2010
DOI: 10.4028/www.scientific.net/amr.97-101.3646
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Structural Dynamic Topology Optimization and Sensitivity Analysis Based on RKPM

Abstract: A numerical method for structural dynamic topology optimization and sensitivity analysis is presented by using RKPM. In this paper, the relative density of node and maximum fundamental eigenfrequency is respectively chosen as design variable and the objective function, and then the mathematical model for dynamic topology optimization based on RKPM is built. During the process of modeling, some effective measures are taken to dispose the multi-eigenvalues and localized modes. Subsequently, the sensitivity analy… Show more

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Cited by 2 publications
(2 citation statements)
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“…Gong et al [4] and Zheng et al [5] developed the topology optimization formulation based on the Element-free Galerkin method (EFG) and the stress constraint. Zhang et al [6] studied the structural dynamic topology optimization based on RKPM and nodal relative density. Cho et al [7] and Zhou et al [8] performed the topology optimization for large deformation nonlinearly structure and linear elastostatics plane problem by using RKPM, respectively.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Gong et al [4] and Zheng et al [5] developed the topology optimization formulation based on the Element-free Galerkin method (EFG) and the stress constraint. Zhang et al [6] studied the structural dynamic topology optimization based on RKPM and nodal relative density. Cho et al [7] and Zhou et al [8] performed the topology optimization for large deformation nonlinearly structure and linear elastostatics plane problem by using RKPM, respectively.…”
Section: Introductionmentioning
confidence: 99%
“…are very similar to plane stress problem in terms of form, but in fact the global stiffness matrix of plane stress problem is a (2 ) (2 )IP IP * × *square matrix[6], while K and α K in Eq. (7) have been degenerated into ( ) IP IP × square matrices, and great change takes place in the corresponding computational matrices,…”
mentioning
confidence: 99%