摘要
拓扑优化设计可为现代工程提供具有卓越热学、力学及声学等多学科性能的创新型结构,移动变形组件法(Moving Morphable Components,MMC)以其独特的显示拓扑优化方法备受青睐,MMC法通过一系列可移动变形的显示组件间的移动、变形、重叠实现边界演化以完成结构优化的目的。该文利用椭圆形初始组件替代原有直线型骨架厚度二次变化组件进行拓扑优化。在减少设计变量次数的同时,可缩短一定的计算时间,但在实际计算中,随着初始组件单元数增加,中间迭代计算过程时间依旧相对较多,为精确实时获取拓扑优化构型,该文引入Pyramid-Attention-U-Net(PA-U-Net)深度学习模型加速优化设计,避免中间迭代计算过程。研究结果表明:该方法不仅在可忽略的计算时间内准确实时获取不同参数下的初始组件拓扑构型,而且准确率可达90.89%,高于其他深度学习网络模型。同时这种将深度学习与拓扑优化方法有机结合的形式在大型工程结构优化设计中具有广阔的应用前景。
Topology optimization design provides innovative structures with excellent thermal,mechanical and acoustic performance for modern engineering structures.Moving morphable components(MMC)have gained popularity due to their unique display topology optimization.MMC achieves boundary evolution through migration and superposition of a series of moving morphable display components,resulting in structural optimization.Here instead of the original linear skeleton components with secondary thickness changes,oval initial components are used for topology optimization,which not only reduces the complexity of the variable design but also shows a better fitting effect of boundary evolution,resulting in reduced computation time.However,as the number of initial components increases,the intermediate iterative computation becomes increasingly time-consuming,furthermore,initial components of different parameters generate large differences in the topological configuration.To obtain the optimal topology in an accurate and real-time manner,Pyramid-Attention-U-Net(PA-U-Net)deep learning model is proposed to improve the optimization design and to avoid intermediate iterative computations.According to the results,the method can not only obtain the optimal topology of the initial components under various parameter conditions in an accurate and timely manner in a negligible computing time,but it can also achieve an accuracy of 90.89%,which is better than other deep learning network models.Meanwhile,in the optimization design of large engineering structures,the organic combination of deep learning with topology optimization has many prospects.
作者
史冬岩
王立夫
张博洋
李光亮
SHI Dong-yan;WANG Li-fu;ZHANG Bo-yang;LI Guang-liang(College of Mechanical and Electrical Engineering,Harbin Engineering University,Harbin 150001,China;College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《工程力学》
EI
CSCD
北大核心
2024年第11期217-224,共8页
Engineering Mechanics
基金
国家自然科学基金项目(12172100)。
