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Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (19): 128410-128410.doi: 10.7527/S1000-6893.2022.28410

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Aerodynamic modeling and flight simulation of maneuver flight at high angle of attack

Huailu LI1, Xu WANG1, Xiao WANG2, Tong ZHAO2, Weiwei ZHANG1()   

  1. 1.School of Aeronautics,Northwestern Polytechnical University,Xi’an 710072,China
    2.AVIC Shenyang Aircraft Design and Research Institute,Shenyang 110035,China
  • Received:2022-12-19 Revised:2023-03-10 Accepted:2023-03-29 Online:2023-10-15 Published:2023-04-12
  • Contact: Weiwei ZHANG E-mail:aeroelastic@nwpu.edu.cn
  • Supported by:
    National Natural Science Foundation of China(12072282)

Abstract:

Due to significant nonlinear and unsteady effects, it is difficult to accurately simulate the maneuver flight characteristics of aircraft at high angle of attack by existing wind tunnel experiments and numerical methods. To improve the accuracy of maneuver flight simulation at high angle of attack, the physical-model-embedding ensemble neural network was developed to accurately model the unsteady aerodynamics of aircraft at high angle of attack, and the aircraft motion equation were further coupled in time domain to realize maneuver flight simulation at high angle of attack. Taking a typical fighter as the research object, the open-loop broadband excitation, open-loop harmonic excitation and post-stall maneuver flight data of longitudinal flight at high angle of attack are utilized as sample data for aerodynamic modeling. Three types of aerodynamic models are constructed and compared, including the traditional dynamic derivative model, the black-box neural network model and the ensemble neural network model. Furthermore, the flight characteristics of coupled simulation are further compared, and the idea of using the flight simulation method to test the robustness of the aerodynamic model is proposed.Results show that the lift coefficient error of aerodynamic modeling of the physical-model-embedding ensemble neural network is 57% lower than that of the traditional dynamic derivative model, and the robustness and stability in the coupling process are better. The aircraft response error is 63% lower than that of the black-box neural network model, which proves the advantages and engineering potential of the proposed modeling framework for small-sample flight data identification.

Key words: aerodynamic modeling, ensemble neural network, fight simulation at high angle of attack, post-stall maneuver, unsteady aerodynamics

CLC Number: