JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Multi-Objective Design Exploration and its Applications
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Multi-Objective Design Exploration and its Applications
Obayashi, Shigeru; Jeong, Shin-Kyu; Shimoyama, Koji; Chiba, Kazuhisa; Morino, Hiroyuki;
  PDF(new window)
 Abstract
Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.
 Keywords
Multidisciplinary design optimization;Evolutionary computation;Multiobjective optimization;Data mining;Self-organizaing map;Response surface method;
 Language
English
 Cited by
1.
Numerical investigation for erratic behavior of Kriging surrogate model, Journal of Mechanical Science and Technology, 2014, 28, 9, 3697  crossref(new windwow)
2.
Analysis and Improvements of the Pareto Optimal Solution Visualization Method Using the Self-Organizing Maps, SICE Journal of Control, Measurement, and System Integration, 2015, 8, 1, 34  crossref(new windwow)
 References
1.
Abdelwahab, A. and Gerber, G. (2008). A new threedimensional aerofoil diffuser for centrifugal compressors. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 222, 819-830. crossref(new window)

2.
Agrawal, G., Lewis, K., Chugh, K., Huang, C. H., Parashar, S., and Bloebaum, C. L. (2004). Intuitive visualization of Pareto Frontier for multi-objective optimization in n-dimensional performance space. 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY. pp. 1523-1533.

3.
Agrawal, G., Parashar, S., and Bloebaum, C. L. (2006). Intuitive visualization of hyperspace pareto frontier for robustness in multi-attribute decision-making. 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, VA. pp. 729-742.

4.
Alpern, B. and Carter, L. (1991). The Hypebox. IEEE Visualization Conference, San Jose, CA. pp. 133-139.

5.
Chernoff, H. (1973). The use of faces to represent points in K-dimensional space graphically. Journal of the American Statistical Association, 68, 361-368. crossref(new window)

6.
Chiba, K., Makino, Y., and Takatoya, T. (2008). Evolutionary-based multidisciplinary design exploration for the silent supersonic technology demonstrator wing. Journal of Aircraft, 45, 1481-1494. crossref(new window)

7.
Chiba, K., Makino, Y., and Takatoya, T. (2009). Designinformatics approach for intimate configuration of silent supersonic technology demonstrator. 47th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, Orlando, FL. pp. AIAA 2009-0968.

8.
Chiba, K. and Obayashi, S. (2008). Knowledge discovery for flyback-booster aerodynamic wing design using data mining. Journal of Spacecraft and Rockets, 45, 975-987. crossref(new window)

9.
Chiba, K., Oyama, A., Obayashi, S., Nakahashi, K., and Morino, H. (2007). Multidisciplinary design optimization and data mining for transonic regional-jet wing. Journal of Aircraft, 44, 1100-1112. crossref(new window)

10.
Cios, K. J., Pedrycz, W., and Swiniarski, R. (1998). Data Mining Methods for Knowledge Discovery. Boston: Kluwer Academic Publishers.

11.
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. New York: John Wiley & Sons.

12.
Eddy, J. and Lewis, K. E. (2002). Visualization of multidisciplinary design and optimization data using cloud visualization. Proceedings of Design Engineering Technical Conferences, Montreal, Quebec. pp. 899-908.

13.
Graening, L., Menzel, S., Hasenjager, M., Bihrer, T., Olhofer, M., and Sendhoff, B. (2008). Knowledge extraction from aerodynamic design data and its application to 3D turbine blade geometries. Journal of Mathematical Modelling and Algorithms, 7, 329-350. crossref(new window)

14.
Hatanaka, K., Obayashi, S., and Jeong, S. (2006). Application of the variable-fidelity MDO tools to a jet aircraft design. 25th International Congress of the Aeronautical Sciences, Hamburg, Germany.

15.
Holden, C. M. E. and Keane, A. J. (2004). Visualization methodologies in aircraft design. 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Albany, NY. pp. 1685-1697.

16.
Ikui, T. (1988). Turbo-Blowers and Compressors. Tokyo, Japan: Corona Publishing Co., Ltd. (in Japanese).

17.
Inselberg, A. (1997). Parallel coordinates for visualizing multidimensional geometry. In Statistical Office of the European Communities, ed. New Techniques and Technologies for Statistics II: Proceedings of the Second Bonn Seminar. Washington, DC: IOS Press. pp. 279-288.

18.
Inselberg, A. and Dimsdale, B. (1990). Parallel coordinates: A tool for visualizing multi-dimensional geometry. Proceedings of the First 1990 IEEE Conference on Visualization, San Francisco, CA. pp. 361-378.

19.
Ito, Y. and Nakahashi, K. (2002). Direct surface triangulation using stereolithography data. AIAA Journal, 40, 490-496. crossref(new window)

20.
Jeong, M. J., Kobayashi, T., and Yoshimura, S. (2007). Multidimensional visualization and clustering for multiobjective optimization of artificial satellite heat pipe design. Journal of Mechanical Science and Technology, 21, 1964-1972. crossref(new window)

21.
Jeong, S., Chiba, K., and Obayashi, S. (2005a). Data mining for aerodynamic design space. Journal of Aerospace Computing, Information and Communication, 2, 452-469. crossref(new window)

22.
Jeong, S., Murayama, M., and Yamamoto, K. (2005b). Efficient optimization design method using kriging model. Journal of Aircraft, 42, 413-420. crossref(new window)

23.
Jeong, S. and Obayashi, S. (2005). Efficient Global Optimization (EGO) for multi-objective problem and data mining. IEEE Congress on Evolutionary Computation, Edinburgh, Scotland. pp. 2138-2145.

24.
Jones, D. R., Schonlau, M., and Welch, W. J. (1998). Efficient Global Optimization of Expensive Black-Box Functions. Journal of Global Optimization, 13, 455-492. crossref(new window)

25.
Keane, A. J. (2003). Wing optimization using design of experiment, response surface, and data fusion methods. Journal of Aircraft, 40, 741-750. crossref(new window)

26.
Kim, H. W., Park, J. I., Ryu, S. H., Choi, S. W., and Ghal, S. H. (2009). The performance evaluation with diffuser geometry variations of the centrifugal compressor in a marine engine (70 MW) turbocharger. Journal of Engineering for Gas Turbines and Power, 131, 012201-1-7. crossref(new window)

27.
Kitadume, M., Kawahashi, M., Hirahara, H., Uchida, T., and Yanagawa, H. (2007). Experimental analysis of 3D flow in scroll casing of multi-blade fan for air-conditioner. Journal of Fluid Science and Technology, 2, 302-310. crossref(new window)

28.
Kodiyalam, S., Yang, R. J., and Gu, L. (2004). Highperformance computing and surrogate modeling for rapid visualization with multidisciplinary optimization. AIAA Journal, 42, 2347-2354. crossref(new window)

29.
Kohonen, T. (1995). Self-Organizing Maps. Berlin: Springer.

30.
Krain, H. (1981). A study on centrifugal impeller and diffuser flow. Journal of Engineering for Power, Transactions of the ASME, 103, 688-697.

31.
Kumano, T., Jeong, S., Obayashi, S., Ito, Y., Hatanaka, K., and Morino, H. (2006a). Multidisciplinary design optimization of wing shape for a small jet aircraft using kriging model. 44th AIAA Aerospace Sciences Meeting, Reno, NV. pp. 11158-11170.

32.
Kumano, T., Jeong, S., Obayashi, S., Ito, Y., Hatanaka, K., and Morino, H. (2006b). Multidisciplinary design optimization of wing shape with nacelle and pylon. European Conference on Computational Fluid Dynamics (ECCOMAS CFD 2006), Egmond aan Zee, The Netherlands.

33.
Kumano, T., Morino, H., Jeong, S., and S., O. (2008). Aeroelastic analysis using unstructured CFD method for realistic aircraft design. 8th World Congress on Computational Mechanics / 5th European Congress on Computational Methods in Applied Sciences and Engineering, Venice, Italy.

34.
Ligetti, C. B. and Simpson, T. W. (2005). Metamodel-driven design optimization using integrative graphical design interfaces: Results from a job-shop manufacturing simulation experiment. Journal of Computing and Information Science in Engineering, 5, 8-17. crossref(new window)

35.
Ligetti, C., Simpson, T. W., Frecker, M., Barton, R. R., and Stump, G. (2003). Assessing the impact of graphical design interfaces on design efficiency and effectiveness. Journal of Computing and Information Science in Engineering, 3, 144-154. crossref(new window)

36.
Makino, Y. and Naka, Y. (2007). Sonic-boom research and low-boom demonstrator project in JAXA. Proceedings on 19th International Congress on Acoustics, Madrid, Spain.

37.
Mattson, C. A. and Messac, A. (2003). Concept selection using s-pareto frontiers. AIAA Journal, 41, 1190-1198. crossref(new window)

38.
McKay, M. D., Beckman, R. J., and Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 239-245. crossref(new window)

39.
Messac, A. and Chen, X. (2000). Visualizing the optimization process in real-time using physical programming. Engineering Optimization, 32, 721-747. crossref(new window)

40.
Morino, H., Yamaguchi, H., Kumano, T., Jeong, S., and Obayashi, S. (2009). Efficient aeroelastic analysis using unstructured CFD method and reduced-order unsteady aerodynamic model. 50th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Palm Springs, CA. pp. AIAA 2009-2326.

41.
Murakami, A. (2006). Silent supersonic technology demonstration program. Proceedings on 25th International Council of the Aeronautical Sciences, Hamburg, Germany.

42.
Obayashi, S., Jeong, S., and Chiba, K. (2005). Multiobjective design exploration for aerodynamic configurations. 35th AIAA Fluid Dynamics Conference and Exhibit. pp. AIAA-2005-4666.

43.
Obayashi, S., Jeong, S., Chiba, K., and Morino, H. (2007). Multi-objective design exploration and its application to regional-jet wing design. Transactions of The Japan Society for Aeronautical and Space Sciences, 50, 1-8. crossref(new window)

44.
Obayashi, S. and Sasaki, D. (2003). Visualization and data mining of Pareto solutions using self-organizing map. 2nd International Conference on Evolutionary Multi-Criterion Optimization, Faro, Portugal. pp. 796-809.

45.
Ohnuki, T., Hirako, K., and Sakata, K. (2006). National experimental supersonic transport project. Proceedings on 25th International Council of the Aeronautical Sciences, Hamburg, Germany.

46.
Ohrn, A. (2000). ROSETTA Technical Reference Manual. Trondheim, Norway: Department of Computer and Information Science, Norwegian University of Science and Technology.

47.
Oyama, A., Verburg, P. C., Nonomura, T., Hoeijmakers, H. W. M., and Fujii, K. (2010). Flow field data mining of Paretooptimal airfoils using proper orthogonal decomposition. 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, FL. pp. AIAA 2010-1140.

48.
Parashar, S., Pediroda, V., and Poloni, C. (2008). Self organizing maps (SOM) for design selection in robust multiobjective design of aerofoil. 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV.

49.
Parker, S. G., Weinstein, D. M., and Johnson, C. R. (1997). The SCIRun computational steering software system. In E. Arge, A. M. Bruaset, and H. P. Langtangen, eds. Modern Software Tools for Scientific Computing. Boston: Birkhauser. p. 380 p.

50.
Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11, 341-356. crossref(new window)

51.
Paxson, D. E. and Skoch, G. J. (1998). Wave augmented diffusers for centrifugal compressors. 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit, Reston, VA. pp. AIAA 98-3401.

52.
Pohlheim, H. (1999). Visualization of evolutionary algorithms: set of standard techniques and multidimensional visualization. Genetic and Evolutionary Computation Conference, San Francisco, CA. pp. 533-540.

53.
Pryke, A., Mostaghim, S., and Nazemi, A. (2007). Heatmap visualization of population based multi objective algorithms. 4th International Conference on Evolutionary Multi-Criterion Optimization, Matsushima, Japan. pp. 361-375.

54.
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and Kevin Tucker, P. (2005). Surrogate-based analysis and optimization. Progress in Aerospace Sciences, 41, 1-28. crossref(new window)

55.
Shimoyama, K., Sugimura, K., Jeong, S., and Obayashi, S. (2010). Performance map construction for a centrifugal diffuser with data mining techniques. Journal of Computational Science and Technology, 4, 36-50. crossref(new window)

56.
Simon, H., Wallmann, T., and Moenk, T. (1987). Improvements in performance characteristics of single-stage and multistage centrifugal compressors by simultaneous adjustments of inlet guide vanes and diffuser vanes. Journal of Turbomachinery, 109, 41-47. crossref(new window)

57.
Simpson, T. W., Carlsen, D. E., Congdon, C. D., Stump, G., and Yukish, M. A. (2008). Trade space exploration of a wing design problem using visual steering and multi-dimensional data visualization. 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Schaumburg, IL. pp. AIAA 2008-2139.

58.
Simpson, T. W., Iyer, P. S., Rothrock, L., Frecker, M., Barton, R. R., Barron, K. A., and Meckesheimer, M. (2005). Metamodel-driven interfaces for engineering design: Impact of delay and problem size on user performance. 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Austin, TX. pp. 3198-3208.

59.
Sobol, I. M. (1993). Sensitivity estimates for nonlinear mathematical models. Mathematical Modeling and Computational Experiments, 1, 407-414.

60.
Stump, G., Lego, S., Yukish, M., Simpson, T. W., and Donndelinger, J. A. (2009). Visual steering commands for trade space exploration: User-guided sampling with example. Journal of Computing and Information Science in Engineering, 9, 1-10.

61.
Stump, G., Simpson, T. W., Yukish, M., and Bennett, L. (2002). Multidimensional visualization and its application to a design by shopping paradigm. 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, Atlanta, GA. pp. AIAA 2002-5622.

62.
Stump, G. M., Simpson, T. W., Yukish, M., and O’Hara, J. J. (2004). Trade space exploration of satellite datasets using a design by shopping paradigm. IEEE Aerospace Conference Proceedings, Big Sky, MT. pp. 3885-3894.

63.
Sugimura, K., Jeong, S., Obayashi, S., and Kimura, T. (2009a). Kriging-model-based multi-objective robust optimization and trade-off rule mining of a centrifugal fan with dimensional uncertainty. Journal of Computational Science and Technology, 3, 196-211. crossref(new window)

64.
Sugimura, K., Obayashi, S., and Jeong, S. (2009b). A new design method based on cooperative data mining from multi-objective design space. Journal of Computational Science and Technology, 3, 287-302. crossref(new window)

65.
Sugimura, K., Obayashi, S., and Jeong, S. (2010). Multiobjective optimization and design rule mining for an aerodynamically efficient and stable centrifugal impeller with a vaned diffuser. Engineering Optimization, 42, 271-293. crossref(new window)

66.
Svensen, M. (1998). GTM: The Generative Topographic Mapping. PhD Thesis, Aston University.

67.
Takenaka, K., Hatanaka, K., Yamazaki, W., and Nakahashi, K. (2008). Multidisciplinary design exploration for a winglet. Journal of Aircraft, 45, 1601-1611. crossref(new window)

68.
Takenaka, K., Obayashi, S., Nakahashi, K., and Matsushima, K. (2005). The application of MDO technologies to the design of a high performance small jet aircraft-lessons learned and some practical concerns. 35th AIAA Fluid Dynamics Conference and Exhibit, Toronto, Ontario. pp. AIAA 2005-4797.

69.
van Wijk, J. J. and Liere, R. V. (1993). HyperSlice: visualization of scalar functions of many variables. IEEE Visualization Conference, San Jose, CA. pp. 119-125.

70.
Winer, E. H. and Bloebaum, C. L. (2002a). Development of visual design steering as an aid in large-scale multidisciplinary design optimization. Part I: Method development. Structural and Multidisciplinary Optimization, 23, 412-424. crossref(new window)

71.
Winer, E. H. and Bloebaum, C. L. (2002b). Development of visual design steering as an aid in large-scale multidisciplinary design optimization. Part II: Method validation. Structural and Multidisciplinary Optimization, 23, 425-435. crossref(new window)

72.
Witten, I. H. and Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed. Boston: Morgan Kaufman.

73.
Wong, P. C. and Bergeron, R. D. (1997). 30 years of multidimensional multivariate visualization. Proceeding Scientific Visualization, Overviews, Methodologies, and Techniques. pp. 2-33.