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清华大学学报(自然科学版)  2017, Vol. 57 Issue (7): 673-679    DOI: 10.16511/j.cnki.qhdxxb.2017.25.021
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
大迎角非定常气动参数辨识研究
张婉鑫, 朱纪洪
清华大学 计算机科学与技术系, 北京 100084
Unsteady aerodynamic identification of aircraft at high angles of attack
ZHANG Wanxin, ZHU Jihong
Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
全文: PDF(1207 KB)  
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摘要 针对现有模型在非定常气动参数辨识中存在的局限性,该文对大迎角机动过程非定常气动特性进行了研究,提出了一种建模方法。该方法结合物理机理,以广义气动导数模型为基础,受到Wiener模型建模思想的启发,建立了动态特性和静态特性分解的模块化级联模型。通过平方相关系数评价各模型项对非定常特性的贡献,确定最终模型结构,并给出了参数估计中相关的数据处理方法。用类F-22模型的风洞试验数据验证了提出的辨识方法,结果表明:模型辨识精度高,相对误差可控制在5%以内,可以有效地描述工程中非定常气动参数。
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张婉鑫
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关键词 非定常气动力大迎角建模参数辨识风洞试验气动导数模块化平方相关系数    
Abstract:Existing models cannot accurately identify aircraft at high angles of attack due to the unsteady aerodynamic characteristics high angle of attack flight. This paper presents a model using a dynamic block with a static block based on a general aerodynamic derivatives model, which was inspired by the modelling structure of the Wiener model. The model identification is then based on a squared correlation coefficient that estimates the contribution of each model term. The data processing procedure for the parameter estimations is given. Wind tunnel tests with a model similar to an F-22 are used to verify the method. The results show that the method is able to accurately identify the unsteady aerodynamic parameters with a relative error below 5%. The model can effectively describe the unsteady aerodynamic parameters.
Key wordsunsteady aerodynamic    high angles of attack    modeling    parameter identification    wind tunnel test    aerodynamic derivative    block oriented    squared correlation coefficient
收稿日期: 2016-12-09      出版日期: 2017-07-15
ZTFLH:  V212.1  
通讯作者: 朱纪洪,教授,E-mail:jhzhu@tsinghua.edu.cn     E-mail: jhzhu@tsinghua.edu.cn
引用本文:   
张婉鑫, 朱纪洪. 大迎角非定常气动参数辨识研究[J]. 清华大学学报(自然科学版), 2017, 57(7): 673-679.
ZHANG Wanxin, ZHU Jihong. Unsteady aerodynamic identification of aircraft at high angles of attack. Journal of Tsinghua University(Science and Technology), 2017, 57(7): 673-679.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.021  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I7/673
  图1 Wiener模型
  图2 模型结构
  表1 用于模型辨识的试验数据
  表2 模型结构确定
  表3 模型测试数据
  图3 表3第1组数据对比结果
  图4 表3第2组数据对比结果
  图5 模型与试验数据对比结果
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