• DocumentCode
    175608
  • Title

    An optimal design of marine systems based on neuro-response surface method

  • Author

    Jae-chul Lee ; Sung-chul Shin ; Soo-young Kim

  • Author_Institution
    Dept. of Naval Archit. & Ocean Eng., Pusan Nat. Univ., Busan, South Korea
  • fYear
    2014
  • fDate
    19-21 Aug. 2014
  • Firstpage
    58
  • Lastpage
    66
  • Abstract
    The shapes of marine system have an effect on various performances; hydrodynamic, structural, propulsion. In case of engineering structure including the marine system, most performances are determined during initial design stage. However, the prediction of system performance is very difficult because performance analysis is time consuming. Thus, if the design techniques considering the performance were developed, it will be very useful design tool in the initial design stage. The major objective in this research is establishing a design methodology for marine system in the initial design stage. For this purpose, we proposed a framework for optimal design based on the Neuro-Response Surface Method (NRSM). The constructed framework is composed of three parts: the definition of the geometry, the generation of the design space using NRSM, and an optimization process. The optimization algorithm of the constructed framework uses a Back-Propagation Neural Network (BPN) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The BPN is used for generating the design space, and the optimization process is done using NSGA-II. Through case study on a 5MW TLP-type wind turbine and ultimate strength of ship stiffened panel, we have confirmed the usefulness of the constructed framework in view of hydrodynamic performances and structural performances.
  • Keywords
    backpropagation; design engineering; genetic algorithms; hydrodynamics; marine propulsion; marine systems; mechanical engineering computing; neural nets; plates (structures); response surface methodology; shapes (structures); ships; sorting; wind turbines; BPN; NRSM; NSGA-II; TLP-type wind turbine; back-propagation neural network; design space generation; design techniques; engineering structure; hydrodynamic performances; marine system optimal design; neuroresponse surface method; nondominated sorting genetic algorithm-II; optimization process; performance analysis; power 5 MW; propulsion performances; ship stiffened panel ultimate strength; structural performances; system performance prediction; Acceleration; Accuracy; Biological neural networks; Optimization; Response surface methodology; Wind turbines; 5MW TLP-type wind turbine; Back-Propagation Neural Network; Neuro-Response Surface Method; Non-dominated Sorting Genetic Algorithm-II; Shape optimization; ultimate strength of ship stiffened panel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2014 10th International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4799-5150-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2014.6975810
  • Filename
    6975810