한국소음진동공학회논문집 (Transactions of the Korean Society for Noise and Vibration Engineering)

  • 제12권5호
  • /
  • Pages.380-388
  • /
  • 2002
  • /
  • 1598-2785(pISSN)
  • /
  • 2287-5476(eISSN)
한국소음진동공학회 (The Korean Society for Noise and Vibration Engineering)
권호 표지
DOI QR코드

DOI QR Code

조합 유전 알고리듬을 이용한 증기 터빈 회전체-베어링 시스템의 최적설계

Optimal Design for Steam-turbine Rotor-bearing System Using Combined Genetic Algorithm

  • 발행 : 2002.05.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

This paper describes the optimum design for low-pressure steam turbine rotor of 1,000 MW nuclear power plant by using a combined genetic algorithm, which uses both a genetic algorithm and a local concentrate search algorithm (e.g. simplex method). This algorithm is not only faster than the standard genetic algorithm but also supplies a more accurate solution. In addition, this algorithm can find the global and local optimum solutions. The objective is to minimize the resonance response (Q factor) and total weight of the shaft, and to separate the critical speeds as far from the operating speed as possible. These factors play very important roles in designing a rotor-bearing system under the dynamic behavior constraint. In the present work, the shaft diameter, the bearing length, and clearance are used as the design variables. The results show that the proposed algorithm can improve the Q factor and reduce the weight of the shaft and the 1st critical speed.

키워드

참고문헌

  1. Rao, S. S., 1996, 'Engineering Optimization', John Wiley & Sons, Inc
  2. Nelder, J. A. and Mead. R., 1965, 'A Simplex Method for Function Minimization'. Computer Journal, Vol. 7, pp. 308-313 https://doi.org/10.1093/comjnl/7.4.308
  3. Box, M. J., 1965, 'A New Method of Constrained Optimization and a Comparison with Other Methods,' Computer Journal, Vol. 8, No. 1. pp. 42-52 https://doi.org/10.1093/comjnl/8.1.42
  4. Goldberg, D. E.. 1989, 'Genetic Algorithms in Search, Optimization & Machine Learning', Addison-Wesley Publishing Company, pp. 1-146
  5. Davis, L., 1991, 'Handbook of Genetic Algohthms,' Van Nostrand Reinhold, pp. 3-23
  6. 최병근, 양보석, 1996, '유전 알고리즘을 이용 한 회전축계의 진동 최적설계,' 한국소음진동공학회지 제 7 권 제 3 호, pp, 467-475
  7. 최병근, 양보석, 2002, '유전 알고리즘을 이용 한 회전체의 진동 저감,' 한국소음진동공학회지 제 10 권 제 2 호, pp. 199-206
  8. Choi, B. G. and Yang, B. S., 2000, 'Optimum Shape Design of Shaft Using Genetic Algohthm', Journal of Vibration and Control, Vol. 6, No. 1, pp. 207-222 https://doi.org/10.1177/107754630000600203
  9. Choi, B.G. and Yang, B. S., 2001, 'Optimal Design of Rotor-Beahng Systems Using Immune-genetic Algorithm', Trans. ASME, Journal of Vibration and Acoustics, Vol. 123, No. 3, pp. 398-401 https://doi.org/10.1115/1.1377021
  10. Kim, Y. C. and Yang, B. S., 2001. 'Enhanced Genetic Algorithm for Fast and Accurate Global and Local Optimization Search',Proceedings of 8th Inter-national Congress on Sound and Vibration, July 2-6, Hong Kong, pp. 2143-2150
  11. 김영찬, 양보석, 2001, '다봉성 함수의 최적화 를 위한 향상된 유전 알고리듬의 제안,' 퍼지 및 지능시스템학회 논문지, Vol. 11, No. 5, pp. 373-378
  12. Someya, T., 1988, 'Journal-bearing Data book,' Springer-Verlag
  13. 류근배, 최영준, 1994, '유성생식 유전 알고리 듬: 다중선택과 이배성이 탐색성능에 미치는 영향', 대한전기학회 하계학술대회논문집, pp. 165-167
  14. Friswell, M. I. and Mottershead, J. E., 1996, 'Finite Element Model Updating in Structural Dynamics', Kluwer Academic Publi-shers, Vol. 38, pp. 56-59