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Material database construction for data-driven computing via a continuous path-following method
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DOI：10.1016/j.compstruct.2023.117187.png)
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摘要
En 中文
Data-driven computational homogenization has been proposed recently for the analyses of composite struc-tures. Its basic idea is to construct an equivalent stress-strain database of composites via offline homogenization on the representative volume element and conduct online macroscopic simulation through distance-minimizing data-driven computing. Thanks to the scale separation of concurrent multiscale systems, this framework allows for improving online computational efficiency. However, high-density database construction in the offline stage remains a burdensome and time-consuming task. To this end, this work proposed an efficient approach that associates computational homogenization with the Asymptotic Numerical Method (ANM) to construct a high-density database. Being a reliable and efficient perturbation technique, the ANM allows for accurate tracking of the displacement-load paths and easily generates abundant equivalent stress-strain data on the paths. A fiber reinforced composite material with fiber buckling has been considered to demonstrate the accuracy and efficiency of the proposed method for the database construction of composites.
Keyword:
Data-driven computational homogenization
Multiscale modeling
Database construction
Asymptotic numerical method
期刊
IF:
7.1
论文数:
1.8W
被引数:
8.0W
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