Zooming method for FEA using a neural network. (15th April 2021)

• Record Type: Journal Article
• Authors: Yamaguchi, Taichi; Okuda, Hiroshi
• Is Part Of: Computers & structures. Volume 247(2021)
• Publication Date: 2021-04-15
• Subjects: FEM -- Neural network -- Zooming method -- CFRP -- Large-scale model -- Parallel computing; Structural engineering -- Data processing -- Periodicals; Electronic data processing -- Structures, Theory of -- Periodicals; 624.171
• DOI: 10.1016/j.compstruc.2021.106480
• Languages: English
• ISSNs: 0045-7949

Abstract

Highlights: Zooming method for FEA using a neural network. Local boundary displacement obtained accurately even when local nodes are outside of global meshes. Simple zooming method that could be easily applied to FEA software. Stress analysis of CFRP using a detailed model by parallel FEM. A large-scale model with 70 million degree of freedom. Abstract: 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 large-scale 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.