Research on the hull form optimization using the surrogate models

Shenglong Zhang, Tahsin Tezdogan, Baoji Zhang, Ling Li

Research output: Contribution to journalArticlepeer-review

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Abstract

The ship hull form optimization using the Computational Fluid Dynamics (CFD) method is increasingly employed in the early design of a ship, as an optimal ship hull form can obtain good hydrodynamics. However, it is time-consuming due to its many CFD simulations for the optimization. This paper presents a ship hull form optimization loop using the surrogate model, deep belief network (DBN), to reduce the wave-making resistance of the Wigley ship. The prediction performance of the wave-making resistance of the Wigley ship using the DBN method is discussed and compared with the traditional surrogate models found in this study. The results show that the resistance obtained using the deep belief network algorithm is superior to that obtained using the typical surrogate models. Then, a ship hull form optimization framework is built by integrating the Free From Deformation, non-linear programming by quadratic Lagrangian and deep belief network algorithms. The optimization results show that the deep belief network-based ship hull form optimization loop can be used to optimize the Wigley ship. The study presented in this paper could provide a deep learning algorithm for the ship design optimization.

Original languageEnglish
Pages (from-to)747-761
Number of pages15
JournalEngineering Applications of Computational Fluid Mechanics
Volume15
Issue number1
DOIs
Publication statusPublished - 3 May 2021

Keywords

  • ship hull form optimisaiton
  • surrogate model
  • wave making resistance
  • deep belief network

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