沈阳航空航天大学学报2025，Vol.42Issue(4)：30-36,7.DOI:10.3969/j.issn.2095-1248.2025.04.005
基于AGA-LSTM神经网络的飞机气动力建模方法
Aircraft aerodynamic modeling method based on AGA-LSTM neural network
李东霏 1高婷 1张鹏1
作者信息
- 1. 沈阳航空航天大学 民用航空学院,沈阳 110136
- 折叠
摘要
Abstract
Addressing the high-precision modeling requirements of unsteady aerodynamics during complex aircraft maneuvers,a method for modeling non-steady aerodynamic forces based on an adaptive genetic algorithm(AGA)optimized long short-term memory(LSTM)neural network was proposed.Computational fluid dynamics(CFD)simulations were conducted to capture maneuver flight data during rapid turns at varying bank angles and rolling and looping maneuvers at different Mach numbers.An AGA-LSTM model was developed using this data to predict aerodynamic coefficients under non-steady conditions.Specifically,predictions for the aerodynamic coefficients during a 60° bank angle rapid turn maneuver were made,demonstrating accurate estimation of lift coefficient,drag coefficient,and pitch moment coefficient that closely matched CFD simulation results.To further validate the proposed model's accuracy,predictions were compared with CFD simulation data and a traditional LSTM neural network model for Envelopment maneuvers.The results indicate that the AGA-LSTM neural network model provides closer predictions to simulation data compared to traditional LSTM models,thus offering improved prediction accuracy.关键词
长短时记忆/自适应遗传算法/非定常气动力建模/计算流体力学/机动飞行Key words
long short-term memory/adaptive genetic algorithm/unsteady aerodynamic modeling/computational fluid dynamics/maneuver flight分类
航空航天引用本文复制引用
李东霏,高婷,张鹏..基于AGA-LSTM神经网络的飞机气动力建模方法[J].沈阳航空航天大学学报,2025,42(4):30-36,7.
