THESIS
2019
xi, 58 pages : illustrations (some color) ; 30 cm
Abstract
Nowadays, topology optimization has developed as a powerful design instrument for the
engineering process. An increasing number of manufacturers begin to employ the topology
optimization method to improve structural performance during the design procedure. At present,
CAD software manufacturers such as AutoCAD, Dassault, Altair, and others have insert
topology optimization algorithms into their commercial software. As one of the structural
optimization methods, topology optimization methods mainly include a homogenization-based
approach, the solid isotropic material with penalization (SIMP) approach, the evolutionary
structural optimization approach, the level set approach and the newly moving morphable
components/voids (MMC/MMV) approach.
As the topology optimization method is...[
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Nowadays, topology optimization has developed as a powerful design instrument for the
engineering process. An increasing number of manufacturers begin to employ the topology
optimization method to improve structural performance during the design procedure. At present,
CAD software manufacturers such as AutoCAD, Dassault, Altair, and others have insert
topology optimization algorithms into their commercial software. As one of the structural
optimization methods, topology optimization methods mainly include a homogenization-based
approach, the solid isotropic material with penalization (SIMP) approach, the evolutionary
structural optimization approach, the level set approach and the newly moving morphable
components/voids (MMC/MMV) approach.
As the topology optimization method is gradually applied to practical engineering projects,
the increasing computational complexity has become the biggest obstacle. Therefore, how to
effectively improve the computational efficiency of topology optimization is an urgent problem
to be solved. The SIMP method implements parallel computing to improve computing
efficiency. As for the level set method, the introduction of parallel computing also becomes the
preferred solution.
In this thesis, a high-performance computing method will be utilized in the level-set
topology optimization method for generating large-scale structures. Here, the structure based
on the compactly supported radial basis functions (CSRBFs) as the example is to exhibit how
parallel computing employed in whole computation process, including mesh generation,
calculation and assembly of finite element stiffness matrices, sensitivity analysis, solution of
the structural state equation, updating and evolution of level set function and out-steaming of
final results.
Through the optimized structure and time-consuming analysis, there are several valuable
discoveries: (1) the speed of optimization computing has extremely increased especially for
large-scale structures, (2) the optimized structures of the truss-like components are gradually
replaced by thin-sheet-like parts while refining the mesh. These results described above identify
that parallel computing has a significant contribution to level-set topology optimization.
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