Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges

Sougata Mukherjee, Dongcheng Lu, Balaji Raghavan, Piotr Breitkopf, Subhrajit Dutta, Manyu Xiao, Weihong Zhang

Research output: Contribution to journalArticlepeer-review

96 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)4549-4571
Number of pages23
JournalArchives of Computational Methods in Engineering
Volume28
Issue number7
DOIs
StatePublished - Dec 2021

Fingerprint

Dive into the research topics of 'Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges'. Together they form a unique fingerprint.

Cite this