4.7 Article

TopADD: a 2D/3D integrated topology optimization parallel-computing framework for arbitrary design domains

期刊

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 64, 期 3, 页码 1701-1723

出版社

SPRINGER
DOI: 10.1007/s00158-021-02917-z

关键词

2D; 3D topology optimization; Additive manufacturing; Arbitrary design domain; Voxelization; Parallel computing

资金

  1. Natural Sciences and Engineering Research Council of Canada (NSERC)
  2. Federal Economic Development Agency for Southern Ontario (FedDev Ontario)
  3. Siemens Energy Canada Limited

向作者/读者索取更多资源

In this work, a novel two-dimensional and three-dimensional integrated topology optimization parallel-computing framework, TopADD, was developed to handle optimization problems with arbitrary design domains. The framework demonstrated superior efficiency in computational time and memory usage compared to other frameworks in literature, and showed compatibility with additive manufacturing.
In this work, a two-dimensional (2D) and three-dimensional (3D) integrated topology optimization (TO) parallel-computing framework, named TopADD (TOPology optimization for Arbitrary Design Domains), is developed to deal with topology optimization problems with arbitrary design domains. The parallel-computing framework is an extended work of the initial parallel-computing framework developed by Aage et al. (Struct Multidiscip Optim 51(3): 565-572, 2015). The extension is threefold: (a) a 2D implementation has been incorporated into the framework to achieve seamless switching between 2D and 3D dimensions; (b) an efficient voxelizer that can initialize complex geometries into the design domains for topology optimization is developed; and (c) besides the compliance minimization problem, two other physics have been considered: the compliant mechanism and the heat conduction problems. Additionally, the computational efficiency of the proposed framework has been examined. Compared to the other frameworks in the literature, the proposed work has superior efficiency in both computational time and memory usage. Lastly, the proposed topology optimization framework's compatibility with additive manufacturing (AM) has been demonstrated by exporting and printing the final optimized parts without postprocessing.

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