Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference

ZHOU Xiaoyu; HUANG Jiangtao; ZHANG Sheng; LIU Gang

doi:10.7638/kqdlxxb-2023.0087

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Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference

更新时间：2026-05-28

- Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference
- Vol. 42, Issue 3, Pages: 92-101(2024)
- 作者机构：
  
  1. 中国空气动力研究与发展中心空天技术研究所,绵阳,621000
  2. 中国空气动力研究与发展中心,绵阳,621000
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0087
  CLC： V211.4
- Published：2024
- 稿件说明：
移动端阅览
周晓雨, 黄江涛, 章胜, 等. 考虑强风干扰的固定翼飞行器“神经元”飞行气动建模[J]. 空气动力学学报, 2024, 42(3): 92−101.

ZHOU Xiaoyu, HUANG Jiangtao, ZHANG Sheng, et al. Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference[J]. 2024, 42(3): 92-101.
周晓雨, 黄江涛, 章胜, 等. 考虑强风干扰的固定翼飞行器“神经元”飞行气动建模[J]. 空气动力学学报, 2024, 42(3): 92−101. DOI： 10.7638/kqdlxxb-2023.0087

ZHOU Xiaoyu, HUANG Jiangtao, ZHANG Sheng, et al. Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference[J]. 2024, 42(3): 92-101. DOI： 10.7638/kqdlxxb-2023.0087.

摘要

大气环境中的风是影响飞行器实际飞行的主要动态环境干扰因素之一，强风干扰下的复杂空气动力学是固定翼飞行器安全稳定飞行面临的严峻挑战。为提高强风干扰下固定翼飞行器的环境适应能力，发展了一种基于深度元学习的创新固定翼飞行器“神经元”飞行气动建模方法，该方法采用相对于地面坐标系的变量进行描述，根据多元函数的切比雪夫级数理论，将气动力和气动力矩函数分解为不同变量函数的乘积和，通过生成对抗网络技术构建强风干扰下飞行器空气动力模型的共性基函数模型，进而预测飞行器在飞行过程中受到的气动力（矩）。研究结果表明，文章建立的固定翼无人机空气动力共性基函数模型准确，可以较好地预测未知风况条件下飞行器的气动力与气动力矩，为实时空气动力学建模的迁移应用奠定良好基础。

Abstract

The strong and unsteady wind imposes severe challenges to the safe flight and aerodynamic prediction of the fixed-wing aircraft. Traditional aerodynamic models established in the wind-oriented coordinate system have a clear physical meaning but cannot be readily applied to unsteady windy environments. This paper proposes an innovative "neural"-fly aerodynamic modeling method based on deep meta-learning to accurately predict the aerodynamic forces and moments online for fixed-wing aircraft subjected to strong and unsteady wind. Based on variables in a coordinate system relative to the ground

this method decomposes the aerodynamic forces and moments into the sum of polynomial multiplication and constructs the common aerodynamic base functions by a three-step deep meta-learning algorithm using the Generative Adversarial Network. The application of the method for the fixed-wing aircraft F-18 demonstrates that the method can accurately predict the aerodynamic forces and moments under unknown wind conditions

laying a good foundation for real-time aerodynamic modeling.

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references

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