Algorithm Research Based on the PDE Sensitivity Filter北大核心CSCD

采用PDE灵敏度滤波器可以消除连续体结构拓扑优化结果存在的棋盘格现象、数值不稳定等问题，且PDE灵敏度滤波器的实质是具有Neumann边界条件的Helmholtz偏微分方程.针对大规模PDE灵敏度滤波器的求解问题，有限元分析得到其代数方程，分别采用共轭梯度算法、多重网格算法和多重网格预处理共轭梯度算法对代数方程进行求解，并且研究精度、过滤半径以及网格数量对拓扑优化效率的影响.结果表明：与共轭梯度算法和多重网格算法相比，多重网格预处理共轭梯度算法迭代次数最少，运行时间最短，极大地提高了拓扑优化效率.

Algorithm Research Based on the PDE Sensitivity Filter

The PDE sensitivity filter can eliminate the checkerboard patterns and numerical instability existing in the topology optimization results of continuum structures, and the essence of the PDE sensitivity filter is the Helmholtz partial differential equation with Neumann boundary conditions. To solve the large-scale PDE sensitivity filter problem, the conjugate gradient algorithm, the multigrid algorithm and the multigrid preconditioned conjugate gradient algorithm were used to solve the algebraic equations obtained by finite element analysis, and the effects of accuracy, filter radius and grid numbers on the efficiency of topology optimization were studied. The results show that, compared with the conjugate gradient algorithm and the multi-grid algorithm, the multi-grid preconditioned conjugate gradient algorithm has the least number of iterations and the shortest running time, which greatly improves the efficiency of topology optimization.