Research on the hull form optimisation using the surrogate models

Zhang, Shenglong and Tezdogan, Tahsin and Zhang, Baoji and Li, Ling (2021) Research on the hull form optimisation using the surrogate models. Engineering Applications of Computational Fluid Mechanics, 15 (1). pp. 747-761. ISSN 1994-2060

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    Abstract

    The ship hull form optimisation 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 optimisation. This paper presents a ship hull form optimisation 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 deep belief network 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 optimisation framework is built by integrating the Free From Deformation (FFD), non-linear programming by quadratic Lagrangian (NLPQL) and deep belief network algorithms. The optimisation results show that the deep belief network-based ship hull form optimisation loop can be used to optimise the Wigley ship. The study presented in this paper could provide a deep learning algorithm for the ship design optimisation.

    ORCID iDs

    Zhang, Shenglong, Tezdogan, Tahsin ORCID logoORCID: https://orcid.org/0000-0002-7032-3038, Zhang, Baoji and Li, Ling;