Large-scale structural topology optimization has always suffered from prohibitively high computational costs that have till date hindered its widespread use in industrial design. The first and major contributor to this problem is the cost of solving the Finite Element equations during each iteration of the optimization loop. This is compounded by the frequently very fine 3D models needed to accurately simulate mechanical or multi-physical performance. The second issue stems from the requirement to embed the high-fidelity simulation within the iterative design procedure in order to obtain the optimal design. The prohibitive number of calculations needed as a result of both these issues, is often beyond the capacities of existing industrial computers and software. To alleviate these issues, the last decade has opened promising pathways into accelerating the topology optimization procedure for large-scale industrial sized problems, using a variety of techniques, including re-analysis, multi-grid solvers, model reduction, machine learning and high-performance computing, and their combinations. This paper attempts to give a comprehensive review of the research activities in all of these areas, so as to give the engineer both an understanding as well as a critical appreciation for each of these developments.

大规模的结构拓扑优化始终遭受过高的计算成本，迄今为止一直阻碍了其在工业设计中的广泛使用。导致此问题的首要因素是在优化循环的每次迭代过程中求解有限元方程的成本。精确模拟机械或多物理性能所需的非常精细的3D模型常常使情况更加复杂。第二个问题源于将高保真仿真嵌入迭代设计过程中以获得最佳设计的要求。由于这两个问题而导致的计算量过高，通常超出了现有工业计算机和软件的能力。为了减轻这些问题，在过去的十年中，使用多种技术（包括重新分析，多网格求解器，模型简化，机器学习和高性能计算及其解决方案）为加速大规模工业规模问题的拓扑优化过程开辟了有希望的途径。组合。本文试图对所有这些领域的研究活动进行全面的回顾，以便使工程师既理解又批评地评价这些发展。