کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10151398 863650 2019 27 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Large-scale stochastic topology optimization using adaptive mesh refinement and coarsening through a two-level parallelization scheme
ترجمه فارسی عنوان
بهینه سازی توپولوژی تصادفی در مقیاس بزرگ با استفاده از پالایش و تطبیق شبکه های تطبیقی ​​از طریق یک طرح موازنه دو سطح
کلمات کلیدی
پالایش توری سازنده، مقیاس بزرگ، محاسبات موازی، بهینه سازی توپولوژی قوی مشتق شده توپولوژیک، شبکۀ تفریحی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods in Applied Mechanics and Engineering - Volume 343, 1 January 2019, Pages 186-206
نویسندگان
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