摘要
由于显著的非线性、非定常效应,现有的风洞试验和数值方法仍然很难精确复现飞行器大迎角机动飞行特性。为了提高大迎角机动飞行仿真的精度,通过发展嵌入物理模型的集成神经网络架构来准确模化飞行器大迎角非定常气动力,进一步在时域耦合飞行器运动方程,实现了大迎角机动飞行仿真。以典型战斗机作为研究对象,将大迎角纵向飞行的开环宽频激励、开环简谐激励和过失速机动飞行数据作为气动力建模的样本数据,构建并对比了传统动导数模型、黑箱神经网络模型、集成神经网络模型这3种气动力模型,并进一步对比了耦合仿真的飞行特性,提出了利用飞行仿真方法来对气动力模型进行鲁棒性检验的思路。结果表明,嵌入物理模型的集成神经网络的气动力建模的升力系数误差较传统动导数模型降低了57%,并在耦合过程中具有更好的鲁棒性和稳定性;飞机响应误差较黑箱神经网络模型降低了63%,验证了所提出的建模框架在小样本飞行数据辨识中的优势和工程潜力。
Due to significant nonlinear and unsteady effects,it is difficult to accurately simulate the maneuver flight characteristics of aircraft at high angle of attack by existing wind tunnel experiments and numerical methods.To improve the accuracy of maneuver flight simulation at high angle of attack,the physical-model-embedding ensemble neural network was developed to accurately model the unsteady aerodynamics of aircraft at high angle of attack,and the aircraft motion equation were further coupled in time domain to realize maneuver flight simulation at high angle of attack.Taking a typical fighter as the research object,the open-loop broadband excitation,open-loop harmonic excitation and post-stall maneuver flight data of longitudinal flight at high angle of attack are utilized as sample data for aero-dynamic modeling.Three types of aerodynamic models are constructed and compared,including the traditional dynamic derivative model,the black-box neural network model and the ensemble neural network model.Furthermore,the flight characteristics of coupled simulation are further compared,and the idea of using the flight simulation method to test the robustness of the aerodynamic model is proposed.Results show that the lift coefficient error of aerodynamic modeling of the physical-model-embedding ensemble neural network is 57%lower than that of the traditional dynamic derivative model,and the robustness and stability in the coupling process are better.The aircraft response error is 63%lower than that of the black-box neural network model,which proves the advantages and engineering potential of the proposed modeling framework for small-sample flight data identification.
作者
李怀璐
王旭
王霄
赵彤
张伟伟
LI Huailu;WANG Xu;WANG Xiao;ZHAO Tong;ZHANG Weiwei(School of Aeronautics,Northwestern Polytechnical University,Xi'an 710072,China;AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110035,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2023年第19期78-95,共18页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金面上项目(12072282)。
关键词
气动力建模
集成神经网络
大迎角飞行仿真
过失速机动
非定常气动力
aerodynamic modeling
ensemble neural network
fight simulation at high angle of attack
post-stall maneuver
unsteadyaerodynamics
