: dynamic derivatives, forced vibration motion, dynamic stability, local piston theory, hypersonic, supersonic

CLC Number:

V221.3

LI Zhengzhou, GAO Chang, XIAO Tianhang, MA Zicheng, XIAO Jiliang, ZHU Jianhui. Extremely efficient prediction technique of dynamic derivatives for super/hypersonic flight vehicles[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(4): 123545-123545.

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Extremely efficient prediction technique of dynamic derivatives for super/hypersonic flight vehicles

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