Multi-resolution topology optimization using adaptive isosurface variable grouping (MTOP-aIVG) for enhanced computational efficiency

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 48
  • Download : 0
Because finite elements and density elements are separated in multi-resolution topology optimization (MTOP), a relatively fewer number of finite elements can be used, thereby significantly reducing computing cost in finite element analysis (FEA) during topology optimization. However, for large-scale problems, numerous design variables are still required to precisely represent the optimum topology. This causes a dominant computational burden in design optimization. In this paper, an efficient multi-resolution topology optimization (MTOP) using adaptive isosurface variable grouping (aIVG) is proposed to alleviate the above computational burden in topology optimization by grouping design variables of similar grouping criteria into a single grouped design variable. Adaptive isosurface variable grouping is performed according to the grouping criterion which can be calculated using design variables and their sensitivities. Numerical examples such as 2D and 3D compliance minimization, 2D compliant mechanism, 2D multiple displacement constraints, and 3D thermal compliance minimization demonstrate that the proposed MTOP-aIVG significantly reduces computation time in optimization by virtue of using a reduced number of design variables.
Publisher
SPRINGER
Issue Date
2021-04
Language
English
Article Type
Article
Citation

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, v.63, no.4, pp.1743 - 1766

ISSN
1615-147X
DOI
10.1007/s00158-020-02774-2
URI
http://hdl.handle.net/10203/282260
Appears in Collection
ME-Journal Papers(저널논문)GT-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0