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다분야통합최적설계 방법론의 병렬처리 성능 분석

Performances of Multidisciplinary Design Optimization Methodologies in Parallel Computing Environment

  • 안문열 (서울시립대학교 기계정보공학과) ;
  • 이세정 (서울시립대학교 기계정보공학과)
  • 발행 : 2007.12.01

초록

Multidisciplinary design optimization methodologies play an essential role in modern engineering design which involves many inter-related disciplines. These methodologies usually require very long computing time and design tasks are hard to finish within a specified design cycle time. Parallel processing can be effectively utilized to reduce the computing time. The research on the parallel computing performance of MDO methodologies has been just begun and developing. This study investigates performances of MDF, IDF, SAND and CO among MDO methodologies in view of parallel computing. Finally, the best out of four methodologies is suggested for parallel processing purpose.

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참고문헌

  1. YarKhan, A., Seymour, K., Sagi, K., Shi, Z., Dongarra, J., 2006, 'Recent Developments in GridSolve,' International Journal of High Performance Computing Applications (Special Issue: Scheduling for Large-Scale Heterogeneous Platforms), Robert, Y eds. Sage Science Press, Vol. 20
  2. Eres, M.H., Pound, G.E., Jiao, Z., Wason, J.L., Xu, F., Keane,A.J., Cox, S.J., 2004, 'Implementation and utilisation of a Grid-enabled problem solving environment in Matlab,' Future Generation Computer Systems(in Press)
  3. http://www.fh-kaernten.at/mdice/
  4. Eldred, M. S., Giunta, A. A., and Bart, G. B. W., 2005, DAKOTA, A Multilevel Parallel ObjectOriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis, Version 3.3+ Users Manual, Sandia National Laboratories
  5. Roger, J. L., 1996, 'DeMAID/GA an Enhanced Design Manager's Aid for Intelligent Decomposition,' AlAA paper, NASA Langley Research Center
  6. Park Hyung-Wook, Kim Sung-Chan, Kim Min-Soo and Choi Dong-Hoon, 2001, 'Decomposition Based Parallel Processing Technique for Efficient Collaborative Optimization,' Trans. Of the KSME(A), Vol. 25, No.5, pp. 883-890
  7. Cramer, E. J., Dennis, J., Frank, P. D., Lewis, R. M. and Shubin, GR., 1994, 'Problem Formulation for Multidisciplinary Optimization,' SIAM Journal on Optimization, Vol. 4, No.4, pp. 754-776 https://doi.org/10.1137/0804044
  8. Tedford, N. P. and Martins, J. R. R. A., 2006, 'On the Common Structure of MDO Problems: A Comparison of Architectures,' AIAA-2006-7080, 11th AlAA/ISSMO Multidisciplinary Analysis and Optimization Conference, Portsmouth, Virginia
  9. Alexandrov, N. M., Lewis, R. M., 2000, 'Analytical and Computational Aspects of Collaborative Optimization,' NASA TM2000-210104
  10. Colville, A. R., 1968, 'A Comparative Study on Nonlinear Programming Codes,' IBM New York Science Center Report No. 320-2949, Test Problem #8(pg. 32), IBM Corporation, Philadelphia Scientific Center, Philadelphia, PA

피인용 문헌

  1. analysis using MALDI-TOF mass spectrometry vol.59, pp.2, 2014, https://doi.org/10.1111/lam.12261