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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2014, Vol. 35 ›› Issue (11): 2949-2957.doi: 10.7527/S1000-6893.2014.0086

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Identification Method of Second-order Kernels in Aerodynamics

WANG Yunhai1, HAN Jinglong1, ZHANG Bing2, Yuan Haiwei1   

  1. 1. State Key Lab of Mechanics and Control for Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. School of Mechanical and Automotive Engineering, Hefei University of Technology, Hefei 230009, China
  • Received:2014-02-17 Revised:2014-05-05 Online:2014-11-25 Published:2014-05-12
  • Supported by:

    National Natural Science Foundation of China (11102085); Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Abstract:

Based on the truncated third-order Volterra series model, this paper investigates the identification of second-order Volterra kernels in aerodynamics. In this model, the second-order Volterra kernels can be parameter treated by employing a cluster of Chebyshev orthogonal polynomials. And then the problem of non-parameter is converted to the problem of parameter. In comparison with other treatments, it has several advantages. First, it ensures that the second-order Volterra kernels can be identified with better fidelity due to the reduced signal amplitude sensitivity. Then, in this model, the accuracy of first- and second-order Volterra kernels can be improved and the third-order Volterra kernels do not need to be identified. That means the cost for identification is reasonable. Last, the proposed parametric methods treat the identification as a whole and the partial data is available to be used for identification. That means the total number of executing computational fluid dynamics (CFD) code can be reduced and the identification cost is highly time-saving. The numerical results show the efficiency and high accuracy compared to those non-parameter methods and the total amount of CFD time can be reduced by at least one order of magnitude.

Key words: system identification, Volterra series, reduced-order model, Chebyshev expansion method, parametric approach

CLC Number: