移动节点法显式拓扑optimization：梁单元识别
Explicit topology optimization through moving node approach: beam elements recognition
摘要：
结构优化（拓扑、形状、尺寸）是促进结构设计中新概念出现的重要工具。通常，拓扑优化在设计的早期阶段进行，然后依次进行形状和尺寸设计。与传统的拓扑最优化方法不同，显式方法由于其在处理低阶设计变量的同时，缩短了CAD/CAE过程的成本，而引起了人们的极大关注。本文的目的是提出一个适应的流动启发的方法，所谓的移动节点的方法（MNA）的拓扑优化。在这种方法中，离散化是解耦的材料分布和最终目标是识别最佳的梁组件，同时最大限度地减少遵守。这里的范例已经改变，并使用新的设计变量，如节点位置，元素长度/方向和大小，提供比基于像素的设计变量更少的设计变量。该方法被验证使用2个经典的测试用例在拓扑优化领域：悬臂梁和L形问题。
英文摘要：
Structural optimization (topology, shapes, sizing) is an important tool for facilitating the emergence of new concepts in structural design. Normally, topology optimization is carried out at the early stage of design and then shape and sizing design are performed sequentially. Unlike traditional topology optimization method, explicit methodologies have attracted a great deal of attention because of the advantages of shortcuting the costly CAD/CAE processes while dealing with low order number of design variables compared to implicit method (such as SIMP). This paper aims at presenting an adaptation of a flow-inspired approach so-called Moving Node Approach (MNA) in topology optimization. In this approach, the discretization is decoupled from the material distribution and the final objective is to recognize the best beam assembly while minimizing compliance. The paradigm has here changed and new design variables are used such as nodes location, elements length/orientation and size providing a lower number of design variables than pixels-based. The methodology is validated using 2 classical testcases in the field of Topology Optimization: the Cantilever beam and the L-Shape problem.
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