Concurrent multiscale topology optimization: A hybrid approach

M. Nguyen, T. Bui
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引用次数: 1

Abstract

This paper presents a hybrid approach for multiscale topology optimization of structures. The topological shape of both macro-structure and micro-structure are concurrently optimized, based on the solid isotropic material with penalization (SIMP) technique in combination with finite element method (FEM). The material is assumed to have periodically patterned micro-structures, such that the effective properties can be evaluated via energy-based homogenization method (EBHM). In every iteration, the effective properties of material are passed to the macroscopic problem, and the macroscopic behavior (e.g. strain energy) is transferred back to the micro-scale problem, where the unit cell representing the micro-structure of material is determined for the next iteration. It is found that the update process can be done separately, i.e., the sensitivity of macro-scale design variables is not required during the update of micro-scale design variables, and vice versa. Hence, the proposal is that the macro-structure is updated by the gradient-free Proportional Topology Optimization (PTO) algorithm to utilize the computational efficiency of PTO. The micro-structure is still updated by the common gradient-based algorithm, namely Optimality Criteria (OC). Three benchmark numerical examples are investigated, demonstrating the feasibility and efficiency of the proposed hybrid approach.
并发多尺度拓扑优化:一种混合方法
提出了一种结构多尺度拓扑优化的混合方法。基于固体各向同性材料惩罚法(SIMP)和有限元法(FEM)相结合的方法,对宏观结构和微观结构的拓扑形状进行了并行优化。假设材料具有周期性图案的微观结构,从而可以通过基于能量的均匀化方法(EBHM)来评估有效性能。在每一次迭代中,材料的有效性质被传递到宏观问题,宏观行为(如应变能)被传递回微观问题,在微观问题中,代表材料微观结构的单元格被确定,用于下一次迭代。研究发现,更新过程可以单独进行,即在更新微尺度设计变量时不需要考虑宏观尺度设计变量的敏感性,反之亦然。因此,建议采用无梯度比例拓扑优化(PTO)算法更新宏观结构,以充分利用PTO算法的计算效率。微观结构的更新仍然是由常见的基于梯度的算法,即最优性准则(OC)。通过对三个基准数值算例的分析,验证了该方法的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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