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Zooming method for FEA using a neural network
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DOI：10.1016/j.compstruc.2021.106480.png)
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Abstract
En 中文
In the analysis of carbon fiber reinforced composite materials (CFRP), zooming analysis is used to simplify a finite element model by dividing it into global coarse meshes and local fine meshes. The zooming method using a shape function has a problem that displacement of boundary nodes of a local model cannot be obtained accurately if the nodes are outside a global model. In addition, a large-scale finite element model is required to simulate their complex failure accurately by modeling fibers and resin matrices in a local model. Parallel finite element analysis (FEA) open-source software has been developed to analyze large-scale models, but to implement a zooming method into a finite element software is not easy. In this study, we have developed a zooming method using a neural network. The neural network learns the relationship between nodal coordinates and nodal displacements of a global model, and the displacements for boundary conditions of a local model are obtained using the trained neural network. We verified the proposed method using small-scale models. The analysis results from this method were in good agreement with analysis results when using fine meshes. It was found that the method had advantages even when a part of a local model was outside of a global model. In addition, it is a simple method that does not require rewriting software codes, and it can be applied to various pieces of software easily using frameworks for a neural network. We also evaluated if this method can be applied to the analysis of largescale CFRP models with more than 70 million degrees of freedom. This zooming method and parallel FEM could evaluate the stress and strain of fibers and resin matrices in detail. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
FEM
Neural network
Zooming method
CFRP
Large-scale model
Parallel computing
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