@article{ZHOU2024, 
author = {Xiaoyu ZHOU and Jiangtao HUANG and Sheng ZHANG and Gang LIU},
title = {Aerodynamic modeling of “Neural”-Fly for fixed-wing aircraft considering strong wind interference},
year = {2024},
journal = {Acta Aerodynamica Sinica},
volume = {42},
number = {3},
pages = {92-101},
keywords = {fixed-wing aircraft, aerodynamic modeling, neural-fly, generative adversarial network, generic base},
url = {https://www.sciopen.com/article/10.7638/kqdlxxb-2023.0087},
doi = {10.7638/kqdlxxb-2023.0087},
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.}
}