| 低温柔性管道关键结构层的多目标优化设计 |
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| 引用本文： | 英玺蓬,曹慧鑫,杨志勋,杨亮,刘淼儿,范嘉堃,李方遒,阎军.低温柔性管道关键结构层的多目标优化设计[J].海洋工程,2022,40(6):142-151. |
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| 作者姓名： | 英玺蓬 曹慧鑫 杨志勋 杨亮 刘淼儿 范嘉堃 李方遒 阎军 |
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| 作者单位： | 大连理工大学 工程力学系 工业装备结构分析国家重点实验室, 辽宁 大连 116024;哈尔滨工程大学 机电工程学院, 黑龙江 哈尔滨 150001;中海石油气电集团技术研发中心, 北京 100028 |
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| 基金项目： | 国家自然科学基金资助项目(U1906233，11732004，52001088)；山东省重点研发计划项目(2019JZZY010801)；工业装备结构分析国家重点实验室开发基金资助项目(GZ20105) |
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| 摘 要： | 低温柔性管道是海上浮式液化天然气装置系统(FLNG)的核心输运装备。针对低温柔性管道多材料、多层复合的结构设计难点，将其按照不同功能解耦成内衬层、抗拉铠装层以及辅助层三个关键结构层。基于神经网络模型(RBF)、Kriging模型以及响应面模型(RSM)三种建模方法建立了上述三个结构层响应分析的代理模型，并通过模型准确度的比较，发现RBF的误差均最小。在优化设计中，基于遗传算法分别对低温柔性管道上述关键结构层进行多目标优化设计。在内衬层结构的优化中，以质量及弯曲刚度最小为优化目标；在抗拉铠装层结构的优化中，以质量最小及拉伸刚度最大为优化目标；在辅助层结构的优化中，以质量、弯曲刚度及传热速率最小为优化目标。研究工作为低温柔性管道的结构提供了关键设计参数及理性的设计方法。
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| 关 键 词： | 低温柔性管道 关键结构层 数值模拟 遗传算法 代理模型 多目标优化设计 Pareto优化解集 |
| 收稿时间： | 2022/1/27 0:00:00 |
Multi-objective optimization design of key structural layers of cryogenic hose |
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| YING Xipeng,CAO Huixin,YANG Zhixun,YANG Liang,LIU Miaoer,FAN Jiakun,LI Fangqiu,YAN Jun.Multi-objective optimization design of key structural layers of cryogenic hose[J].Ocean Engineering,2022,40(6):142-151. |
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| Authors: | YING Xipeng CAO Huixin YANG Zhixun YANG Liang LIU Miaoer FAN Jiakun LI Fangqiu YAN Jun |
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| Institution: | Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China;College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China;Research and Development Center, CNOOC Gas & Power Group Co., Ltd., Beijing 100028, China |
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| Abstract: | Cryogenic hose is the core equipment of offshore floating liquefied natural gas (FLNG) installation system. In this paper, according to the structural characteristics of multi-material and multi-layer, the structure of cryogenic hose is decoupled into lining layer structure, armored layer structure and auxiliary layer structure. Based on neural network (RBF), Kriging modeling and response surface (RSM) methods, the surrogate models are established and the accuracy of different models is compared. It is found that the error of RBF modeling method is the least. In the optimization design, based on genetic algorithm, multi-objective optimization design of three different functional structure layers of cryogenic hose is carried out. In the optimization of lining layer, the minimum mass and minimum bending stiffness are the optimization objectives. In the optimization of armor layer, the minimum mass and maximum tensile stiffness are the optimization objectives. In the optimization of auxiliary layers, the optimization objectives are to minimize the mass, bending stiffness and heat transfer rate. The key design parameters and rational design methods are provided for the structure of cryogenic hose. |
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| Keywords: | cryogenic hose key structural layers numerical simulation genetic algorithm surrogate model multi-objective optimization design Pareto optimization solution set |
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