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...[ Read more ]

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.