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Multi-Objective Optimum Shape Design of Rotor-Bearing System with Dynamic Constraints Using Immune-Genetic Algorithm

면역.유전 알고리듬을 이용한 로터 베어링시스템의 다목적 형상최적설계

  • Published : 2000.07.01

Abstract

An immune system has powerful abilities such as memory, recognition and learning how to respond to invading antigens, and has been applied to many engineering algorithms in recent year. In this pap er, the combined optimization algorithm (Immune- Genetic Algorithm: IGA) is proposed for multi-optimization problems by introducing the capability of the immune system that controls the proliferation of clones to the genetic algorithm. The optimizing ability of the proposed combined algorithm is identified by comparing the result of optimization with simple genetic algorithm for two dimensional multi-peak function which have many local optimums. Also the new combined algorithm is applied to minimize the total weight of the shaft and the transmitted forces at the bearings. The inner diameter oil the shaft and the bearing stiffness are chosen as the design variables. The dynamic characteristics are determined by applying the generalized FEM. The results show that the combined algorithm and reduce both the weight of the shaft and the transmitted forces at the bearing with dynamic conatriants.

Keywords

Rotor Shaft;Critical Speed;Bending Stress;Genetic Algorithm;Immune Algorithm;Multi-Objective Optimum Design

References

  1. Nelson, H. D. and McVaugh, J. M., 1976, 'The Dynamics of Rotor-Bearing Systems Using Finite Elements,' Trans. ASME Journal of Engineering for Industry, Vol. 98, No. 2, pp. 71-75
  2. Choi, W. H., Yang, B. S., and Joo, H. J., 1996, 'Optimum Balancing of Rotating Machinery Using Genetic Algorithm,' in Proceedings of 6th International Symposium on Transport Phenomena and Dynamics of Rotating Machinery, Hawaii, USA, February 25-28, pp. 106-115
  3. Choi, B. G. and Yang, B. S., 2000, 'Optimum Shape Design of Rotor Shafts Using Genetic Algorithm,' Journal of Vibration and Control, Vol. 6, pp. 207-222 https://doi.org/10.1177/107754630000600203
  4. Yang, B. S., Choi, B. G., Yu, Y. H., and Nan, H. T., 1999, 'Optimum Design of a Damping Plate with an Unconstrained Viscoelastic Damping Layer Using Combined Genetic Algorithm,' KSME International Journal, Vol. 13, No. 5, pp. 387-397
  5. Mori, K., Tsukiyama, M., and Fukuda, T., 1993, 'Application of an Immune Algorithm to Multi-Optimization Problem,' T. IEE Japan, Vol. 117-c, No. 5, pp. 593-598
  6. Ivan, M. R., Jonathan, B., and David, M., 1989, Immunology, Grower Medical Publishing, New York
  7. Shima, T., 1995, 'Global Optimization by a Miche Method for Evolution Algorithm,' システム制御情報學會論文誌, Vol. 8, No. 2, pp. 94-96
  8. Shima, T., 1995, 'Global Optimization by a Niche Method for Genetic Algorithm,' システム制御情報學會論文誌, Vol. 8, No. 5, pp. 233-235
  9. Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization & Machine Leaning, Addision Wesley, New York, USA
  10. Diewald, W. and Nordmann, R., 1990. 'Parameter Optimization for the Dynamics of Rotating Machinery,' in Proceedings of the 3rd International Conference on Rotor Dynamics, Lyon, France, pp. 51-55
  11. Davis, L. ed., 1991, Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, USA
  12. Shiau, T. N. and Hwang, J. L., 1990, 'Optimum Weight Design of a Rotor Bearing System with Dynamic Behavior Constraints,' Trans. ASME Journal of Engineering for Gas Turbines and Power, Vol. 112, pp. 454-462
  13. Shiau, T. N. and Chang, J. R., 1993, 'Multi-Objective Optimization of Rotor-Bearing System with Critical Speed Constraints,' Trans ASME Journal of Engineering for Gas Turbines and Power, Vol. 115, pp. 246-255
  14. Shiau, T. N. and Hwang, J. L., 1988, 'Minimum Weight Design of a Rotor Bearing System with Multiple Frequency Constraints,' Trans. ASME Journal of Engineering for Gas Turbines and Power, Vol. 110, No. 2, pp. 592-599