Parametric reduced-order model of unsteady aerodynamics based on incremental learning algorithm

CHEN Zhiqiang; LIU Zhanhe; MIAO Nan; FENG Wei

doi:10.7527/S1000-6893.2021.25103

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2021 , Vol. 42 >Issue 7: 125103 - 125103

DOI: https://doi.org/10.7527/S1000-6893.2021.25103

Fluid Mechanics and Flight Mechanics

Parametric reduced-order model of unsteady aerodynamics based on incremental learning algorithm

CHEN Zhiqiang ,
LIU Zhanhe ,
MIAO Nan ,
FENG Wei

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1. School of Aeronautical Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China;
2. School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450046, China

Received date: 2020-12-14

Revised date: 2021-03-23

Online published: 2021-04-27

Supported by

National Natural Science Foundation of China (11702255); The Science and Technology Research Project of Henan Province(212102210052, 212102210334,202102210267, 202102210509, 202102210288)

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Abstract

The aerodynamic Reduced-Order Model (ROM) is a useful tool in the prediction of nonlinear unsteady aerodynamics with reasonable accuracy and low computational cost. The efficacy of this method has been validated by recent studies. However, the robustness of ROMs with respect to flight parameter variations should be further improved. To enhance the prediction capability of ROMs for varying flight parameters, this paper presents two parametric reduced-order models based on the Least Squares Support Vector Regression (LS-SVR) and the incremental learning algorithm. LS-SVR is a class of regression methods with good generalization ability, and the main contribution of the incremental learning algorithm based on LS-SVR is that it is not necessary to relearn the whole data set while the new sample sets are incremented. To illustrate the approach, we construct the parametric unsteady aerodynamic ROMs of the NACA64A010 airfoil model with two degrees of freedom. The Mach number and angle of attack are considered as the additional model inputs to train the relationship between aerodynamic inputs and the corresponding outputs. It is demonstrated that the model can accurately capture the dynamic characteristics of aerodynamic and aeroelastic systems for varying flight parameters.

Key words： unsteady aerodynamics; parametric reduced-order models; least squares support vector regression; incremental learning algorithms; flutter

Cite this article

CHEN Zhiqiang , LIU Zhanhe , MIAO Nan , FENG Wei . Parametric reduced-order model of unsteady aerodynamics based on incremental learning algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021 , 42(7) : 125103 -125103 . DOI: 10.7527/S1000-6893.2021.25103

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