Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 838354, 12 pages
http://dx.doi.org/10.1155/2013/838354
Research Article

Development of an Efficient Hull Form Design Exploration Framework

1Department of Mechanical Engineering, Kyunghee University, Yongin 446-701, Republic of Korea
2Department of Computational and Data Science, George Mason University, Fairfax, VA 22030, USA

Received 16 April 2013; Accepted 24 June 2013

Academic Editor: Ming Li

Copyright © 2013 Shinkyu Jeong and Hyunyul Kim. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, Boston, Mass, USA, 1989.
  2. K. Deb, Multi-objective optimization using evolutionary algorithms, John Wiley & Sons, Chichester, UK, 2001. View at Zentralblatt MATH · View at MathSciNet
  3. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks. Part 1 (of 6), pp. 1942–1948, December 1995. View at Scopus
  4. T. W. Lowe and J. Steel, “Conceptual hull design using a genetic algorithm,” Journal of Ship Research, vol. 47, no. 3, pp. 222–236, 2003. View at Google Scholar · View at Scopus
  5. Y. Tahara, S. Tohyama, and T. Katsui, “CFD-based multi-objective optimization method for ship design,” International Journal for Numerical Methods in Fluids, vol. 52, no. 5, pp. 499–527, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Tahara, D. Peri, E. F. Campana, and F. Stern, “Computational fluid dynamics-based multiobjective optimization of a surface combatant using a global optimization method,” Journal of Marine Science and Technology, vol. 13, no. 2, pp. 95–116, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Tahara, T. Hino, M. Kandasamy, W. He, and F. Stern, “CFD-based multiobjective optimization of waterjet propelled high speed ships,” in Proceedings of the 11th International Conference on Fast Sea Transportation (FAST '11), Honolulu, Hawaii, 2011.
  8. M. A. Gammon, “Optimization of fishing vessels using a multi-objective genetic algorithm,” Ocean Engineering, vol. 38, no. 10, pp. 1054–1064, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. J. T. Knight, F. T. Zahradka, D. J. Singer, and M. D. Collette, “Multi-objective particle swarm optimization of a planing craft with uncertanity,” in Proceedings of the 11th International Conference on Fast Sea Transportation (FAST '11), Honolulu, Hawaii, 2011.
  10. G. E. P. Box, W. G. Hunter, and J. S. Hunter, Statistics for Experimenters, John Wiley & Sons, New York, NY, USA, 1978. View at Zentralblatt MATH · View at MathSciNet
  11. S. Jeong, M. Murayama, and K. Yamamoto, “Efficient optimization design method using kriging model,” Journal of Aircraft, vol. 42, no. 2, pp. 413–420, 2005. View at Google Scholar · View at Scopus
  12. K. Sugimura, S. Obayashi, and S. Jeong, “Multi-objective optimization and design rule mining for an aerodynamically efficient and stable centrifugal impeller with a vaned diffuser,” Engineering Optimization, vol. 42, no. 3, pp. 271–293, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. D. R. Jones, M. Schonlau, and W. J. Welch, “Efficient global optimization of expensive black-box functions,” Journal of Global Optimization, vol. 13, no. 4, pp. 455–492, 1998. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  14. O. Schabenberger and C. A. Cotway, Statistical Methods For Spatial Data Analysis, New York, NY, USA, Champmand and Hall/CRC, 1st edition, 2004.
  15. T. C. Bailey and W. J. Krzanowski, “An overview of approaches to the analysis and modelling of multivariate geostatistical data,” Mathematical Geosciences, vol. 44, no. 4, pp. 381–383, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Jeong, K. Chiba, and S. Obayashi, “Data mining for aerodynamic design space,” Journal of Aerospace Computing, Information and Communication, vol. 2, no. 11, pp. 452–469, 2005. View at Google Scholar · View at Scopus
  17. T. Kohonen, Self-Organizing Maps, vol. 30, Springer, Berlin, Germany, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  18. F. H. Todd and F. X. Forest, A Proposed New Basis For the Design of Single New Screw MerchAnt Ship Forms and a Standard Series of Lines, Transactions of S.N.A.M.E., 1951.
  19. F. H. Todd, Some Further Experiments on Single Screw Merchant Ship Forms-Series 60, Transactions of S.N.A.M.E., 1953.
  20. H. Lackenby, On the Systematic Geometrical Variation of Ship Forms, vol. 92, Transaction of Royal Institute of Naval Architects, 1950.
  21. H. Y. Kim, C. Yang, and F. Noblesse, “Hull form optimization of reduced resistance and improved seakeeping via practical design-oriented CFD tool,” in Proceedings of the Grand Challenges in Modeling and Simulation Conference, pp. 375–385, 2010.
  22. H. Y. Kim, C. Yang, and H. H. Chun, “A combined local and global hull form modification for hydrodynamic optimization,” in Proceedings of the 28th Symposium on Naval Hydrodynamics, Pasadena, CA, USA, 2010.
  23. A. de Boer, M. S. van der Schoot, and H. Bijl, “Mesh deformation based on radial basis function interpolation,” Computers and Structures, vol. 85, no. 11–14, pp. 784–795, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Yang, H. Y. Kim, G. Delhommeau, and F. Noblesse, “The Neumann-Kelvin and Neumann-Michell linear models of steady flow about a ship,” in Proceedings of the 12th International Congress of the International Maritime Association of the Mediterranean (IMAM '07), pp. 129–136, Varna, Bulgaria, September 2007. View at Scopus
  25. C. Yang, H. Y. Kim, and F. Noblesse, “A practical method for evaluating steady flow about a ship,” in Proceedings of the 7th International Conference on Fast Sea Transportation, 2007.
  26. M. D. McKay, R. J. Beckman, and W. J. Conover, “A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,” Technometrics, vol. 21, no. 2, pp. 239–245, 1979. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  27. A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: a review,” ACM Computing Surveys, vol. 31, no. 3, pp. 316–323, 1999. View at Google Scholar · View at Scopus
  28. P. J. Rousseeuw, “Silhouettes: a graphical aid to the interpretation and validation of cluster analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987. View at Google Scholar · View at Scopus
  29. S. Jeong and K. Shimoyama, “Review of data mining for multi-disciplinary design optimization,” Proceedings of the Institution of Mechanical Engineers G, vol. 225, no. 5, pp. 469–479, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. ITTC, “1978 performance prediction method,” ITTC Recommended Procedures and Guideline, Procedure 7. 5-02-01-03, Revision 00, 2002.
  31. N. Su, Z.-G. Yu, V. Anh, and K. Bajracharya, “Fractal tidal waves in coastal aquifers induced both anthropogenically and naturally,” Environmental Modelling and Software, vol. 19, no. 12, pp. 1125–1130, 2004. View at Publisher · View at Google Scholar · View at Scopus
  32. M. Li, C. Cattani, and S.-Y. Chen, “Viewing sea level by a one-dimensional random function with long memory,” Mathematical Problems in Engineering, vol. 2011, Article ID 654284, 13 pages, 2011. View at Publisher · View at Google Scholar · View at Scopus