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Shape Design of Passages for Turbine Blade Using Design Optimization System

최적화설계시스템을 이용한 터빈블레이드 냉각통로의 형상설계

  • 정민중 (Computation Biology Research Center, National Institute of Advanced Industrial Science & Technology) ;
  • 이준성 (경기대학교 기계시스템디자인공학부)
  • Published : 2005.07.01

Abstract

In this paper, we developed an automatic design optimization system for parametric shape optimization of cooling passages inside axial turbine blades. A parallel three-dimensional thermoelasticity finite element analysis code from an open source system was used to perform automatic thermal and stress analysis of different blade configuration. The developed code was connected to an evolutionary optimizer and built in a design optimization system. Using the optimization system, 279 feasible and optimal solutions were searched. It is provided not only one best solution of the searched solutions, but also information of variation structure and correlation of the 279 solutions in function, variable, and real design spaces. To explore design information, it is proposed a new interpretation approach based on evolutionary clustering and principal component analysis. The interpretation approach might be applicable to the increasing demands in the general area of design optimization.

Keywords

References

  1. Lee, J.S., 2004, 'Optimal Design for 3D Structures Using Artificial Intelligence: Its Application to Accelerometer,' Journal of Fuzzy Logic and Intelligent Systems, Vol. 14, No.4, pp. 445-450 https://doi.org/10.5391/JKIIS.2004.14.4.445
  2. Kim, M.S and Choi, D.H., 1997, 'A Study on the Treatment of a Max-Value Cost Function in Parametric Optimization,' Transactions of the KSME(A), Vol. 21, No. 10, pp. 1561-1570
  3. Jeong, M.J. and Yoshimura, S., 2002, 'An Evolutionary Clustering Approach to Pareto Solutions in Multiobjective Optimization,' ASME Proceedings of Design Automation Conference, DETC2002/DAC-34048
  4. Jeong, M.J., Dennis, B.H. and Yoshimura, S., 2003, 'Multidimensional Solution Clustering and Its Application to The Coolant Passage of A Turbine Blade,' ASME Proceedings of Design Automation Conference, DETC2003/DAC-48764
  5. Jeong, M.J., 2003, Integrated Support System for Decision-Making in Design Optimization, Ph.D. Theis, The University of Tokyo, December
  6. Dennis, B. H., Egorov, I. N., Sobieczky, H., Dulikravich, G.S. and Yoshimura, S., 2003, 'Parallel Thermoelasticity Optimization of 3-D Serpentine Cooling Passages in Turbine Blades,' ASME Turbo Expo 2003, ASME GT2003-38180
  7. Nomoto, H., Konga, A., Ito, S., Fukuyama, Y., Otomo, F., Shibuya, S., Sato, M., Kobayashi, Y. and Matsuzaki, H., 1997, 'The Advanced Cooling Technology for the 1500 C Class Gas Turbine: Steam-Cooled Vanes and Air-Cooled Blades,' ASME Journal of Engineering for Gas Turbines and Power, Vol. 119, pp. 624-632 https://doi.org/10.1115/1.2817030
  8. Krueger, U., Kusterer, K., Lang, G., Roesch, H., Bohn, D. and Martens, E., 2001, 'Analysis of the Influence of Cooling Steam Conditions on the Cooling Efficiency of a Steam-Cooled Vane Using the Conjugate Calculation Technique,' ASME Turbo Expo 2001, ASME 2001-GT-0056
  9. Dennis, B.H., Egorov, I.N., Han, Z.X., Dulikravich, G.S. and Poloni, C., 2001, 'Multi-Objective Optimization of Turbomachinery Cascades for Minimum Loss, Maximum Loading, and Maximum Gap-to-Chord Ratio,' International Journal of Turbo & Jet-Engines, Vol. 18, No.3, pp. 201-210
  10. ADVENTURE Project Official Homepage, http://adventure.q.t.u-tokyo.ac.jp
  11. Back, T., 1996, Evolution Algorithms in Theory and Practice, Oxford University Press, New York
  12. Egorov, I.N., Kretinin, G.V., Leshchenko, I.A. and Kostiuk, S.S, 1999, 'The Methodology of Stochastic Optimization of Parameters and Control Laws for the Aircraft Gas-Turbine Engines Flow Passage Components,' ASME paper 99-GT-227
  13. Jain, A.K. and Dubes, R.C., 1989, Algorithms for Clustering Data, Prentice-HaIl, Englewood Cliffs
  14. Baraldi, A. and Blonda, P., 1999, 'A Survey of Fuzzy Clustering Algorithms for Pattern Recognition - Part I,' IEEE Transactions on Systems, Man, and Cybernetics, Part B, Vol. 29, No.6, pp. 778-786 https://doi.org/10.1109/3477.809032
  15. Davies, D.L. and Bouldin, D.W., 1979, 'A Cluster Separation Measure,' IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI, Vol. 1, No.4, pp. 224-227 https://doi.org/10.1109/TPAMI.1979.4766909
  16. Fukunaga, K. and Koonts, W.L.G., 1970, 'Application of the Karhunen-Loeve Expansion to Feature Selection and Ordering,' IEEE Transactions on Computers, Vol. 19, Part C, pp. 311-318 https://doi.org/10.1109/T-C.1970.222918