A Data-driven Multiscale Analysis for Hyperelastic Composite Materials Based on the Mean-field Homogenization Method

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초록

The classical multiscale finite element (FE2) method involves iterative calculations of micro-boundary value problems for representative volume elements at every integration point in macro scale, making it a computationally time and data storage space. To overcome this, we developed the data-driven multiscale analysis method based on the mean-field homogenization (MFH). Data-driven computational mechanics (DDCM) analysis is a model-free approach that directly utilizes strain-stress datasets. For performing multiscale analysis, we efficiently construct a strain-stress database for the microstructure of composite materials using mean-field homogenization and conduct data-driven computational mechanics simulations based on this database. In this paper, we apply the developed multiscale analysis framework to an example, confirming the results of data-driven computational mechanics simulations considering the microstructure of a hyperelastic composite material. Therefore, the application of data-driven computational mechanics approach in multiscale analysis can be applied to various materials and structures, opening up new possibilities for multiscale analysis research and applications.

키워드

(Data-driven computational mechanics)(Multiscale analysis)(Mean-field homogenization)(Hyperelasticity)MODELFRAMEWORKBEHAVIOR
제목
A Data-driven Multiscale Analysis for Hyperelastic Composite Materials Based on the Mean-field Homogenization Method
저자
Kim, SuhanLee, WonjooShin, Hyunseong
DOI
10.7234/composres.2023.36.5.329
발행일
2023-10
유형
Article
저널명
Composites Research
36
5
페이지
329 ~ 334