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On Non-Penalization SEMDOT Using Discrete Variable Sensitivities

Author

Listed:
  • Yun-Fei Fu

    (Deakin University
    Deakin University)

  • Kai Long

    (North China Electric Power University)

  • Bernard Rolfe

    (Deakin University)

Abstract

This work proposes a non-penalization Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm, which is a typical elemental volume fraction-based topology optimization method, by adopting discrete variable sensitivities for solid, void, and assumed boundary elements instead of the continuous variable sensitivities used in the penalization one. In the proposed non-penalized SEMDOT algorithm, the material penalization scheme is eliminated. The efficiency, effectiveness, and general applicability of the proposed non-penalized algorithm are demonstrated in three case studies containing compliance minimization, compliant mechanism design, and heat conduction problems, as well as thorough comparisons with the penalized algorithm. In addition, the length scale control approach is used to solve the discontinuous boundary issue observed in thin and long structural features. The numerical results show that the convergency of the newly proposed non-penalization algorithm is stronger than the penalization algorithm, and improved results can be obtained by the non-penalized algorithm.

Suggested Citation

  • Yun-Fei Fu & Kai Long & Bernard Rolfe, 2023. "On Non-Penalization SEMDOT Using Discrete Variable Sensitivities," Journal of Optimization Theory and Applications, Springer, vol. 198(2), pages 644-677, August.
  • Handle: RePEc:spr:joptap:v:198:y:2023:i:2:d:10.1007_s10957-023-02222-3
    DOI: 10.1007/s10957-023-02222-3
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    References listed on IDEAS

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    1. Niels Aage & Erik Andreassen & Boyan S. Lazarov & Ole Sigmund, 2017. "Giga-voxel computational morphogenesis for structural design," Nature, Nature, vol. 550(7674), pages 84-86, October.
    2. Thibaut Rodriguez & Marco Montemurro & Paul Texier & Jérôme Pailhès, 2020. "Structural Displacement Requirement in a Topology Optimization Algorithm Based on Isogeometric Entities," Journal of Optimization Theory and Applications, Springer, vol. 184(1), pages 250-276, January.
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