Journal of Advanced Marine Engineering and Technology

The Korean Society of Marine Engineering (한국마린엔지니어링학회)
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Multiobjective optimization strategy based on kriging metamodel and its application to design of axial piston pumps

크리깅 메타모델에 기반한 다목적최적설계 전략과 액셜 피스톤 펌프 설계에의 응용

  • Received : 2013.10.25
  • Accepted : 2013.11.08
  • Published : 2013.11.30
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Abstract

In this paper, a Kriging metamodel-based multi-objective optimization strategy in conjunction with an NSGA-II(non-dominated sorted genetic algorithm-II) has been employed to optimize the valve-plate shape of the axial piston pump utilizing 3D CFD simulations. The optimization process for minimum pressure ripple and maximum pump efficiency is composed of two steps; (1) CFD simulation of the piston pump operation with various combination of six parameters selected based on the optimization principle, and (2) applying a multi-objective optimization approach based on the NSGA-II using the CFD data set to evaluate the Pareto front. Our exploration shows that we can choose an optimal trade-off solution combination to reach a target efficiency of the axial piston pump with minimum pressure ripple.

NSGA-II와 함께 크리깅 메타모델기반 다목적최적설계 전략을 3차원 CFD 시뮬레이션을 통해 액셜 피스톤 펌프의 밸브 플레이트 형상을 최적화하는데 적용하였다. 펌프의 압력 변동을 저감하고 수력 효율을 최대화하기 위한 최적설계 과정은 두 단계, 즉 (1) 밸브 플레이트 상의 6개 형상 설계 변수를 선정하고 각 설계변수의 변화에 따른 CFD 해석을 수행하며, (2) CFD 데이터를 이용한 NSGA-II에 기반한 다목적최적설계 접근방식으로 최소 맥동 압력과 펌프 효율 설계에 대해 파레토 프론트를 평가하는 것으로 구성된다. 이들 결과로부터 최소 맥동 압력을 가지며 액셜 피스톤 펌프의 목표 효율에 도달하는 최적 절충해를 선택할 수 있었다.

Keywords

References

  1. J. H. Jeong, J. K. Kim, and Y. K. Suh, "Numerical study on hydraulic fluid flows within axial piston pump," Transactions of The Korean Society of Mechanical Engineers B, vol. 34, no. 2, pp. 129-136, 2010.
  2. Y. B. Lee and G. C. Lee, "An experimental study on the improving noise characteristic of hydraulic power unit," Journal of the Korean Society of Marine Engineering, vol. 37, no. 6, pp. 638-643, 2013. https://doi.org/10.5916/jkosme.2013.37.6.638
  3. N. D. Manring, "Valve-plate design for an axial piston pump operating at low displacements," American Society of Mechanical Engineers Journal of Mechanical Design, vol. 125, no. 1, pp. 200-205, 2003.
  4. J. Kim, H. Kim, Y. Lee, J. Jung, and S. Oh, "Measurement of fluid film thickness on the valve plate in oil hydraulic axial piston pumps (Part II: Spherical Design Effects)," Journal of Mechanical Science and Technology, vol. 19, no. 2, pp. 655-663, 2005. https://doi.org/10.1007/BF02916187
  5. S. Wang, "The analysis of cavitation problems in the axial piston pump," American Society of Mechanical Engineers Journal of Fluids Engineering, vol. 132, no. 7, pp. 074502, 2010.
  6. S. Wang, "Improving the volumetric efficiency of the axial piston pump," American Society of Mechanical Engineers Journal of Mechanical Design, vol. 134, no. 11, pp. 111001, 2012.
  7. D. H. Jang, S. K. Lee, J. H. Kwon, and S. H. Park, "A study on pressure, flow fluctuation and noise in the cylinder of swash plate type axial piston pump," Transactions of the Korea fluid power systems society, vol. 6, no. 3, pp. 1-9, 2009.
  8. R. E. Steuer, Multiple Criteria Optimization: Theory, Computation and Application, John Wiley & Sons, New York, 1986.
  9. S. H. Baek, S. S. Cho, H. S. Kim, and W. S. Joo, "Trade-off analysis in multi-objective optimization using chebyshev orthogonal polynomials," Journal of Mechanical Science and Technology, vol. 20, no. 3, pp. 366-375, 2006. https://doi.org/10.1007/BF02917519
  10. S. H. Baek, S. S. Cho, and W. S. Joo, "Response surface approximation for fatigue life prediction and its application to multi-criteria optimization with a priori preference information," Transactions of The Korean Society of Mechanical Engineers A, vol. 33, no. 2, pp. 114-126, 2009. https://doi.org/10.3795/KSME-A.2009.33.2.114
  11. X. G. Song, L. Wang, S. H. Baek, and Y. C. Park, "Multidisciplinary optimization of a butterfly valve," International Society of Automation Transactions, vol. 48, no. 3, pp. 370-377, 2009.
  12. T. Goel, N. Stander, and Y. Lin, "Efficient resource allocation for genetic algorithm based multi-objective optimization with 1,000 simulations," Structural and Multidisciplinary Optimization, vol. 41, no. 3, pp. 421-432, 2010. https://doi.org/10.1007/s00158-009-0426-9
  13. K. Deb, Multi-objective Optimization Using Evolutionary Algorithms, Wiley, New York, 2001.
  14. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, "A fast and elitist multiobjective genetic algorithm: NSGA-II," Institute of Electrical and Electronics Engineers on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, 2002.
  15. I. Ferreira, J. A. Cabral, P. Saraiva, and M. C. Oliveira, "A multidisciplinary framwork to support the design of injection mold tools," Structural and Multidisciplinary Optimization, vol. 48, no. 220, pp. 1-21, 2013. https://doi.org/10.1007/s00158-013-0895-8
  16. ANSYS ICEM CFD 10.0 User Guide, 2010.
  17. ANSYS CFX 10.0 Theory Guide, 2010.
  18. S. H. Baek, K. M. Kim, S. S. Cho, D. Y. Jang, and W. S. Joo, "A sequential optimization algorithm using metamodel based multilevel analysis," Transactions of The Korean Society of Mechanical Engineers A, vol. 33, no. 9, pp. 892-902, 2009. https://doi.org/10.3795/KSME-A.2009.33.9.892
  19. G. G. Wang and S. Shan, "Review of meta-modeling techniques in support of engineering design optimization," American Society of Mechanical Engineers Journal of Mechanical Design, vol. 129, no. 4, pp. 370-380, 2007.
  20. E. Acar, R. T. Haftka, and T. F. Johnson, "Tradeoff of uncertainty reduction mechanisms for reducing weight of composite laminates," American Society of Mechanical Engineers Journal of Mechanical Design, vol. 129, no. 3, pp. 266-274, 2007.
  21. E. M. Kasprzak and K. E. Lewis, "Pareto analysis in multiobjective optimization using the collinearity theorem and scaling method," Structural and Multidisciplinary Optimization, vol. 22, no. 3, pp. 208-211, 2001. https://doi.org/10.1007/s001580100138
  22. H. A. Jensen and A. E. Sepulveda, "A preference aggregation rule approach for structural optimization," Structural and Multidisciplinary Optimization, vol. 16, no. 4, pp. 246-257, 1998. https://doi.org/10.1007/BF01271431
  23. B. J. Hunt, V. Y. Blouin, and M. M. Wiecek, "Modeling relative importance of design criteria with a modified pareto preference," American Society of Mechanical Engineers Journal of Mechanical Design, vol. 129, no. 9, pp. 907-914, 2007.
  24. A. Engau and M. M. Wiecek, "2D decision-making for multicriteria design optimization," Structural and Multidisciplinary Optimization, vol. 34, no. 4, pp. 301-315, 2007. https://doi.org/10.1007/s00158-006-0078-y
  25. ANSYS DesignXplorer 14.5 Workbench User Guide, 2012.
  26. G. Rennen, "Subset selection from large datasets for kriging modeling," Structural and Multidisciplinary Optimization, vol. 38, no. 6, pp. 545-569, 2009. https://doi.org/10.1007/s00158-008-0306-8

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