Volume 31 Issue 11
Nov.  2016
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Article Navigation >  Journal of Aerospace Power  > 2016  >  31(11): 2665-2672
MA Yang, ZHANG Qing-bin, HAN Qi-long, HUA Ming-jun, LI Hong-xia. Factors influencing the accuracy of Kriging surrogate model in two-dimensional aerodynamic problem[J]. Journal of Aerospace Power, 2016, 31(11): 2665-2672. doi: 10.13224/j.cnki.jasp.2016.11.014
Citation: MA Yang, ZHANG Qing-bin, HAN Qi-long, HUA Ming-jun, LI Hong-xia. Factors influencing the accuracy of Kriging surrogate model in two-dimensional aerodynamic problem[J]. Journal of Aerospace Power, 2016, 31(11): 2665-2672. doi: 10.13224/j.cnki.jasp.2016.11.014
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Factors influencing the accuracy of Kriging surrogate model in two-dimensional aerodynamic problem

doi: 10.13224/j.cnki.jasp.2016.11.014

  • 1. Department of Power Engineering, Rocket Force University of Engineering, Xi'an 710025, China;

  • 2. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

  • Received Date: 2015-02-02
  • Publish Date: 2016-11-28
    Abstract
  • The factors influencing the accuracy of Kriging surrogate model including the number of sample points, the parameters of model and their types, were researched. The drag property of two-dimensional transonic airfoil was used to construct the surrogate model. The computational fluid dynamics (CFD) was employed to compute the drag coefficient. Three kinds of errors,i.e. average error, maximal error and standardized cross-validated residual were employed to measure the accuracy of the Kriging surrogate model while the cross validation was applied as the accuracy validation method. The results obtained are summarized as follows. First, the Kriging surrogate model performs well when predicting the aerodynamic drag of the two-dimensional transonic airfoil. Second, the accuracy of model improves with the increase of sample number, and when the ‘bizarre airfoil’ whose responses based on Kriging surrogate model are opposite with the normal ones are deleted, the accuracy of the model is improved obviously, and the average error and maximal error decrease 5%-38% and 13%-77% respectively. Third, the model accuracy is mainly affected by type of kernal function, followed by the correlation parameter, while the regression model has little influences. The Kriging surrogate model with Gauss correlation function, second order regression model and optimal correlation parameter has the best accuracy.

     

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