Chinese Journal of Theoretical and Applied Mechanics ›› 2020, Vol. 52 ›› Issue (1): 150-161.DOI: 10.6052/0459-1879-19-287

• Dynamics, Vibration and Control • Previous Articles     Next Articles


Zhang Jiaming,Yang Zhijun,Huang Rui()   

  1. State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2019-10-17 Accepted:2020-01-08 Online:2020-01-18 Published:2020-02-23
  • Contact: Huang Rui


Reduced-order modeling for high dimensional nonlinear aeroelastic systems is one of the hot issues in the field of aeroelasticity and control. Some linear/nonlinear reduced-order modeling methodologies, such as autoregressive exogenous, auto regressive-moving-average model, Volterra series, artificial neural networks, Wiener model, and Kriging technique, were proposed for reconstructing low-dimensional aerodynamic models. However, the previous nonlinear reduced-order models, such as the nonlinear Wiener model and neural network model, still have some problems need to be addressed. For example, the identification algorithm is too complexity and the accuracy in reconstructing the dynamic behaviors needs to be improved further. In this paper, a nonlinear state-space identification-based reduced-order modeling methodology for transonic aeroelastic systems is proposed. Firstly, the unit impulse response of the transonic aerodynamic system was computed via computational fluid dynamic method. By using the snapshots of the unit impulse response, the linear dynamics part of the nonlinear state-space model is identified by using the eigensystem realization algorithm. Then, the nonlinear functions of the state variables and control input are introduced and the coefficient matrices of these nonlinear functions are optimized via the optimization algorithm. As a result, a nonlinear reduced-order aerodynamic model can be obtained. To verify the accuracy of the reduced-order modeling in predicting the transonic aeroelastic behaviors, a three-dimensional wing is selected as the testbed and the aerodynamic forces, transonic flutter computation, and limit-cycle oscillation prediction are implemented as the numerical examples. Moreover, to demonstrate the accuracy of the present reduced-order modeling method in predicting unsteady aerodynamic forces, the numerical results are also compared with other reduced-order modeling method. The numerical results show that the above three dynamic behaviors predicted via the present reduced-order model have a good agreement with the direct fluid-structure interaction method. The comparison proves that the present reduced-order aerodynamic model can be used to predict the transonic aeroelastic behaviors of aircraft with high efficiency.

Key words: aeroelasticity, flutter, reduced-order model, system identification

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