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.

本提案的前传重点分析了三维网状实体（例如，**小梁骨）的拓扑蓝图。拓扑学（希腊语，研究地点）与形态学（研究形状）相对，指的是一个整体的组成部分相互联系的方式，而不考虑它们的大小、形状和规模。例如，河床和分支血管的拓扑结构是相似的；自行车车轮和方向盘的拓扑结构是不同的。拓扑图是结构力学性能、性能和用途的基础。在小梁骨的情况下，它在优化骨强度方面起着关键作用，同时最大限度地减少其重量并降低其对生物体的维护成本。在人造工程结构中，拓扑很少被考虑在内，但次优拓扑的影响可以通过使用昂贵的材料、更大量的材料、或更复杂的结构内元素组合来部分补偿。根据从小梁骨拓扑研究中吸取的经验教训，该项目使用基于仿生拓扑的设计来生成轻量化和坚固的结构，这些结构可以按比例缩放到任何尺寸，并用于各种工程应用，从假肢医疗设备到土木工程。正如我们从小梁骨中了解到的那样，它的自然、功能驱动的拓扑优化是缓慢和迭代的。在这里，我们建议在人工智能（深度学习-基于神经网络的算法）的帮助下，加速和细化结构化人工结构的拓扑优化过程，其中基本原理借鉴了骨生物学。这是一个经过数百万年的进化而完善的高效结构的逆向工程的例子。