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.

在船舶的早期设计中，越来越多地使用计算流体动力学（CFD）方法进行船体形式优化，因为最佳的船体形式可以获得良好的流体动力学。但是，由于要进行优化的许多CFD模拟，因此非常耗时。本文提出了一种使用替代模型深度信念网络（DBN）的船体形式优化回路，以降低Wigley船的兴波阻力。讨论了使用DBN方法对Wigley船的造波阻力的预测性能，并将其与本研究中发现的传统替代模型进行了比较。结果表明，使用深度置信网络算法获得的抵抗力优于使用典型替代模型获得的抵抗力。然后，通过整合自由变形，二次拉格朗日非线性编程和深度置信网络算法，构建了船体形式优化框架。优化结果表明，基于深度信念网络的船体形式优化循环可用于优化Wigley船。本文提出的研究可以为船舶设计优化提供一种深度学习算法。