Chinese Journal of Ship Research, Volume. 19, Issue 6, 64(2024)

Hierarchical space reduction method based on self-organizing maps and K-means clustering for hull form optimization

Qun YU1...2, Peng LI3, Qiang ZHENG1,2, Baiwei FENG1,2, Chunliang QIU4 and Dalian ZENG4 |Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
  • 2School of Shipbuilding, Marine Engineering, Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China
  • 3China Ship Development and Design Center, Wuhan 430064, China
  • 4Guangxi CSSC Beibu Gulf Shipbuilding and Marine Engineering Design Co., Ltd., Nanning 530028, China
  • show less
    Figures & Tables(24)
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    [in Chinese]
    • Table 1. Principal parameters for a 7 500 t bulk carrier

      View table
      View in Article

      Table 1. Principal parameters for a 7 500 t bulk carrier

      设计水线长Lwl/m垂线间长Lpp/m船宽B/m型深D/m设计吃水T/m方形系数CB
      13012816.27.25.20.86
    • Table 2. Feature parameters for modelling

      View table
      View in Article

      Table 2. Feature parameters for modelling

      参数含义下限初始值上限
      L/B船长船宽比7.8957.97.905
      B/T船宽吃水比3.113.1153.12
      CB方形系数0.860.8650.87
      Xcb浮心纵向位置−0.030.0140.03
      rarea艏部舭部肥大度0.740.8120.86
      rydwl艏部宽度收缩比0.80.9311
      binskegy尾鳍内侧肥大度0.40.5250.7
      b/B尾轴间距比0.40.4440.6
      h/T艉封板高度吃水比0.250.2980.4
    • Table 3. Prediction errors of approximate model

      View table
      View in Article

      Table 3. Prediction errors of approximate model

      eMSEeRMSEeMAPE/%
      0.152 230.390 1673.31
    • Table 4. Comparison of total drag coefficients

      View table
      View in Article

      Table 4. Comparison of total drag coefficients

      母型船PSOHSRM
      总阻力系数2.000×10−31.970×10−31.954×10−3
      变化率(降低)/%1.8542.266
    • Table 5. Comparison of optimization parameters

      View table
      View in Article

      Table 5. Comparison of optimization parameters

      母型船PSOHSRM
      L/B7.900 0007.895 8487.899 179
      B/T3.115 0003.115 6083.116 647
      CB0.865 0000.863 2080.862 775
      Xcb0.014 0000.006 3090.005 675
      rarea0.812 0000.777 6040.794 422
      rydwl0.931 0000.843 1100.828 644
      binskegy0.525 0000.515 8360.564 904
      b/B0.444 0000.478 5550.483 055
      h/T0.298 0000.361 7530.358 558
    • Table 6. Comparison of the performance parameters.

      View table
      View in Article

      Table 6. Comparison of the performance parameters.

      $ {{\nabla}} $/m3变化率/%Lcb/m变化率/%Swet/m2变化率/%Ct变化率/%
      母型船0.976 13.0846.9922.000
      PSO0.976 50.0413.038−1.4866.967−0.3531.963−1.854
      HSRM0.973 2−0.3013.034−1.6096.964−0.4061.954−2.266
    • Table 7. Range of subspaces

      View table
      View in Article

      Table 7. Range of subspaces

      变量原始空间重要子空间潜在子空间
      下限上限下限上限缩减范围/%下限上限缩减范围/%
      L/B7.8957.9057.897 6817.903 24956.687.897 2557.903 24959.94
      B/T3.113.123.112 9083.117 50946.013.112 7093.118 07453.65
      CB0.860.870.861 1650.863 45922.940.861 1650.867 25960.94
      Xcb−0.030.03−0.010 9720.006 21628.65−0.019 4900.017 92862.36
      rarea0.740.860.788 3430.823 86229.600.761 7460.835 77061.69
      rydwl0.81.00.827 6990.920 12746.210.800 0000.928 39164.20
      binskegy0.40.70.491 7630.623 56743.930.489 1470.671 17960.68
      b/B0.40.60.469 0640.536 96933.950.428 1880.538 46655.14
      h/T0.250.400.345 2520.361 37210.750.278 8400.361 37255.02
    Tools

    Get Citation

    Copy Citation Text

    Qun YU, Peng LI, Qiang ZHENG, Baiwei FENG, Chunliang QIU, Dalian ZENG. Hierarchical space reduction method based on self-organizing maps and K-means clustering for hull form optimization[J]. Chinese Journal of Ship Research, 2024, 19(6): 64

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Theory and Method of Intelligent Design for Ship and Ocean Engineering

    Received: Jul. 4, 2024

    Accepted: --

    Published Online: Mar. 14, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.04050

    Topics