Journal of Ship and Ocean Technology

대한조선학회 (The Society of Naval Architects of Korea)
권호 표지

An Approximation Method in Collaborative Optimization for Engine Selection coupled with Propulsion Performance Prediction

  • Jang, Beom-Seon (Structure/Shipbuilding ＆ Plant R＆D Institute, Samsung Heavy Industries) ;
  • Yang, Young-Soon (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Suh, Jung-Chun (Department of Naval Architecture and Ocean Engineering, Seoul National University)
  • 발행 : 2004.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Ship design process requires lots of complicated analyses for determining a large number of design variables. Due to its complexity, the process is divided into several tractable designs or analysis problems. The interdependent relationship requires repetitive works. This paper employs collaborative optimization (CO), one of the multidisciplinary design optimization (MDO) techniques, for treating such complex relationship. CO guarantees disciplinary autonomy while maintaining interdisciplinary compatibility due to its bi-level optimization structure. However, the considerably increased computational time and the slow convergence have been reported as its drawbacks. This paper proposes the use of an approximation model in place of the disciplinary optimization in the system-level optimization. Neural network classification is employed as a classifier to determine whether a design point is feasible or not. Kriging is also combined with the classification to make up for the weakness that the classification cannot estimate the degree of infeasibility. For the purpose of enhancing the accuracy of a predicted optimum and reducing the required number of disciplinary optimizations, an approximation management framework is also employed in the system-level optimization.

키워드

참고문헌

  1. Alexandrov, N.M., and R.M. Lewis. 1999. Comparative properties of collaborative optimization and other approaches to MDO. Technical Report, 99-24, ICASE, Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, Hampton, VA
  2. Alexandrov, N.M., and R.M. Lewis. 2000. Analytical and computational properties of distributed approaches to MDO. Proc. 8th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA-2000-4718, 13-42
  3. Balling, R. J. and J. Sobieszcznski-Sobieski. 1996. Optimization of coupled systems: a critical overview of approaches. AIAA Journal, 34, 1, 6-17
  4. Booker, A. J., J.E. Dennis Jr., P.D. Frank, D.B. Serafini, V. Torczon and M.W. Trosset.1999. A rigorous framework for optimization of expensive functions by surrogates. Struct. Optim., 17, 1-13
  5. Braun, R.D. 1996. Collaborative Optimization: An Architecture for Large-Scale Distributed Design. Ph.D. Thesis, Stanford University
  6. Braun, R.D., A.A. Moore and I.M. Kroo. 1996. Use of the collaborative optimization architecture for launch vehicle design. Proc. 6th AIAA/USAF/NASN/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA Paper, 96-4018, 306-318
  7. Carlton, J.S. 1944. Marine propellers and propulsion, Oxford, I.K. : Butterworth-Heinemann
  8. Harvald, S.A. 1983. Resistance and Propulsion of Ships. John Wiley & Sons
  9. Haykin, S. 1994. Neural Networks, A Comprehensive Foundation. Macmillian College Publishing Company
  10. Holtrop, J. 1984. A statistical re-analysis of resistance and propulsion data. International Shipbuilding Progress, 272-276
  11. Holtrop, J. and G.G.J. Mennen. 1982. An approximate power prediction method. International Shipbuilding Process, 166-170
  12. Kroo, I.M. 1997. MDO for large-scale design, In: N.M. Alexandrov and M.Y.Hussaini(ed.). Multidisciplinary Design Optimization: State of the Art, SIAM, 22-44
  13. Kroo, I.M. and Y.M. Manning. 2000. Collaborative optimization: status and directions. Proc. 8th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA-2000-4721
  14. Kroo, 1., S. Altus, R. Braun, P. Gage and I. Sobieski. 1994. Multidisciplinary optimization methods for aircraft preliminary design. Proc. 5th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, AIAA 94-4325, 1, 697-707
  15. Neu, W.L., W.H. Mason, S. Ni, Z. Lin, A. Dasgupta and Y. Chen. 2000. A multidisciplinary design optimization scheme for containerships. Proc. 8th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA-2000-4791
  16. Neu, W.L., W.H. Mason, S. Ni, Z. Lin, A. Dasgupta and Y. Chen. 2000. A multidisciplinary design optimization scheme for containerships. Proc. 8th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA-2000-4791
  17. Sobieski, I.P., 1998. Multidisciplinary Design using Collaborative Optimization. Ph.D. Thesis, Department of Aeronautics and Astronautics, Stanford University
  18. Sobieski, I.P., V.M. Manning and I.M. Kroo. 1998. Response surface estimation and refinement in collaborative optimization. Proc. 7th AIAA/USAF/NASA/ISSMO Symp. on Multidisciplinary Analysis and Optimization, AIAA 98-4753, 359-370
  19. Yang, Y.S., B.S. Jang, Y.S. Yeun and H.S. Jung. 2000. A framework for managing approximation models in place of expensive simulations in optimization. Proc. 2nd ASMO UK/ISSMO Conference on Engineering Design Optimization, 249-256
  20. Yang, Y.S., B.S. Jang, Y.S. Yeun and W.S. Ruy. 2002. Managing approximation models in multi-objective optimization. Struct. Optim., 24, 141-156