International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
권호 표지
DOI QR코드

DOI QR Code

A study on multi-objective optimal design of derrick structure: Case study

  • Lee, Jae-chul (Department of Ocean System Engineering, Gyeongsang National University) ;
  • Jeong, Ji-ho (Korea Marine Equipment Research Institute, Energy & Marine Research Division) ;
  • Wilson, Philip (Faculty of Engineering and the Environment, University of Southampton) ;
  • Lee, Soon-sup (Department of Ocean System Engineering, Gyeongsang National University) ;
  • Lee, Tak-kee (Department of Ocean System Engineering, Gyeongsang National University) ;
  • Lee, Jong-Hyun (Department of Ocean System Engineering, Gyeongsang National University) ;
  • Shin, Sung-chul (Department of Naval Architecture and Ocean Engineering, Pusan National University)
  • Received : 2017.01.09
  • Accepted : 2017.09.25
  • Published : 2018.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

Keywords

References

  1. Chen, J., Wei, J., Jiang, W., 2016. Optimization of a twin-skeg container vessel by parametric design and CFD simulations. Int. J. Nav. Archit. Ocean. Eng. 8 (5), 446-474.
  2. Deb, K., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE TRAN. E. C 6 (2), 182-197.
  3. Grigoropoulos, G.J., Chalkias, D.S., 2010. Hull-form optimisation in calm and rough water. Computer-Aided Des. 42, 977-984. https://doi.org/10.1016/j.cad.2009.11.004
  4. Halder, P., Rhee, S.H., Samad, A., 2017. Numerical optimization of Wells turbine for wave energy extraction. Int. J. Nav. Archit. Ocean. Eng. 1 (9), 11-24.
  5. Hong, K.J., Jeon, K.K., Cho, Y.S., Choi, D.H., Lee, S.J., 2000. A study on the construction of response surfaces for design optimization. Trans. Korea Soc. Mech. Eng. A 24 (6), 1408-1418.
  6. Jung, S.P., Kim, Y.G., Park, T.W., 2012. A study on thermal characteristic analysis and shape optimization of a ventilated disc. Int. J. Precis. Eng. Manuf. 13 (1), 57-63. https://doi.org/10.1007/s12541-012-0008-4
  7. Kahraman, F., 2009. The use of response surface methodology for prediction and analysis of surface roughness of AISI 4140 steel. Mater. Technol. MTAEC9 43 (5), 267-270.
  8. Kim, H.J., Chun, H.H., Choi, H.J., 2007. Development of CFD based stern form optimization method. J. Soc. Nav. Archit. Korea 44 (6), 564-571. https://doi.org/10.3744/SNAK.2007.44.6.564
  9. Ko,D.H.,Ko,D.C., Lim,H.J.,Lee, J.M.,Kim, B.M., 2013. FE-simulation coupled with CFD analysis for prediction of residual stresses relieved by cryogenic heat treatment of Al6061 tube. Int. J. Precis. Eng.Manuf. 14 (8), 1301-1309. https://doi.org/10.1007/s12541-013-0177-9
  10. Lee, J.C., Jeong, J.H., Shin, S.C., 2014a. A study on prediction method for added resistance in waves using the genetic programming. In: Proceedings of the Annual Autumn Conference. SNAK, pp. 482-490.
  11. Lee, S.S., Lee, J.C., Shin, S.C., Kim, S.Y., Yoon, H.S., 2014b. A study on optimization of ship hull form based on neuro-response surface method (NRSM). J. Mar. Sci. Technol. 22 (6), 746-753.
  12. Lee, J.C., 2015. Application of Multi-objective Optimization for Marine Systems Using NRSM. Thesis for the degree of Doctor of Philosophy. Pusan National University.
  13. Lee, J.C., Jeong, J.H., Kharoufi, H., Shin, S.C., 2016. A study on a multi-objective optimization method based on neuro-response surface method (NRSM), 2016. Int. Conf. Des. Eng. Sci. (ICDES) 52. https://doi.org/10.1051/matecconf/20165202002.
  14. Lee, Y.S., Choi, Y.B., 2009. Hull form optimisation based on form parameter design. J. Soc. Nav. Archit. Korea 46 (6), 562-568. https://doi.org/10.3744/SNAK.2009.46.6.562
  15. Li, Z.Z., Cheng, T.H., Xuan, D.J., Ren, M., Shen, G.Y., Shen, Y.D., 2012. Optimal design for cooling system of batteries using DOE and RSM. Int. J. Precis. Eng. Manuf. 13 (9), 1641-1645. https://doi.org/10.1007/s12541-012-0215-z
  16. Mayers, R.H., Montgomery, D.C., 1995. Response Surface Methodology - Process and Product Optimization Using Designed Experiments. John Wiley & Sons.
  17. Park, J.H., Choi, J.E., Chun, H.H., 2015. Hull-form optimisation of KSUEZMAX to enhance resistance performance. Int. J. Nav. Archit. Ocean. Eng. 7, 100-114. https://doi.org/10.1515/ijnaoe-2015-0008
  18. Robert, H.N., 1989. Theory of the back-propagation neural network. IJCNN 1, 593-605.
  19. Ross, P.J., 1996. Taguchi Techniques for Quality Engineering 2nd. McGraw-Hill.
  20. Salman, J.M., 2014. Optimization of preparation conditions for activated carbon from palm oil fronds using response surface methodology on removal of pesticides from aqueous solution. Arab. J. Chem. 7 (1), 101-106. https://doi.org/10.1016/j.arabjc.2013.05.033
  21. Sankaya, M., Gullu, A., 2014. Taguchi design and response surface methodology based analysis of machining parameters in CNC turning under MQL. J. Clean. Prod. 65, 604-616. https://doi.org/10.1016/j.jclepro.2013.08.040
  22. Shin, S.C., 2007. A study on prediction of wake distribution by neuro-fuzzy system. Int. J. Fuzzy Log. Intell. Syst. 17 (2), 154-159.
  23. Xu, J.H., 2011. Application of artificial neural network (ANN) for prediction of maritime safety. Inf. Manag. Eng. Commun. Comput. Inf. Sci. 236, 34-38.
  24. Yu, J.W., Lee, C.M., Lee, I.W., Choi, J.E., 2017. Bow hull-form optimization in waves of a 66,000 DWT bulk carrier. Int. J. Nav. Archit. Ocean. Eng. 1-10. ISSN 2092-6782.
  25. Zhang, P., 2008. Parametric approach to design of hull forms. J. Hydrodynamics 20 (6), 804-810. https://doi.org/10.1016/S1001-6058(09)60019-6

Cited by

  1. Research on Optimal Design of Dual Derrick for the 7th Generation Ultra-Deepwater Offshore Platform vol.514, pp.None, 2018, https://doi.org/10.1088/1755-1315/514/2/022068
  2. Multi-Objective Optimization of Drilling Trajectory Considering Buckling Risk vol.12, pp.4, 2018, https://doi.org/10.3390/app12041829