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
王紫浩 1李滚 1刘大伟 2陈德华 3张书俊1
作者信息
- 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
- 3. 电子科技大学航空航天学院,成都 611731;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
- 折叠
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
超临界机翼/抖振边界/气动力系数预测/长短时记忆Key words
supercritical wings/buffeting boundary/aerodynamic coefficient prediction/LSTM引用本文复制引用
基金项目
旋翼空气动力学重点实验室研究开放课题(2104RAL202102-1)
出版年
2024
国家科技期刊平台
NSTL
万方数据