Abstract
In this paper, several accelerating strategies for the numerical methods of topology optimization are proposed. In our implementation, the finite element method is used for discretization, and the novelty of this research lies in two aspects. Two different meshes with variable element sizes are used to discretize the state equation and the level-set evolution equation. On the other hand, GPU-based accelerating schemes are implemented both for finite element assembler and sparse linear solver. Such combination is shown numerically to be effective to reduce the computational costs. Numerical illustrations are presented on several benchmark problems for topology optimization, such as the Cantilever and MBB problems. Compared with the traditional finite element calculations, our GPU-based two-grid schemes run up to 20 times faster without losing numerical accuracy.
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