Sparse Stress Structures from Optimal Geometric Measures

Sparse Stress Structures from Optimal Geometric Measures
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最佳几何测量的稀疏应力结构

DOI:
10.1145/3610548.3618193
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发表时间:
2023
期刊:
SA '23: SIGGRAPH Asia 2023 Conference Papers
影响因子:
--
通讯作者:
Chern, Albert
Chern, Albert
中科院分区:
--
文献类型:
--
作者:
Rowe, Dylan;Chern, Albert

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在给定载荷和约束条件下确定最优结构设计是拓扑优化和形状优化的主要挑战。我们提出了一种新的方法来解决这个问题,找到一个最小的张拉整体结构,一个网络的电缆和支柱在平衡与给定的加载力。通过几何测度理论和压缩感知技术的应用,我们表明,这个看似困难的图论问题可以减少到一个数值上易于处理的连续优化问题。通过仅涉及快速傅里叶变换和局部代数计算的轻量级迭代算法,我们可以生成稀疏的支撑结构,其中包括尊重规定载荷力和障碍物的详细树枝、拱门和加固结构。
Identifying optimal structural designs given loads and constraints is a primary challenge in topology optimization and shape optimization. We propose a novel approach to this problem by finding a minimal tensegrity structure—a network of cables and struts in equilibrium with a given loading force. Through the application of geometric measure theory and compressive sensing techniques, we show that this seemingly difficult graph-theoretic problem can be reduced to a numerically tractable continuous optimization problem. With a light-weight iterative algorithm involving only Fast Fourier Transforms and local algebraic computations, we can generate sparse supporting structures featuring detailed branches, arches, and reinforcement structures that respect the prescribed loading forces and obstacles.
撤回通知最佳传输路径的数值模拟
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