.png){kind=icon}
返回
Zooming method for FEA using a neural network
.png){kind=icon}
PRE
AI
DOI：10.1016/j.compstruc.2021.106480.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
FEM
Neural network
Zooming method
CFRP
Large-scale model
Parallel computing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.8
论文数:
6.2K
被引数:
1.7W
机构
引用论文
分享收藏Experimental and computational investigation of progressive damage accumulation in CFRP composites
PREAI
分享收藏Damage characteristics of open-hole laminated composites subjected to longitudinal loads.png)
COMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
分享收藏Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架
OAAI
OAAI分享收藏HEMIN ATTENUATED LPS-INDUCED ACUTE LUNG INJURY IN MICE VIA PROTECTING PULMONARY EPITHELIAL BARRIER AND REGULATING HO-1/NLRP3-MEDIATED PYROPTOSISShock
ShockIF0
PREAI
分享收藏
查看更多内容