气动弹性
结构工程
直升机旋翼
梁(结构)
转子(电动)
图像扭曲
Timoshenko梁理论
涡轮叶片
空气动力学
机械
工程类
计算机科学
物理
涡轮机
机械工程
人工智能
作者
Lei Shang,Pinqi Xia,Dewey H. Hodges,Lin Changliang
出处
期刊:Journal of The American Helicopter Society
[American Helicopter Society]
日期:2023-04-01
卷期号:68 (2): 127-142
标识
DOI:10.4050/jahs.68.022011
摘要
In this paper, the geometrically exact beam model and aeroelastic solution methods for composite rotor blades in forward flight by the latest variational asymptotic beam sectional analysis (VABS) have been employed. The geometrically exact beam equations of motion in the mixed variational form and the latest VABS are used to deal with one-dimensional blade analysis and the structural property of blade cross section, respectively. The methods can be used for the aeroelastic solution of composite rotor blades with arbitrary cross-sectional shape and material distribution, large deflections and significant nonclassical effects such as cross-sectional warping, transverse shear deformation, and elastic couplings caused by anisotropic material properties. The Peters–He finite state dynamic inflow model and the Peters finite state airloads theory are used to calculate the induced velocity and blade airloads, respectively. An auto-pilot trim scheme is used for calculating the blade pitch controls to meet the trim requirements. The convergence issue encountered when solving the geometrically exact, mixed variational aeroelastic equations in time domain has been successfully addressed. The values of the empirical parameters in the auto-pilot trim scheme for the presented aeroelastic model have been properly selected. The accuracy of the presented aeroelastic modeling and solution methods has been verified against the SA349/2 flight-test data. The influence of transverse shear deformation on the aeroelastic response of composite rotor blades was also investigated, indicating that this effect has a nonnegligible influence on the aeroelastic response of the five different kinds of elastically coupled hingeless composite rotors investigated in this paper.
科研通智能强力驱动
Strongly Powered by AbleSci AI