基于UNet深度学习的VLCC横框架拓扑优化分析  

Topology optimization analysis of VLCC transverse web based on UNet deep learning

作　　者：李振荣 夏利娟[1,2] 冯朔 LI Zhenrong;XIA Lijuan;FENG Shuo(State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Ocean and Civil Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学海洋工程全国重点实验室,上海200240 [2]上海交通大学船舶海洋与建筑工程学院,上海200240

出　　处：《中国舰船研究》2024年第6期108-116,共9页Chinese Journal of Ship Research

摘　　要：[目的]为将人工智能技术应用于复杂船舶结构优化设计,提出一种基于UNet的船体横框架拓扑优化方法。[方法]以某超大型油轮(VLCC)横框架为研究对象,首先根据优化数学原理创建UNet拓扑优化代理模型,然后将有限元网格物理量映射为张量,获得供模型训练的数据集,最后采用交并比(IoU)方法对训练结果进行评估,并将该方法与SIMP法进行拓扑构型对比。[结果]结果显示,所提拓扑优化方法能够快速输出设计域的材料布局,与SIMP拓扑优化相比可以更加高效地获得结构拓扑构型。[结论]所提拓扑优化方法可为船舶横框架结构提供一种新型的设计手段。[Objective]This paper proposes a hull transverse web topology optimization method based on UNet for application in the optimization design of complex ship structures.[Methods]Taking the transverse web of a very large crude carrier(VLCC) as the research object,a UNet topology optimization surrogate model is first created according to optimization mathematical principles.The finite element grid physical quantity is then mapped to the tensor to obtain the dataset for model training.Finally,the intersection over union(IoU) method is used to evaluate the training results,and the method is compared with the solid isotropic material with penalization(SIMP) method in terms of topology configuration.[Results]The results show that this method can quickly output the material layout of the design domain,and compared with SIMP topology optimization,it can obtain the topology configuration more efficiently.[Conclusion]The proposed topology optimization method can provide a new design method for ship transverse web structures.

关 键 词：船舶设计 人工智能 形状优化 拓扑优化 深度学习 UNet 代理模型 数据映射 船舶横框架 

分 类 号：U663.4[交通运输工程—船舶及航道工程]