Journal of Ship and Ocean Technology

The Society of Naval Architects of Korea (대한조선학회)
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FE Model Based Parametric Study Support System

  • Jang, Beom-Seon (Seoul Offshore Design Center, Offshore Basic Engineering Team, Samsung Heavy Industries)
  • Published : 2008.12.31
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Abstract

In preliminary ship design, a parametric study is a more realistic way to explore design space and analyze design problem than an optimization technique due to time-consuming computational work or a difficulty in incorporating all constraints into the optimization formulation. In the parametric study, feasible alternatives are examined in various aspects; the best one can be selected. Among the aspects, the strength assessment by FE analysis is an essential process in the ship design. This paper proposes a system to facilitate a parametric study for FE model based on design of experiment (DOE). It works on a FE pre-processor environment and assists a user to define a parametric study by interacting with FE model. It also provides an interface module with a FE solver in order to control the input file and extract predefined FE results from the output file. Based on the proposed system, a better understating and a better design are expected to be achieved.

Keywords

References

  1. Engineous Software, Inc. 1999. iSIGHT Designer's Guide : version 5.0, North Carolina, 1999
  2. Goldberg, D.E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Massachusetts
  3. Haftka, R.T., R.V. Grandhi. 1986. Structural shape optimization-a survey, Computer Methods in Applied Mechanics and Engineering. 57, 91-106 https://doi.org/10.1016/0045-7825(86)90072-1
  4. Huh, J.S. 2000. Development of a Mechanical Design Tool for MEMS Integrating Commercial Code, Master's thesis of KAIST
  5. Ku, J.K., S.S. Rao and L. Chen. 1998. Taguchi-aided Search Method for Design Optimization of Engineering Systems, Engineering Optimization, 30, 1-23 https://doi.org/10.1080/03052159808941235
  6. Kwak, B.M. 1994. A review on shape optimal design and sensitivity analysis, JSCE Journal Structure Engineering/Earthquake Engineering, 10, 4, 159-174
  7. Park, S.H. and B.S. Lee. 1995. Developing a Maneuvering Design Capability for Naval Vessels. Proc. PRADS'95, Seoul, pp. 1.616-1.628
  8. Park, G.J. and Arora, K.S. 1987 Role of Database Management in Design Optimization System, Journal of Aircraft, 24, 11, 745-750 https://doi.org/10.2514/3.45516
  9. Peace, G.S. 1995. Taguchi Methods: A hands-On Approach to Quality Engineering Addison Wesley, Massachusetts
  10. Phadke, M.S. 1989. Quality Engineering Using Robust Design, Prentice Hall, New Jersey
  11. Rajeev, S., and C.S. Krishnamoorthy. 1992. Discrete Optimization of Structures Using Genetic Algorithms, Journal of Structural Engineering, ASCE, 118, 1233-1250 https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233)
  12. Salajegheh, E. and G.N. Vanderplassts. 1993. Optimum Design of Structures with Discrete Size and Shape Variables, Structural Optimization, 6, 2, 79-85 https://doi.org/10.1007/BF01743339
  13. Shin, J.H., B.M. Kwak. 2001. Optimization of Machine Tools Structure using a CADbased Optimal Design System, Proceedings of the KSME 2001 Spring Annual Meeting, 926-931
  14. Vanderplaats Research & Development, Inc. 1998. VisualDOC Manual : version 1.0, Colorado Springs, CO
  15. Wu, S.J. and P.T. Chow. 1995. Genetic Algorithms for Nonlinear Mixed Discrete-Integer Optimization problems via Meta-Generic Parameter Optimization, Engineering Optimization, 24, 137-159 https://doi.org/10.1080/03052159508941187
  16. Yu, Y.G. 2003. Development of a CAD based General Purpose Optimal Design and Its Application to Structural Shape for Fatigue Life, Master's thesis of KAIST