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ACTA AERONAUTICAET ASTRONAUTICA SINICA ›› 2013, Vol. 34 ›› Issue (6): 1249-1260.doi: 10.7527/S1000-6893.2013.0229

• Fluid Mechanics and Flight Mechanics • Previous Articles     Next Articles

Flow Field Estimation Method Based on Proper Orthogonal Decomposition and Surrogate Model

QIU Yasong1, BAI Junqiang1, HUA Jun1,2   

  1. 1. College of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
    2. Chinese Aeronautical Establishment, Beijing 100012, China
  • Received:2012-07-02 Revised:2012-08-16 Online:2013-06-25 Published:2012-08-28
  • Contact: 10.7527/S1000-6893.2013.0229 E-mail:junqiang@nwpu.edu.cn

Abstract:

To accelerate flow field calculation, a novel estimation method is proposed in this paper. The basic principle is: first, decompose a set of sample flow fields into the same number of basis mode flow fields. Then fit each sample flow field using a few of the basis mode flow fields which contain most characteristics of all samples. Finally, use a surrogate model to build the fitting function between the fit coefficients and the input parameters which decide the sample flow fields. The tests about the steady flow fields of airfoils whose shape are different from each other indicate that: under subsonic conditions, the estimation errors of both surrogate methods would converge with no more than 20 basis modes. Using more basis modes would not improve the estimation precision obviously. Under transonic conditions, estimation errors of both surrogate methods would converge when the number of basis modes is 26. Based on the first 10 modes, introducing more basis modes can improve the estimation precision in most areas of the flow field. But it would bring "noise" shock wave features to the area around the shock wave and decrease the predictive precision at that area. Under both conditions, the computational efficiency of this new estimation method is two hundred times better than the high precision computational fluid dynamics (CFD) method.

Key words: flow field, estimation, proper orthogonal decomposition, surrogate model, noise shock wave feature, computational fluid dynamics

CLC Number: