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Data-driven multiscale method for composite plates
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AI
DOI：10.1007/s00466-022-02195-6.png)
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Abstract
En 中文
Composite plates are widely used in many engineering fields such as aerospace and automotive. An accurate and efficient multiscale modeling and simulation strategy is of paramount importance to improve design and manufacture. To this end, we propose an efficient data-driven computing scheme based on the classical plate theory for the multiscale analysis of composite plates. In order to accurately describe the relationship between the macroscopic mechanical properties and the microscopic architecture, the multiscale finite element method (FE2) is adopted to compute the generalized strain and stress fields. These data are then used to construct a database for data-driven computing. Since the database is offline populated, the data-driven computing scheme allows for a reduced computational cost when compared to the traditional multiscale method, where the concurrent coupling of different scales is still a burden. And data are obtained from a reduced structural model for computational efficiency. The proposed scheme is therefore addressed as Structural-Genome-Driven (SGD) modeling of plates. Compared to the general data-driven computational mechanics modeling of plates, SGD is found to be more efficient since the number of integration points is significantly reduced. This scheme provides a robust alternative computational tool for composite plate structures analysis.
Keywords:
Data-driven computing
Structural-Genome-Driven
Multiscale finite element method
Composite plates
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