大迎角机动飞行的气动力建模与飞行仿真

李怀璐; 王旭; 王霄; 赵彤; 张伟伟

doi:10.7527/S1000-6893.2023.28410

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DOI: https://doi.org/10.7527/S1000-6893.2023.28410

大迎角机动飞行的气动力建模与飞行仿真

李怀璐 ,
王旭 ,
王霄 ,
赵彤 ,
张伟伟

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1. 西北工业大学
2. 沈阳飞机设计研究所

收稿日期: 2022-12-19

修回日期: 2023-04-09

网络出版日期: 2023-04-11

基金资助

基于数据融合策略的翼型亚声速动态失速特性预测新方法

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Aerodynamic modeling and flight simulation of maneuverable flight at high angle of attack

LI Huai-Lu ,
WANG Xu ,
WANG Xiao ,
ZHAO Tong ,
ZHANG Wei-Wei

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Received date: 2022-12-19

Revised date: 2023-04-09

Online published: 2023-04-11

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摘要

由于显著的非线性、非定常效应，现有的风洞实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、开环简谐激励与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、集成神经网络模型三种气动力模型，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，飞机响应的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与工程潜力。

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

本文引用格式

李怀璐 , 王旭 , 王霄 , 赵彤 , 张伟伟 . 大迎角机动飞行的气动力建模与飞行仿真[J]. 航空学报, 0 : 0 -0 . DOI: 10.7527/S1000-6893.2023.28410

Abstract

Due to the significant nonlinear and unsteady effects, it is difficult to accurately simulate the high angle of attack maneu-vering characteristics of aircraft by existing wind tunnel experiments and numerical methods. In order to improve the ac-curacy of high angle of attack maneuver flight simulation, the physical-model-embedding ensemble neural network was developed to accurately model the unsteady aerodynamics of aircraft at high angles of attack, and the flight dynamics equations were further coupled in time domain to realize high angle of attack maneuver flight simulation. Taking the F-16 fighter as the research object, the open loop broadband excitation, open loop Due to the significant nonlinear and unsteady effects, it is difficult to accurately simulate the high angle of attack maneu-vering characteristics of aircraft by existing wind tunnel experiments and numerical methods. In order to improve the ac-curacy of high angle of attack maneuver flight simulation, the physical-model-embedding ensemble neural network was developed to accurately model the unsteady aerodynamics of aircraft at high angles of attack, and the flight dynamics equations were further coupled in time domain to realize high angle of attack maneuver flight simulation. Taking the typi-cal fighter as the research object, the open loop broadband excitation, open loop harmonic excitation and post stall ma-neuver flight data of high angle of attack longitudinal flight are taken as the data sample of aerodynamic modeling. Be-sides, the traditional dynamic derivative model, black box neural network model and ensemble neural network model are constructed and compared, and the flight characteristics of coupled simulation are further compared. Besides, the idea of using the flight simulation method to test the robustness of the aerodynamic model is proposed. The results show that the aerodynamic modeling error of the physical-model-embedding ensemble neural network is 57% lower than that of the traditional dynamic derivative model, and has better robustness and stability in the coupling process. The aircraft re-sponse 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 in small sample flight data identification.

Key words： aerodynamic modeling; ensemble neural networks; high angle of attack fight simulation; post stall maneuver; unsteady aerodynamics

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