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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2021, Vol. 42 ›› Issue (4): 524093-524093.doi: 10.7527/S1000-6893.2020.24093

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Missile aerodynamic performance prediction of Gaussian process through automatic kernel construction

HU Weijie1,2, HUANG Zenghui3, LIU Xuejun1,2, LYU Hongqiang3   

  1. 1. MⅡT Key Laboratory of Pattern Analysis and Machine Intelligence, College of Computer Science and Technology/College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
    2. Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China;
    3. College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2020-04-15 Revised:2020-07-07 Published:2020-07-17
  • Supported by:
    Aeronautical Science Foundation of China (2018ZA52002,2019ZA052011); Foundation of State Key Laboratory of Aerodynamics (SKLA20180102); Key Laboratory of Aerodynamic Noise Control (ANCL20190103)

Abstract: The initial stage of missile design usually requires quick and rough evaluation of missile aerodynamic performance. To improve the low calculation accuracy of traditional engineering estimation software and reduce the high calculation cost of CFD method, we propose a scheme based on the Gaussian Process Regression (GPR) surrogate model to quickly and accurately predict the aerodynamic performance of typical missiles. The prediction results of the GPR model are analyzed, taking the missile shape parameters and angle of attack as the input and the lift coefficient, drag coefficient and moment coefficient as the output. First of all, compared with the prediction accuracy of other commonly used surrogate models, the prediction errors of the GPR model for the three coefficients are only 0.24%, 0.36% and 0.36%, respectively, lower than those of other surrogate models. Secondly, considering the problem that the kernel function selection of the GPR model depends heavily on artificial prior knowledge, we embed an automatic kernel construction algorithm which can automatically learn the kernel function from data without prior knowledge into the GPR framework. Compared with the traditional GPR model, the prediction errors of the GPR model incorporating the algorithm for the three coefficients are reduced to 0.10%, 0.22% and 0.17%, respectively. Finally, an application example of rapid prediction of missile aerodynamic performance is conducted, and the results show that the prediction scheme of the missile aerodynamic performance based on the GPR model is effective and can meet the requirements of fast and accurate aerodynamic performance prediction in the initial stage of missile design.

Key words: missile aerodynamic performance, prediction, surrogate models, Gaussian process regression, automatic kernel construction, kernel function

CLC Number: