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<p>The objective of this study is to develop an innovative and efficient methodology for optimizing ship hull to minimize resistance and enhance hydrodynamic performance. High-fidelity numerical simulations were conducted on the KCS hull to predict still water resistance. These simulation results were verified with experimental results, which yielded results that exhibited strong consistency. In order to overcome the limitations of traditional optimization methods, the Dynamic Weight and Perturbation Particle Swarm Optimization (DWP-PSO) algorithm was developed. The performance of the algorithm was enhanced by the introduction of the dynamic adjustment of the inertial weight <i>w</i> and the global perturbation term <i>η</i>, thereby achieving effective balance between diversity and convergence. A series of comparative tests were performed on three widely recognized standard functions, the purpose of which was to verify that it was consistently superior to the standard PSO algorithm in terms of convergence speed and solution quality. Meanwhile, an efficient automatic hull optimization system was constructed based on the DWP-PSO algorithm. The integration of the simulation method, parametric modeling, and intelligent optimization into a unified framework enabled the systematic, precise, and efficient exploration of the optimal hull. The KCS hull was optimized through this system and a new hull form with significantly reduced resistance performance was obtained. The results not only demonstrated the effectiveness of the DWP-PSO algorithm in solving complex hydrodynamic optimization problems but also proved the practicability and extensibility of the proposed optimization system. This study provides a useful reference for applying advanced computational technologies to promote sustainable and efficient marine engineering practices.</p>

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Minimum resistance hull optimization research based on simulation-based design optimization method

  • 期刊论文

  • 发表日期：

    2025年7月21日

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  • Jie Liu,
  • Bao-ji Zhang,
  • Shu-hui Guo,
  • Zheng Tian,
  • Li-fen Hu,
  • Ying-kai Dong

摘要

The objective of this study is to develop an innovative and efficient methodology for optimizing ship hull to minimize resistance and enhance hydrodynamic performance. High-fidelity numerical simulations were conducted on the KCS hull to predict still water resistance. These simulation results were verified with experimental results, which yielded results that exhibited strong consistency. In order to overcome the limitations of traditional optimization methods, the Dynamic Weight and Perturbation Particle Swarm Optimization (DWP-PSO) algorithm was developed. The performance of the algorithm was enhanced by the introduction of the dynamic adjustment of the inertial weight w and the global perturbation term η, thereby achieving effective balance between diversity and convergence. A series of comparative tests were performed on three widely recognized standard functions, the purpose of which was to verify that it was consistently superior to the standard PSO algorithm in terms of convergence speed and solution quality. Meanwhile, an efficient automatic hull optimization system was constructed based on the DWP-PSO algorithm. The integration of the simulation method, parametric modeling, and intelligent optimization into a unified framework enabled the systematic, precise, and efficient exploration of the optimal hull. The KCS hull was optimized through this system and a new hull form with significantly reduced resistance performance was obtained. The results not only demonstrated the effectiveness of the DWP-PSO algorithm in solving complex hydrodynamic optimization problems but also proved the practicability and extensibility of the proposed optimization system. This study provides a useful reference for applying advanced computational technologies to promote sustainable and efficient marine engineering practices.