Topological optimization of porous solids

多孔固体的拓扑优化

• 批准号: 537121-2018
• 负责人: McKee, Marc
• 金额: $ 0.91万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659325
• 关键词: Topological; optimization; porous; solids

The prequel of this proposal focused on the analysis of the topological blueprint of 3D reticulated solids (e.g.,**trabecular bone). Topology (a study of place, Greek) as opposed to morphology (a study of shape), refers to the**way in which the components of a whole are related to each other, regardless of their size, shape and scale. For**example, the topologies of a river bed and of a branching blood vessel are similar; the topologies of a bicycle**wheel and of a steering wheel are different. Topological blueprint is fundamental for mechanical properties,**behavior and purpose of the structure. In the case of trabecular bone, it plays a critical role in optimizing bone**strength while at the same time minimizing its weight and reducing its cost of maintenance for the organism.**Among human-made engineered structures, topology is rarely taken into account, but the implications of**suboptimal topology can be partially compensated for by using expensive materials, larger amounts of material,**or more sophisticated assemblages of the elements within the structure. Following lessons learned from the**study of trabecular bone topology, this project uses biomimetic topology-based design for generation of**light-weight and strong structures that can be scaled to any size and manufactured for a variety of engineering**applications, from prosthetic medical devices to civil engineering. As we learned from trabecular bone, its**natural, function-driven topological optimization is slow and iterative. Here, we suggest to accelerate and**refine the process of topological optimization of architected artificial structures with the aid of artificial**intelligence (deep learning - neural network-based algorithm) where basic principles are borrowed from bone**biology. This is an example of reverse-engineering of a highly effective structure that has been refined by**millions of years of evolution.

该提案的前传重点是分析3D网状固体的拓扑蓝图（例如**小梁骨）。与形态学（形状的研究）相反，拓扑（对地方，希腊语）是指整体组成部分相互关联的**方式，无论其大小，形状和规模如何。例如，例如，河床和分支血管的拓扑相似。自行车**车轮和方向盘的拓扑不同。拓扑蓝图是机械性能，**的行为和目的的基础。在小梁骨的情况下，它在优化骨骼**强度方面起着至关重要的作用，同时最大程度地减少其重量并降低其对生物体的维护成本。从小梁骨拓扑的**研究中学到的经验教训后，该项目使用基于仿生拓扑的设计来生成**轻巧和强大的结构，这些结构可以缩放到任何规模，并为各种工程**应用，从假肢医疗设备到土木工程。正如我们从小梁骨中学到的那样，它的自然，功能驱动的拓扑优化是缓慢且迭代的。在这里，我们建议借助人工**智能（深度学习 - 基于神经网络的算法）加速和**完善构建的人工结构的拓扑优化过程，其中基本原理是从骨**生物学借用的。这是一个高效结构的反向工程的一个例子，该结构已通过**数百万年的进化来完善。