Interaction Detection in Aerodynamic Design Data

  • Lars Graening
  • Markus Olhofer
  • Bernhard Sendhoff
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5788)


In large and complex aerodynamic systems the overall performance of a design is mainly defined by interactions between design areas rather than by single design regions. Therefore it is necessary to identify these interactions in order to be able to understand and improve the designs. However, detecting and modeling those interactive effects between distant design areas is a very challenging task which usually requires a detailed understanding of the flow patterns and dedicated expert knowledge.

In this paper we apply the information theoretic concept of interaction information to aerodynamic design data in order to detect and quantify interaction effects between distant design regions. Information graphs are suggested in order to provide the results to the aerodynamic engineer in a graphical form. In order to show the feasibility of this approach, the information theoretic quantities are applied to the data of a 2D wing assembly as well as to the 3D turbine blade design data.


Knowledge Extraction Interaction Detection Information Theory Aerodynamic Design Data Turbine Blade 


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  1. 1.
    Arima, T., Sonoda, T., Shirotori, M., Tamura, A., Kikuchi, K.: A numerical investigation of transonic axial compressor rotor flow using a low-reynolds-number k − ε turbulence model. ASME Journal of Turbomachinery 121(1), 44–58 (1999)CrossRefGoogle Scholar
  2. 2.
    Daub, C.O., Steuer, R., Selbig, J., Kloska, S.: Estimating mutual information using b-spline functions - an improved similarity measure for analysing gene expression data. BMC Bioinformatics 5(118) (August 2004)Google Scholar
  3. 3.
    Engwirda, D.: Unstructured Mesh Methods for the Navier-Stokes Equations. Undergraduate Thesis, School of Engineering, University of Sidney (2005)Google Scholar
  4. 4.
    Graening, L., Menzel, S., Hasenjäger, M., Bihrer, T., Olhofer, M., Sendhoff, B.: Knowledge extraction from aerodynamic design data and its application to 3d turbine blade geometries. Mathematical Modelling and Algorithms 7, 329–350 (2008)CrossRefGoogle Scholar
  5. 5.
    Graening, L., Olhofer, M., Sendhoff, B.: Knowledge extraction from unstructured surface meshes. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 497–506. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Hasenjäger, M., Sendhoff, B., Sonoda, T., Arima, T.: Three dimensional aerodynamic optimization for an ultra-low aspect ratio transonic turbine stator blade. In: Proceedings of the ASME Turbo Expo. ASME Paper No. GT2005-68680 (2005)Google Scholar
  7. 7.
    Hasenjäger, M., Sendhoff, B., Sonoda, T., Arima, T.: Three dimensional evolutionary aerodynamic design optimisation using single and multi-objective approaches. In: Evolut. and Deterministic Methods for Design, Opt. and Control with Applications to Industrial and Societal Problems. EUROGEN (2005)Google Scholar
  8. 8.
    Jaccard, J., Turrisi, R.: Interaction Effects in Multiple Regression, 2nd edn. Sage Publications, Thousand Oaks (2003)CrossRefGoogle Scholar
  9. 9.
    Jakulin, A.: Machine Learning Based on Attribute Interactions. Dissertation (2005)Google Scholar
  10. 10.
    Jeong, S., Chiba, K., Obayashi, S.: Data mining for aerodynamic design space. Aerospace Computing, Information and Communication 2(11), 452–469 (2005)CrossRefGoogle Scholar
  11. 11.
    McGill, W.J.: Multivariate information transmission. Psychometrika 19(2), 97–116 (1954)CrossRefzbMATHGoogle Scholar
  12. 12.
    Olhofer, M., Jin, Y., Sendhoff, B.: Adaptive encoding for aerodynamic shape optimization using evolutionary strategies. In: Congress on Evolutionary Computation (CEC), Seoul, Korea, May 2001, pp. 576–583. IEEE Press, Los Alamitos (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lars Graening
    • 1
  • Markus Olhofer
    • 1
  • Bernhard Sendhoff
    • 1
  1. 1.Honda Research Institute Europe GmbHOffenbach/MainGermany

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