JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Design Exploration of High-Lift Airfoil Using Kriging Model and Data Mining Technique
Kanazaki, Masahiro; Yamamoto, Kazuomi; Tanaka, Kentaro; Jeong, Shin-Kyu;
  PDF(new window)
 Abstract
A multi-objective design exploration for a three-element airfoil consisted of a slat, a main wing, and a flap was carried out. The lift curve improvement is important to design high-lift system, thus design has to be performed with considered multi-angle. The objective functions considered here are to maximize the lift coefficient at landing and near stall conditions simultaneously. Kriging surrogate model which was constructed based on several sample designs is introduced. The solution space was explored based on the maximization of Expected Improvement (EI) value corresponding to objective functions on the Krigingmodels. The improvement of the model and the exploration of the optimum can be advanced at the same time by maximizing EI value. In this study, a total of 90 sample points are evaluated using the Reynolds averaged Navier-Stokes simulation(RANS) for the construction of the Kriging model. In order to obtain the information of the design space, two data mining techniques are applied to design result. One is functional Analysis of Variance(ANOVA) which can show quantitative information and the other is Self-Organizing Map(SOM) which can show qualitative information.
 Keywords
High-lift Airfoil ; Design Exploration ; Data Mining ; Kriging Model
 Language
English
 Cited by
1.
Efficient global optimization applied to wind tunnel evaluation-based optimization for improvement of flow control by plasma actuators, Engineering Optimization, 2015, 47, 9, 1226  crossref(new windwow)
 References
1.
C. P. van Dam, 'The aerodynamic design of multi-element high-lift systems for transport airplanes', Progress in Aerospace Science, Vol. 38, pp. 101-144, (2002) crossref(new window)

2.
A. M. O. Smith, 'High-Lift Aerodynamics', Journal of Aircraft, Vol. 12, No. 6, pp. 501-530, (1975) crossref(new window)

3.
S. Jeong, N. Murayama, and K. Yamamoto,'Efficient Optimization Design Method Using Kriging Model', Journal of Aircraft, Vol. 42, pp.413-420, (2005) crossref(new window)

4.
R. J.Donald, S. Matthias, and J.W. William ,'Efficient Global O]:!timization of Expensive Black-Box Function', Journal of global optimization, Vol. 13, pp. 455-192 (1998) crossref(new window)

5.
K Chiba, S. Obayashi, and K Nakahashi, 'Trade-off Analysis of Aerodynamic Wing Design for RLV', Proceedings of International Conference Parallel Computational Fluid Dynamics, to be appeared (2004)

6.
S. Jeong, and S. Obayashi, 'Efficient Global Optimization (EGO) for Multi-Objeclive Problem and Data Mining', Prooceedings of Congress on Evolutionary Computation 2005, Vol. 3, pp. 2138-2145, (2005)

7.
T. Kumano, S. Jeong, S. Obayashi, Y. Ito, K Hatanaka, and H, Morino, 'Multidiciplinary Design Optimization of Wing Shape for a Small Jet Aicraft Using Kriging filodel', AIAA 2006-932, (2006)

8.
M. Kanazaki, K Tanaka, S. Jeong, and K. Yamamoto, 'Multi Clbjective Aerodynamic Optimization of Elements' Setting for High--lift Airfoil Using Kriging Model, AIAA 2006-1471, (2006)

9.
R. Takaki, K Yamamoto, T. Yamane, S. Enomoto, and J. Mukai, The Development of the UPACS CFD Environment', High Performance Computing, Proceedings of ISHPC 2003, Springer, pp. 307-319, (2003)

10.
Crumpton, P. I. and Giles, M. B.,'Implicit time accurate solutions or unstructured dynamic grids', AIAA Paper 95-1671-CP, pp. 284-294, (1995)

11.
Mckay, M. D., Beckrnan, R. J. and Conover, W. J., 'A Comparisor of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code', Technometric, Vol. 21, No. 2, pp. 239-245, (1979) crossref(new window)

12.
Sack, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P., 'Design and Analysis of Computer Experiments (with Discussion)', Statistical Science, Vol. 4, pp. 409-435, (1989) crossref(new window)

13.
J. C. Krzysztof, P. Witold, and W. S. Roman, Data Mining Methods for Knowledge Discovery, Kluwer Academic Publisher, (1998)

14.
Eudaptics software gmbh, http://www.eudaptics.com/somine