论文标题
一种基于自适应重新分析的有效拓扑优化方法，并减少投影
An efficient topology optimization method based on adaptive reanalysis with projection reduction
论文作者
论文摘要
提出了基于自适应辅助减少模型重新分析（AARMR）的有效拓扑优化，以提高计算效率和规模。在这种方法中，将投影辅助还原模型（PARM）集成到合并的近似降低模型（CARM）中，以在不同方面降低模型的维度。首先，Carm限制了解决方案空间，以避免大型基质分解。其次，提出了PARM以动态构建Carm以节省计算成本。此外，建议使用多网格共轭梯度方法自适应地更新PARM。最后，测试了几个经典的数值示例，以表明所提出的方法不仅显着提高了计算效率，而且还可以解决由于内存限制而导致直接求解器难以解决的大规模问题。
Efficient topology optimization based on the adaptive auxiliary reduced model reanalysis (AARMR) is proposed to improve computational efficiency and scale. In this method, a projection auxiliary reduced model (PARM) is integrated into the combined approximation reduced model (CARM) to reduce the dimension of the model in different aspects. First, the CARM restricts the solution space to avoid large matrix factorization. Second, the PARM is proposed to construct the CARM dynamically to save computational cost. Furthermore, the multi-grid conjugate gradient method is suggested to update PARM adaptively. Finally, several classic numerical examples are tested to show that the proposed method not only significantly improves computational efficiency, but also can solve large-scale problems that are difficult to solve by direct solvers due to the memory limitations.
