Sparse Stress Structures from Optimal Geometric Measures

被引:1
|
作者
Rowe, Dylan [1 ]
Chern, Albert [1 ]
机构
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
关键词
Topology optimization; tensegrity; geometric measure theory; varifolds; compressive sensing;
D O I
10.1145/3610548.3618193
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
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收藏
页数:9
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