Neumann-Michell theory-based multi-objective optimization of hull form for a naval surface combatant

被引：31

作者：

Wu, Jianwei ^{[1
]}

Liu, Xiaoyi ^{[1
]}

Zhao, Min ^{[1
]}

Wan, Decheng ^{[1
]}

机构：

[1] Shanghai Jiao Tong Univ, Collaborat Innovat Ctr Adv Ship & Deep Sea Explor, Sch Naval Architecture Ocean & Civil Engn, State Key Lab Ocean Engn, Shanghai 200240, Peoples R China

来源：

APPLIED OCEAN RESEARCH | 2017年 / 63卷

基金：

高等学校博士学科点专项科研基金; 中国国家自然科学基金;

关键词：

OPTShip-SJTU; Multi-objective optimization; NSGA-II algorithm; Neumann-Michell theory; Wave drag; DESIGN OPTIMIZATION; SHAPE OPTIMIZATION; SHIP; RESISTANCE; WAVES;

D O I：

10.1016/j.apor.2017.01.007

中图分类号：

P75 [海洋工程];

学科分类号：

0814 ; 081505 ; 0824 ; 082401 ;

摘要：

A numerical multi-objective optimization procedure is proposed here to describe the development and application of a practical hydrodynamic optimization tool, OPTShip-SJTU. Three components including hull form modification module, hydrodynamic performance evaluation module and optimization module consist of this tool. The free-form deformation (FFD) method and shifting method are utilized as parametric hull surface modification techniques to generate a series of realistic hull forms subjected to geometric constraints, and the Neumann-Michell (NM) theory is implemented to predict the wave drag. Moreover, NSGA-II, a muti-objective genetic algorithm, is adopted to produce pareto-optimal front, and kriging model is used for predicting the total resistance during the optimization process to reduce the computational cost. Additionally, the analysis of variance (ANOVA) method is introduced to represent the influence of each design variable on the objective functions. In present work, a surface combatant DTMB Model 5415 is used as the initial design, and optimal solutions with obvious drag reductions at specific speeds are obtained. Eventually, three of optimal hulls are analyzed by NM theory and a RANS-based CFD solver naoe-FOAM-SJTU respectively. Numerical results confirm the availability and reliability of this multi-objective optimization tool. (C) 2017 Elsevier Ltd. All rights reserved.

引用

页码：129 / 141

页数：13

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