Multi-objective design exploration and its application to regional-jet wing design

Transactions of the Japan Society for Aeronautical and Space Sciences

Volumn 50, Issue 167, 2007, Pages 1-8

  Obayashi, Shigeru ^a   Jeong, Shinkyu ^a   Chiba, Kazuhisa ^b   Morino, Hiroyuki ^c  

^a TOHOKU UNIVERSITY   (Japan)

^b JAPAN AEROSPACE EXPLORATION AGENCY   (Japan)

^c MITSUBISHI HEAVY INDUSTRIES LTD   (Japan)

Author keywords

Aircraft; Design; Evolutionary computation; Multidisciplinary design Optimization; Self organizing map; Wings and airfoil sections

Indexed keywords

ADAPTIVE RANGE MULTI-OBJECTIVE GENETIC ALGORITHMS; COMPUTATIONAL FLUID; DECISION MAKERS; DESIGN EXPLORATION; DESIGN KNOWLEDGE; DESIGN SPACES; EVOLUTIONARY COMPUTATIONS; HIGH-FIDELITY SIMULATIONS; KRIGING MODEL; MULTI-OBJECTIVE DESIGN; MULTIDISCIPLINARY DESIGN OPTIMIZATION; NASTRAN; NAVIER-STOKES SOLVER; OPTIMIZATION SYSTEM; SELF ORGANIZING; TRADE-OFF INFORMATION; VISUAL DATA MINING; WING DESIGN;

AIRFOILS; COMBINATORIAL OPTIMIZATION; COMPUTATIONAL FLUID DYNAMICS; CONFORMAL MAPPING; DATA MINING; DECISION MAKING; GENETIC ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; STRUCTURAL DYNAMICS;

DESIGN;

EID: 80052432266     PISSN: 05493811     EISSN: None     Source Type: Journal    
DOI: 10.2322/tjsass.50.1     Document Type: Article

References (24)

1. Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R. and Tucker, P. K.: Surrogate-Based Analysis and Optimization, Prog. Aerospace Sci., 41 (2005), pp. 1-28. (Pubitemid 40537187)
2. Sasaki, D. and Obayashi, S.: Efficient Search for Trade-Offs by Adaptive Range Multi-objective Genetic Algorithms, J. Aerospace Comput. Inform., Commun., 2 (2005), pp. 44-64. (Pubitemid 40648810)
3. Vanderplaats, G. N.: Numerical Optimization Techniques for Engineering Design, McGraw-Hill Book Company, New York, 1984.
4. Das, I. and Dennis, J. E.: Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems, SIAM J. Optim., 8 (1998), pp. 631-657.
5. Nakayama, H.: Aspiration Level Approach to Interactive Multi- Objective Programming and Its Applications, Paldalos, P. M., Siskos, Y. and Zopounidis, C. (eds.), Advances in Multicriteria Analysis, Kluwer Academic Publishers, Dordrecht, 1995, pp. 147-174.
6. Coello Coello, A. C. and Lamont, G. B.: Applications of Multi- Objective Evolutionary Algorithms, World Scientific Publishing Co. Pte. Ltd., Singapore, 2004.
7. Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons, Ltd., New York, 2001.
8. Fleming, P. J., Purshouse, R. C. and Lygoe, R. J.: Many-Objective Optimization: An Engineering Design Perspective, Evolutionary Multi-Criterion Optimization (EMO2005), Springer, Heidelberg, 2005, pp. 14-32. (Pubitemid 41251753)
9. Fonseca, C. M. and Fleming, P. J.: Genetic Algorithms for Multi- Objective Optimization: Formulation, Discussion and Generalization, Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1993, pp. 416-423.
10. Chiba, K., Obayashi, S., Nakahashi, K. and Morino, H.: High-Fidelity Multidisciplinary Design Optimization of Aerostructural Wing Shape for Regional Jet, AIAA Paper 2005-5080, June 2005.
11. Giannakoglou, K. C.: Design of Optimal Aerodynamic Shapes Using Stochastic Optimization Methods and Computational Intelligence, Prog. Aerospace Sci., 38 (2002), pp. 43-76. (Pubitemid 34201146)
12. Oyama, A., Obayashi, S. and Nakamura, T.: Real-Coded Aptive Range Genetic Algorithm Applied to Transonic Wing Optimization, Appl. Soft Comput., 1 (2001), pp. 179-187. (Pubitemid 33718575)
13. Parussini, L., Pediroda, V. and Obayashi, S.: Design under Uncertainties of Wings in Transonic Field, JSME Int. J., Ser. B, 48 (2005), pp. 218-228.
14. Parmee, I. C. and Abraham, J. A. R.: Supporting Implicit Learning via the Visualisation of COGA Multi-objective Data, Proceedings of IEEE Congress on Evolutionary Computation, 2004, pp. 395-402.
15. Obayashi, S. and Sasaki, D.: Visualization and Data Mining of Pareto Solutions Using Self-Organizing Map, Evolutionary Multi-Criterion Optimization (EMO2003), LNCS 2632, Springer, Heidelberg, 2003, pp. 796-809. (Pubitemid 137634682)
16. Jeong, S., Chiba, K. and Obayashi, S.: Data Mining for Aerodynamic Design Space, AIAA Paper 2005-5079, June 2005.
17. Giannakoglou, K. C., Giotis, A. P. and Karakasis, M. K.: Low-Cost Genetic Optimization Based on Inexact Pre-Evaluations and the Sensitivity Analysis of Design Parameters, J. Inverse Prob. Eng., 9 (2001), pp. 389-412. (Pubitemid 33679530)
18. Keane, A. J.: Wing Optimization Using Design of Experiment, Response Surface, and Data Fusion Methods, J. Aircraft, 40 (2003), pp. 741-750.
19. Willson, B., Cappelleri, D., Simpson, T. W. and Frecker, M.: Efficient Pareto Frontier Exploration Using Surrogate Approximations, Optim. Eng., 2 (2001), pp. 31-50.
20. Kohonen, T.: Self-Organizing Maps, Springer, Berlin, Heidelberg, 1995.
21. Krzysztof, J. C., Witold, P. and Roman, W. S.: Data Mining Methods for Knowledge Discovery, Kluwer Academic Publisher, Boston, 1998.
22. Eudaptics software gmbh, http://www.eudaptics.com/somine/, last access on April 14, 2005.
23. Donald, R. J., Matthias, S. and William, J. W.: Efficient Global Optimization of Expensive Black-Box Function, J. Global Optim., 13 (1998), pp. 455-492.
24. Jeong, S., Murayama, M. and Yamamoto, K.: Efficient Optimization Design Method Using Kriging Model, J. Aircraft, 42 (2005), pp. 413-420. (Pubitemid 40611738)