A Variable-Fidelity Modeling Method for Aero-Loads Prediction

  • Z. -H. Han
  • S. Görtz
  • R. Hain
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 112)


A Variable-Fidelity Modeling (VFM) method has been developed as an efficient and accurate aerodynamic data modeling strategy. In this approach, a set of CFD methods with varying degrees of fidelity and computational expense is exercised to reduce the number of expensive high-fidelity computations. Kriging-based bridge functions are constructed to match the low- and high fidelity CFD data. The method is demonstrated by constructing a global approximation model of the aerodynamic coefficients of an RAE 2822 airfoil based on sampled data. The model is adaptively refined by inserting additional samples. It is shown that the method is promising for efficiently generating accurate aerodynamic models that can be used for the rapid prediction of aerodynamic data across the flight envelope.


Ordinary Kriging Kriging Model Aerodynamic Coefficient Bridge Function RANS Computation 
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  1. 1.
    Braibant, V., Fleury, C.: An Approximation-Concepts Approach to Shape Optimal Design. Computer Methods in Applied Mechanics and Engineering 53, 119–148 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Haftka, R.T.: Combining global and local approximations. AIAA Journal 29(9), 1523–1525 (1991)CrossRefGoogle Scholar
  3. 3.
    Chang, K.J., Haftka, R.T., Giles, G.L., Kao, P.-J.: Sensitivity-based scaling for approximating structural response. Journal of Aircraft 30(2), 283–288 (1993)CrossRefGoogle Scholar
  4. 4.
    Alexandrov, N.M., Lewis, R.M., Gumbert, C.R., Green, L.L., Newman, P.A.: Optimization with variable-fidelity models applied to wing design. AIAA Paper 2000-0841 (January 2000)Google Scholar
  5. 5.
    Cho, S., Alonso, J.J., Kroo, I.M., Wintzer, M.: Multi-fidelity Design Optimization of Low-boom Supersonic Business Jets. AIAA Paper 2004-1530,Google Scholar
  6. 6.
    Hatanaka, H., Obayashi, S., Jeong, S.: Application of the Variable-fidelity MDO Tools to a Jet Aircraft Design. In: The 25th International Congress of the Aeronautical Science 2006 (2006)Google Scholar
  7. 7.
    Gano, S.E., Renaud, J.E., Martin, J.D., Simpson, T.W.: Update Strategies for Kriging Models for Using in Variable Fidelity Optimization. AIAA Paper 2005-2057 (April 2005)Google Scholar
  8. 8.
    Tang, C.Y., Gee, K., Lawrence, S.L.: Generation of Aerodynamic Data using a Design of Experiment and Data Fusion Approach. AIAA Paper 2005-1137 (January 2005)Google Scholar
  9. 9.
    Sacks, J., Welch, W.J., Mitchell, T.J., Wynn, H.P.: Design and Analysis of Computer Experiments. Statistical Science 4, 409–423 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Schwamborn, D., Gerhold, T., Heinrich, R.: The DLR TAU-Code: Recent Applications in Research and Industry. In: ECCOMAS CDF 2006, The Netherland (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Z. -H. Han
    • 1
  • S. Görtz
    • 1
  • R. Hain
    • 1
  1. 1.Deutsches Zentrum für Luft- und Raumfahrt e.V.Institut für Aerodynamik und StrömungstechnikBraunschweigGermany

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