Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges
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.
Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges
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.
Accelerating Large-scale Topology Optimization: State-of-the-Art and Challenges
Mukherjee, Sougata (Autor:in) / Lu, Dongcheng (Autor:in) / Raghavan, Balaji (Autor:in) / Breitkopf, Piotr (Autor:in) / Dutta, Subhrajit (Autor:in) / Xiao, Manyu (Autor:in) / Zhang, Weihong (Autor:in)
2021
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Topology optimization of large scale stokes flow problems
| British Library Online Contents | 2008
An efficient topology optimization algorithm for large-scale three-dimensional structures
| Springer Verlag | 2024
Large-scale parallel topology optimization using a dual-primal substructuring solver
| British Library Online Contents | 2008
Imposing maximum length scale in topology optimization
| British Library Online Contents | 2009
Large deformations and stability in topology optimization
| British Library Online Contents | 2005