Journal of Mechanical Science and Technology

, Volume 22, Issue 1, pp 25–33 | Cite as

Optimization design technique for reduction of sloshing by evolutionary methods

  • Kim Hyun-Soo
  • Lee Young-Shin


The oscillation of a fluid caused by external force, called sloshing, occurs in moving vehicles containing liquid masses, such as trucks, railroad cars, aircraft, and liquid rockets. This sloshing effect could be a severe problem in vehicle stability and control. Therefore, development of efficient and easy method to reduce sloshing effect is positively necessary.

In this study, optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively, the artificial neural network (ANN) and genetic algorithm (GA). ANN is used for the analysis of sloshing and GA is adopted as optimization algorithm. The considered storage tank for fluid is a rectangular tank. The design variables are width and installation location of the baffle, and sloshing reduction coefficient by baffle is used as an object function in the optimization. As a result of this study, the optimal design for sloshing reduction is presented.


Optimization design Sloshing Evolutionary methods ANN GA 


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  1. [1]
    H. F. Bauer and E. Eidel, Frictionless liquid sloshing in circular cylindrical container configurations, Aero Science and Technology 5 (1999) 301–311.CrossRefGoogle Scholar
  2. [2]
    S. Aliabadi, A. Johnson and J. Abedi, Comparison of finite element and pendulum models for simulation of sloshing, Computers and Fluids 32 (4) (2003) 535–545.zbMATHCrossRefGoogle Scholar
  3. [3]
    B. F. Chen and R. Nokes, Time-independent finite difference analysis of fully non-linear and viscous fluid sloshing in a rectangular tank, Journal of Computational Physics 209 (1) (2005) 47–81.zbMATHCrossRefGoogle Scholar
  4. [4]
    J. R. Cho and S. Y. Lee, Transient dynamic response analysis of liquid-storage tanks with baffles, Journal of the Korean Society for Aeronautical and Space Sciences 29 (4) (2001) 43–50.Google Scholar
  5. [5]
    J. R. Cho, M. J. Kim, S. Y. Lee and J. W. Huh, Dynamic suppression effects of liquid container to the baffle number and hole diameter, Journal of the Computational Structural Engineering Institute of Korea 15 (1) (2002) 147–154.Google Scholar
  6. [6]
    J. R. Cho and H. W. Lee, Free surface tracking for the accurate time response analysis of nonlinear liquid sloshing, Journal of Mechanical Science and Technology 19 (7) (2005) 1517–1525.CrossRefGoogle Scholar
  7. [7]
    T. Ikeda and S. Murakami, Auto parameter resonances in a structure/fluid interaction system carrying a cylindrical liquid tank, Journal of Sound and Vibration 285 (3) (2005) 517–546.CrossRefGoogle Scholar
  8. [8]
    H. S. Kim, J. H. Lee, Y. S. Lee and S. H. Ko, A study on the sloshing of the rectangular tank filled with water under translational motion, Tenth International Congress on Sound and Vibration (ICSV10). Stockholm, Sweden (2003).Google Scholar
  9. [9]
    Y. W. Kim, Y. S. Lee and S.H. Ko, Coupled vibration of partially fluid-filled cylindrical shells with ring stiffeners, Journal of Sound and Vibration 276 (2004) 869–897.CrossRefGoogle Scholar
  10. [10]
    Y. W. Kim and Y. S. Lee, Coupled vibration analysis of liquid-filled rigid cylindrical storage tank with an annular plate cover, Journal of Sound and Vibration 279 (2005) 217–235.CrossRefGoogle Scholar
  11. [11]
    Y. K. Kwack and S. H. Ko, Computational fluid dynamics study on two-dimensional sloshing in rectangular tank, Trans. of KSME (B). 27 (8) (2003) 1142–1149.Google Scholar
  12. [12]
    D. H. Lee, M. H. Kim, S. H. Kwon, J. W. Kim and Y. B. Lee, A Parametric sensitivity study on LNG tank sloshing loads by numerical simulations, Ocean Engineering 34 (1) (2007) 3–9.CrossRefGoogle Scholar
  13. [13]
    Y. S. Lee, H. S. Kim, J. H. Lee and S. H. Ko, A study on the damping of the sloshing of storage tank using wing and diaphragm baffle, Tenth International Congress on Sound and Vibration (ICSV10). Stockholm, Sweden (2003).Google Scholar
  14. [14]
    D. E. Goldberg, Genetic Algorithm in Search, Optimization, and Machine Learning, Addison-Wesley, (1989).Google Scholar
  15. [15]
    J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-Fuzzy and Soft Computing, Prentice Hall (1997).Google Scholar
  16. [16]
    J. H. Lee, A study on the sloshing of rectangular tank partially filled with water translational motion. M.S. THESIS, Chungnam National University, (2003).Google Scholar
  17. [17]
    MSC/Software, MSC/Dytran ver. 4.7 Users Manual, 1 (1999).Google Scholar
  18. [18]
    MSC/Software, MSC/Dytran ver. 4.7 Users Manual, 2 (1999).Google Scholar
  19. [19]
    R. D. Vanluchene and R. Sun, Neural networks in structural engineering, Microcomputers in Civil Engineering 5 (1990) 207–215.Google Scholar

Copyright information

© Korean Society of Mechanical Engineers 2008

Authors and Affiliations

  • Kim Hyun-Soo
    • 1
  • Lee Young-Shin
    • 2
  1. 1.Dept. of International Joint DevelopmentR&D Division, Korea Aerospace Industries, Ltd, Youngdang-Ri, SacheonGyeongnamSouth Korea
  2. 2.Dept. of Mechanical Design EngineeringChungnam National UniversityYuseong-gu, DaejeonSouth Korea

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