An adaptive T-spline finite cell method for structural shape optimization

Chen, Liang; Zhang, Weihong; Meng, Liang; Jiu, Lipeng; Feng, Shengqi

doi:10.1007/s00158-020-02645-w

Cited by 10 publications

(1 citation statement)

References 49 publications

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“…Zeng et al 67 proposed an adaptive hierarchical optimization approach for variable stiffness laminates using lamination parameters based on THB. Chen et al 68 presented an adaptive T‐spline finite cell method to perform structural shape optimization. To make the full potential of THB, fully adaptive TO methods are presented in References 69,70 with respect to SIMP and MMC frameworks.…”

Section: Introductionmentioning

confidence: 99%

Adaptive topology optimization under suitably graded THB‐spline refinement and coarsening

Xie

Yang

Jiang

et al. 2021

Numerical Meth Engineering

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In the present work, we propose an adaptive topology optimization (TO) method under suitably graded truncated hierarchical B-spline refinement and coarsening (SGTHB-TO). A series of algorithms have been devised to implement admissible adaptivity of SGTHB-TO by resorting to the definitions of suitably graded isogeometric hierarchical meshes. We apply the proposed SGTHB-TO method to two-dimensional and three-dimensional TO problems of compliance and compliant mechanism. According to the numerical results, SGTHB-TOs with or without admissible restriction share similar optimization processes, except that numerical artifact, such as edge blurring and islanding gray regions occurring in SGTHB-TO without admissible restriction, can be eliminated by SGTHB-TO with admissible parameter equaling to 2. Besides, the convergence rate can be largely accelerated by imposing an admissible constraint on SGTHB-TO for TO problems with geometric nonlinearity existing in the design domain, which is attributed to the improved numerical accuracy by the admissible adaptive discretization scheme of isogeometric analysis (IGA). Therefore, the SGTHB-TO method with the admissible parameter equaling to 2 is a more effective adaptive TO method than SGTHB-TO without considering admissible restriction, in terms of both numerical stability and convergence rate.

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Section: Introductionmentioning

confidence: 99%