A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching...A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.展开更多
The optimization of two-scale structures can adapt to the different needs of materials in various regions by reasonably arranging different microstructures at the macro scale,thereby considerably improving structural ...The optimization of two-scale structures can adapt to the different needs of materials in various regions by reasonably arranging different microstructures at the macro scale,thereby considerably improving structural performance.Here,a multiple variable cutting(M-VCUT)level set-based data-driven model of microstructures is presented,and a method based on this model is proposed for the optimal design of two-scale structures.The geometry of the microstructure is described using the M-VCUT level set method,and the effective mechanical properties of microstructures are computed by the homogenization method.Then,a database of microstructures containing their geometric and mechanical parameters is constructed.The two sets of parameters are adopted as input and output datasets,and a mapping relationship between the two datasets is established to build the data-driven model of microstructures.During the optimization of two-scale structures,the data-driven model is used for macroscale finite element and sensitivity analyses.The efficiency of the analysis and optimization of two-scale structures is improved because the computational costs of invoking such a data-driven model are much smaller than those of homogenization.展开更多
A method is proposed to control the minimum width of lattice structure in the topology optimization by using a Multiple Variable Cutting(M-VCUT)based substructure.The geometry of substructure is described by using the...A method is proposed to control the minimum width of lattice structure in the topology optimization by using a Multiple Variable Cutting(M-VCUT)based substructure.The geometry of substructure is described by using the M-VCUT level set approach,and the substructures are condensed to superelements.A data-driven model of substructure is constructed,and it is used for the finite element analysis and sensitivity analysis during the optimization,so that computational costs are reduced.More importantly,only the substructures whose minimum width are larger than an admissible value are considered in the data-driven model,thus inherently enforcing the constraint of minimum width and making the optimization much easier.The effectiveness of the proposed method is demonstrated through several numerical examples.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘A data-driven model ofmultiple variable cutting(M-VCUT)level set-based substructure is proposed for the topology optimization of lattice structures.TheM-VCUTlevel setmethod is used to represent substructures,enriching their diversity of configuration while ensuring connectivity.To construct the data-driven model of substructure,a database is prepared by sampling the space of substructures spanned by several substructure prototypes.Then,for each substructure in this database,the stiffness matrix is condensed so that its degrees of freedomare reduced.Thereafter,the data-drivenmodel of substructures is constructed through interpolationwith compactly supported radial basis function(CS-RBF).The inputs of the data-driven model are the design variables of topology optimization,and the outputs are the condensed stiffness matrix and volume of substructures.During the optimization,this data-driven model is used,thus avoiding repeated static condensation that would requiremuch computation time.Several numerical examples are provided to verify the proposed method.
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘The optimization of two-scale structures can adapt to the different needs of materials in various regions by reasonably arranging different microstructures at the macro scale,thereby considerably improving structural performance.Here,a multiple variable cutting(M-VCUT)level set-based data-driven model of microstructures is presented,and a method based on this model is proposed for the optimal design of two-scale structures.The geometry of the microstructure is described using the M-VCUT level set method,and the effective mechanical properties of microstructures are computed by the homogenization method.Then,a database of microstructures containing their geometric and mechanical parameters is constructed.The two sets of parameters are adopted as input and output datasets,and a mapping relationship between the two datasets is established to build the data-driven model of microstructures.During the optimization of two-scale structures,the data-driven model is used for macroscale finite element and sensitivity analyses.The efficiency of the analysis and optimization of two-scale structures is improved because the computational costs of invoking such a data-driven model are much smaller than those of homogenization.
基金supported by the National Natural Science Foundation of China(Grant No.12272144).
文摘A method is proposed to control the minimum width of lattice structure in the topology optimization by using a Multiple Variable Cutting(M-VCUT)based substructure.The geometry of substructure is described by using the M-VCUT level set approach,and the substructures are condensed to superelements.A data-driven model of substructure is constructed,and it is used for the finite element analysis and sensitivity analysis during the optimization,so that computational costs are reduced.More importantly,only the substructures whose minimum width are larger than an admissible value are considered in the data-driven model,thus inherently enforcing the constraint of minimum width and making the optimization much easier.The effectiveness of the proposed method is demonstrated through several numerical examples.
