期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 343, 期 -, 页码 186-206出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2018.08.028
关键词
Adaptive mesh refinement; Large scale; Parallel computing; Robust topology optimization; Topological derivative; Sparse grid
资金
- AEI/FEDER and UE through the ELASTIC-FLOW, Spain [DPI2015-67857-R]
- GPUSIM, Spain [DPI2016-77538-R]
- Fundacion Seneca-Agencia de Ciencia y Tecnologia de la Region de Murcia, Spain [19274/PI/14]
- Spanish Government through the Ramon y Cajal grant, Spain [RYC-2015-17367]
- Red Espanola de Supercomputacion
- European PRACE computational network
Topology optimization under uncertainty of large-scale continuum structures is a computational challenge due to the combination of large finite element models and uncertainty propagation methods. The former aims to address the ever-increasing complexity of more and more realistic models, whereas the latter is required to estimate the statistical metrics of the formulation. In this work, the computational burden of the problem is addressed using a sparse grid stochastic collocation method, to calculate the statistical metrics of the topology optimization under uncertainty formulation, and a parallel adaptive mesh refinement method, to efficiently solve each of the stochastic collocation nodes. A two-level parallel processing scheme (TOUU-PS2) is proposed to profit from parallel computation on distributed memory systems: the stochastic nodes are distributed through the distributed memory system, and the efficient computation of each stochastic node is performed partitioning the problem using a domain decomposition strategy and solving each subdomain using an adaptive mesh refinement method. A dynamic load-balancing strategy is used to balance the workload between subdomains, and thus increasing the parallel performance by reducing processor idle time. The topology optimization problem is addressed using the topological derivative concept in combination with a level-set method. The performance and scalability of the proposed methodology are evaluated using several numerical benchmarks and real-world applications, showing good performance and scalability up to thousands of processors. (C) 2018 Elsevier B.V. All rights reserved.
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