Data Mining for Aerodynamic Design Space

Analysis of variance (ANOVA) and self-organizing map (SOM) were applied to data mining for aerodynamic design space. These methods make it possible to identify the effect of each design variable on objective functions. ANOVA shows the information quantitatively, while SOM shows it qualitatively. Furthermore, ANOVA can show the effects of interaction between design variables on objective functions and SOM can visualize the trade-offs among objective functions. This information will be helpful for designers to determine the final design from non-dominated solutions of multi-objective problems. These methods were applied to two design results: a fly-back booster in reusable launch vehicle design, which has 4 objective functions and 71 design variables, and a transonic airfoil design performed with the adaptive search region method.

[1]  Kazuomi Yamamoto,et al.  Efficient Optimization Design Method Using Kriging Model , 2005 .

[2]  Gary B. Lamont,et al.  Applications Of Multi-Objective Evolutionary Algorithms , 2004 .

[3]  Gary B. Lamont,et al.  AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS , 2004 .

[4]  Shigeru Obayashi,et al.  Kriging-Based Probabilistic Method for Constrained Multi- Objective Optimization Problem , 2004 .

[5]  Kenjiro Kamijo,et al.  Conceptual Study of Rocket Powered TSTO with Fly-back Booster , 2003 .

[6]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[7]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[8]  Witold Pedrycz,et al.  Data Mining Methods for Knowledge Discovery , 1998, IEEE Trans. Neural Networks.

[9]  K. Mardia,et al.  Maximum likelihood estimation of models for residual covariance in spatial regression , 1984 .

[10]  B. Efron,et al.  The Jackknife Estimate of Variance , 1981 .

[11]  Daisuke Sasaki,et al.  Efficient Search for Trade-Offs by Adaptive Range Multi-Objective Genetic Algorithms , 2005, J. Aerosp. Comput. Inf. Commun..

[12]  S. Obayashi,et al.  Aerodynamic Optimization of Supersonic Transport Wing Using Unstructured Adjoint Method , 2001 .

[13]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[14]  H. Sobieczky.  Parametric Airfoils and Wings , 1999 .

[15]  Kazuhiro Nakahashi,et al.  Euler/Navier-Stokes Optimization of Supersonic Wing Design Based on Evolutionary Algorithm , 1999 .

[16]  Kazuhiro Nakahashi,et al.  Tradeoff Analysis of Aerodynamic Wing Design for RLV , 1996 .

[17]  A. Jameson,et al.  Aerodynamic shape optimization of wing and wing-body configurations using control theory , 1995 .

[18]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .