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规则波中船舶波浪增阻理论和经验法预报分析与比较
引用本文:杨春蕾,朱仁传,莫中华,洪亮,缪国平.规则波中船舶波浪增阻理论和经验法预报分析与比较[J].船舶力学,2017(z1).
作者姓名:杨春蕾  朱仁传  莫中华  洪亮  缪国平
作者单位:南通中远川崎船舶工程有限公司,江苏南通226005;上海交通大学 船舶海洋与建筑工程学院, 海洋工程国家重点实验室, 高新船舶与深海开发装备协同创新中心, 上海 200240;上海交通大学 船舶海洋与建筑工程学院, 海洋工程国家重点实验室, 高新船舶与深海开发装备协同创新中心, 上海 200240;南通中远川崎船舶工程有限公司,江苏南通,226005
摘    要:针对规则波中迎浪下有航速集装箱船船型阻力增加预报,采用一种改进的三维移动脉动源面元法求解船舶运动,进而通过理论方法求解辐射和绕射增阻.速度势求解引入了格林函数空间线段积分和面元积分方法,辐射增阻采用三维辐射能量法计算,绕射增阻通过二阶波浪力中绕射速度势获得.通过与试验数据对比证明了该理论预报模型在精度和效率方面有明显优势.在此预报结果基础上对经验预报模型提出了改进公式,计算结果表明了改进模型对工程设计具有较好的适用性.

关 键 词:船舶阻力增加  格林函数空间线段积分  经验预报模型

Analysis and comparison of the prediction of the added resistance for ships in regular waves based on theoretical and empirical methods
YANG Chunlei,ZHU Renchuan,MO Zhonghua,Hong Liang,MIAO Guoping.Analysis and comparison of the prediction of the added resistance for ships in regular waves based on theoretical and empirical methods[J].Journal of Ship Mechanics,2017(z1).
Authors:YANG Chunlei  ZHU Renchuan  MO Zhonghua  Hong Liang  MIAO Guoping
Abstract:Aiming to predict the added resistance of container ships advancing and heading in regular waves, an improved panel method based on three-dimensional translating pulsating source is adopted for ship motions, and then the added resistance radiation and diffraction are computed by theoretical method. Green function integration on spatial lines and panel integration methods are introduced for the velocity potential solution, three-dimensionalradiation energy method is adopted for theradiation resistance, and the second order diffraction potential is used for diffraction resistance. The comparison analysis between computed and experimental results shows that the theoretical method predicts added resistance with better accuracy and efficiency. Furthermore, improved empirical formulas based on existing empirical models are developed,and computed results of the improved model prove that the model is applicable to the engineering design.
Keywords:added resistance of a ship  Green function integration expression on spatial lines  empirical prediction models
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