本文以某集装箱船为研究对象，对降速航行后的球鼻首进行优化。采用Catia建立船体三维模型，为了产生不同形状的球鼻首，选取球鼻特征参数来描述其基本结构；采用拉丁超立方试验抽样方法得到12组不同形状的球鼻首，提出运用非线性拟合能力较强的BP网络构建球鼻首参数和阻力系数之间的关系模型；采用遗传算法对训练后的网络进行极值寻优。结果显示，优化船型的阻力系数显著降低，说明该方法对球鼻首的优化有一定的借鉴意义。
This paper selects a container ship as research target, and optimization work of bulbous bow when the ship slows down its speed is carried out. Main work of this paper can be summarized as follows. Firstly, three-dimensional ship model is established using the software of Catia, and in order to produce different bulbous bow shapes, four characteristic parameters is selected to describe its basic structure. Secondly, twelve groups of bulbous bow is obtained using Latin hypercube sampling method. Then, the BP network with high nonlinear fitting capability is adopted to establish the relation between characteristic parameters of bulbous bow and resistance coefficient. Finally, a genetic algorithm is used to find the optimal solution of the network, the result shows that resistance coefficient of the optimal individual has been decreased notably, which indicates that this method can be used to optimize ship's bulbous bow.
2021,43(3): 37-40 收稿日期：2020-04-02
DOI：10.3404/j.issn.1672-7649.2021.03.008
分类号：U661.3
作者简介：高现娇(1989-)，女，硕士研究生，研究方向为船舶水动力性能
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