当前位置: X-MOL 学术Struct. Multidisc. Optim. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Multiscale structural optimization with concurrent coupling between scales
Structural and Multidisciplinary Optimization ( IF 3.9 ) Pub Date : 2021-01-08 , DOI: 10.1007/s00158-020-02773-3
Ryan Murphy , Chikwesiri Imediegwu , Robert Hewson , Matthew Santer

A robust three-dimensional multiscale structural optimization framework with concurrent coupling between scales is presented. Concurrent coupling ensures that only the microscale data required to evaluate the macroscale model during each iteration of optimization is collected and results in considerable computational savings. This represents the principal novelty of this framework and permits a previously intractable number of design variables to be used in the parametrization of the microscale geometry, which in turn enables accessibility to a greater range of extremal point properties during optimization. Additionally, the microscale data collected during optimization is stored in a reusable database, further reducing the computational expense of optimization. Application of this methodology enables structures with precise functionally graded mechanical properties over two scales to be derived, which satisfy one or multiple functional objectives. Two classical compliance minimization problems are solved within this paper and benchmarked against a Solid Isotropic Material with Penalization (SIMP)–based topology optimization. Only a small fraction of the microstructure database is required to derive the optimized multiscale solutions, which demonstrates a significant reduction in the computational expense of optimization in comparison to contemporary sequential frameworks. In addition, both cases demonstrate a significant reduction in the compliance functional in comparison to the equivalent SIMP-based optimizations.



中文翻译:

尺度之间并发耦合的多尺度结构优化

提出了一种鲁棒的三维多尺度结构优化框架,其中尺度之间并发耦合。并发耦合确保仅收集在每次优化迭代期间评估宏观模型所需的微观数据,并节省可观的计算量。这代表了该框架的主要新颖性,并允许在微尺度几何结构的参数化中使用以前难以处理的数量的设计变量,从而可以在优化过程中访问更大范围的极点特性。另外,优化过程中收集的微尺度数据存储在可重复使用的数据库中,从而进一步减少了优化的计算费用。这种方法的应用使得可以得出在两个尺度上具有精确的功能梯度机械性能的结构,这些结构可以满足一个或多个功能目标。在本文中解决了两个经典的合规性最小化问题,并以基于惩罚性的固体各向同性材料(SIMP)为基础的拓扑优化为基准。只需一小部分微结构数据库即可得出优化的多尺度解决方案,这表明与现代顺序框架相比,优化的计算量大大减少了。此外,与基于SIMP的等效优化相比,这两种情况均表明合规功能显着降低。满足一个或多个功能目标。在本文中解决了两个经典的合规性最小化问题,并以基于惩罚性的固体各向同性材料(SIMP)为基础的拓扑优化为基准。只需一小部分微结构数据库即可得出优化的多尺度解决方案,这表明与现代顺序框架相比,优化的计算量大大减少了。此外,与基于SIMP的等效优化相比,这两种情况均表明合规功能显着降低。满足一个或多个功能目标。在本文中解决了两个经典的合规性最小化问题,并以基于惩罚性的固体各向同性材料(SIMP)为基础的拓扑优化为基准。只需一小部分微结构数据库即可得出优化的多尺度解决方案,这表明与现代顺序框架相比,优化的计算量大大减少了。此外,与基于SIMP的等效优化相比,这两种情况均表明合规功能显着降低。只需一小部分微结构数据库即可得出优化的多尺度解决方案,这表明与现代顺序框架相比,优化的计算量大大减少了。此外,与基于SIMP的等效优化相比,这两种情况均表明合规功能显着降低。只需一小部分微结构数据库即可得出优化的多尺度解决方案,这表明与现代顺序框架相比,优化的计算量大大减少了。此外,与基于SIMP的等效优化相比,这两种情况均表明合规功能显着降低。

更新日期:2021-01-08
down
wechat
bug