On Non-Penalization SEMDOT Using Discrete Variable Sensitivities

Yun-Fei Fu (), Kai Long () and Bernard Rolfe ()
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Yun-Fei Fu: Deakin University
Kai Long: North China Electric Power University
Bernard Rolfe: Deakin University

Journal of Optimization Theory and Applications, 2023, vol. 198, issue 2, No 8, 644-677

Abstract: 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.

Keywords: Topology optimization; Non-penalization; Sensitivity analysis; Smooth-edged material strategy (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10957-023-02222-3

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