In this study, an efficient reanalysis strategy for dynamic topology optimization is proposed. Compared with other related studies, an online successive dynamic reanalysis method and POD-based approximate dynamic displacement strategy are integrated. In dynamic reanalysis, the storage of the stiffness matrix decomposition can be avoided and the reduced basis vectors should be updated successively according to the structural status in each iteration. Therefore, the bottleneck of combined approximation method for large-scale dynamic topology optimization can be handled. Sequentially, the Proper Orthogonal Decomposition (POD) is employed to obtain the approximate dynamic displacement, in which the Proper Orthogonal Mode (POM) of the displacement field is employed to establish the approximated equivalent static loads of Equivalent Static Load (ESL) method. Compared with the exact equivalent static loads at all the time intervals, the number of equivalent static loads is significantly reduced. Finally, the 2D and 3D test results indicate that the proposed method has remarkable speed-up effect on the premise of small relative error, support the strength of the proposed strategy.

翻译：本研究提出了一种面向动态拓扑优化的高效重分析策略。与相关研究相比，该方法集成了在线连续动态重分析与基于本征正交分解（POD）的近似动态位移策略。在动态重分析中，可避免存储刚度矩阵分解结果，并需根据每次迭代中的结构状态连续更新缩减基向量，从而解决大规模动态拓扑优化中组合近似方法的瓶颈问题。随后，采用本征正交分解（POD）获取近似动态位移，利用位移场的本征正交模态（POM）建立等效静载荷法（ESL）中的近似等效静载荷。与全时间区间内的精确等效静载荷相比，等效静载荷数量显著减少。二维与三维测试结果表明，该方法在保持较小相对误差的前提下具有显著加速效果，验证了所提策略的有效性。