Parallel BESO framework for solving high-resolution topology optimisation problems. (February 2023)

• Record Type: Journal Article
• Authors: Xiong, Yulin; Zhao, Zi-Long; Lu, Hongjia; Shen, Wei; Xie, Yi Min
• Is Part Of: Advances in engineering software. Volume 176(2023)
• Publication Date: 2023-02
• Subjects: Topology optimisation -- Bi-directional evolutionary structural optimisation -- FEniCS -- Parallel computing -- High-resolution; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103389
• Languages: English
• ISSNs: 0965-9978

Abstract

Abstract: The bi-directional evolutionary structural optimisation (BESO) has attracted much interest in recent decades. However, the high computational cost of the topology optimisation method hinders its applications in large-scale industrial designs. In this study, a parallel BESO method is developed to solve high-resolution topology optimisation problems. An open-source computing platform, FEniCS, is used to parallelise the finite element analysis (FEA) and optimisation steps. Significant improvements in efficiency have been made to the FEA and the filtering process. An iterative solver, a reanalysis approach and a hard-kill option in BESO have been developed to reduce the computational cost of the FEA. A PDE-based isotropic filter scheme is used to eliminate the time-consuming elemental adjacency search process. The efficiency and effectiveness of the developed method are demonstrated by a series of numerical examples in both 2D and 3D. It is shown that the parallel BESO can efficiently solve problems with more than 100 million tetrahedron elements on a 14-cores CPU server. This work holds great potential for high-resolution design problems in engineering and architecture. Highlights: A parallel BESO method is proposed for high-resolution topology optimisation. An iterative solver, a reanalysis approach and a hard-kill option are developed. A PDE-based filter is used to avoid the elemental adjacency search. An optimisation problem with 120 million elements is solved on an ordinary server.