基于LSTM的超临界机翼抖振边界预测方法

王紫浩; 李滚; 刘大伟; 陈德华; 张书俊

doi:10.7638/kqdlxxb-2023.0146

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基于LSTM的超临界机翼抖振边界预测方法

研究论文 | 更新时间：2026-05-28

- 基于LSTM的超临界机翼抖振边界预测方法
- Buffeting boundary prediction method of supercritical wing using LSTM
- 空气动力学学报 2024年42卷第6期页码：56-65
- 作者机构：
  
  1. 电子科技大学航空航天学院,成都,611731
  2. 中国空气动力研究与发展中心高速空气动力研究所,绵阳,621000
- 作者简介：
  
  王紫浩（1998—），男，云南曲靖人，硕士研究生，研究方向：航空智能计算. E-mail：wangzhuestc@163.com
  张书俊* （1976—），男，河南内乡人，研究方向：流体力学/飞行器多学科设计优化. E-mail： zhangsj@uestc.edu.cn
- 基金信息：
  
  旋翼空气动力学重点实验室研究开放课题（2104RAL202102-1）
- DOI：10.7638/kqdlxxb-2023.0146
  中图分类号： V211.3;O354
- 纸质出版：2024
- 稿件说明：
移动端阅览
王紫浩, 李滚, 刘大伟, 等. 基于LSTM的超临界机翼抖振边界预测方法[J]. 空气动力学学报, 2024, 42(6): 56−65.

WANG Zihao, LI Gun, LIU Dawei, et al. Buffeting boundary prediction method of supercritical wing using LSTM[J]. 2024, 42(6): 56-65.
王紫浩, 李滚, 刘大伟, 等. 基于LSTM的超临界机翼抖振边界预测方法[J]. 空气动力学学报, 2024, 42(6): 56−65. DOI： 10.7638/kqdlxxb-2023.0146

WANG Zihao, LI Gun, LIU Dawei, et al. Buffeting boundary prediction method of supercritical wing using LSTM[J]. 2024, 42(6): 56-65. DOI： 10.7638/kqdlxxb-2023.0146.

摘要

超临界机翼的抖振对运输机的安全性和稳定性有着极大的影响，如何高效准确地确定抖振边界一直是备受关注的研究热点。针对CHN-T1型运输机标模，构建了一种基于长短时记忆（long short-term memory

LSTM）神经网络的超临界机翼抖振边界预测框架。根据CHN-T1标模的计算数据，设计了基于LSTM的气动力系数预测模型和抖振起始迎角判定模型，用于准确预测给定马赫数下气动力系数的变化趋势，并且实现了抖振起始迎角的快速判定；通过整合抖振起始迎角数据确定了CHN-T1标模的抖振边界，并用风洞试验数据验证了结果的准确性。研究结果显示，LSTM模型对气动力系数变化趋势有良好的预测能力，其均方根误差维持在2%以内；同时，在抖振起始迎角的判定方面表现出色，抖振边界的误差保持在2%以内。这些结果验证了该方法在抖振边界预测中的可靠性和准确性，为超临界机翼的抖振研究提供了有力支持。

Abstract

The buffeting of supercritical airfoils significantly impacts the safety and stability of transport aircraft. Efficient and accurate determination of buffeting boundaries has been a focal point of research. In this study

a prediction framework for buffeting boundaries of supercritical airfoils was developed using Long Short-Term Memory (LSTM) neural networks

focusing on the CHN-T1 transport aircraft model. Utilizing computational data from the CHN-T1 model

LSTM-based models for predicting aerodynamic coefficients and determining buffeting onset angles were designed. These models forecast changes in aerodynamic coefficients accurately at a given Mach numbers and rapidly determine buffeting onset angles at a given Mach numbers. Integration of buffeting onset angle data ultimately defined the buffeting boundaries of the CHN-T1 model

validated with wind tunnel experimental data. The results demonstrated the LSTM model's excellent predictive capabilities for aerodynamic coefficient trends

maintaining a RMSE within 2%. Furthermore

the model exhibited outstanding performance in buffeting onset angle determination

with errors remaining within 2%. These findings validate the reliability and accuracy of this approach in buffeting boundary prediction

providing robust support for research on supercritical airfoil buffeting.

关键词

Keywords

references

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