期刊
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 61, 期 1, 页码 19-38出版社
SPRINGER
DOI: 10.1007/s00158-019-02440-2
关键词
Topology optimization; Large scale computing; Sparse data structures; VDB; Level set method
资金
- DARPA [HR0011-16-2-0032]
- EPSRC [EP/M002322/1, EP/M002322/2, EP/J014389/1] Funding Source: UKRI
We present a numerical study of a new large-scale level set topology optimization (LSTO) method for engineering design. Large-scale LSTO suffers from challenges in both slow convergence and high memory consumption. We address these shortcomings by adopting the spatially adaptive and temporally dynamic Volumetric Dynamic B+ (VDB) tree data structure, open sourced as OpenVDB, which is tailored to minimize the computational cost and memory footprint by not carrying high fidelity data outside the narrow band. This enables an efficient level set topology optimization method and it is demonstrated on common types of heat conduction and structural design problems. A domain decomposition-based finite element method is used to compute the sensitivities. We implemented a typical state-of-the-art LSTO algorithm based on a dense grid data structure and used it as the reference for comparison. Our studies demonstrate the level set operations in the VDB algorithm to be up to an order of magnitude faster.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据