Interaction Detection in Aerodynamic Design Data
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.
KeywordsKnowledge Extraction Interaction Detection Information Theory Aerodynamic Design Data Turbine Blade
Unable to display preview. Download preview PDF.
- 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.Engwirda, D.: Unstructured Mesh Methods for the Navier-Stokes Equations. Undergraduate Thesis, School of Engineering, University of Sidney (2005)Google Scholar
- 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.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
- 9.Jakulin, A.: Machine Learning Based on Attribute Interactions. Dissertation (2005)Google Scholar
- 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