Article (Scientific journals)
Bubble-Enriched Smoothed Finite Element Methods for Nearly-Incompressible Solids
Lee, Changkye; Natarajan, Sundararajan; Hale, Jack et al.
2021In Computer Modeling in Engineering and Sciences, 127 (2), p. 411-436
Peer reviewed
 

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Keywords :
strain smoothing; smoothed finite element method; SFEM; bubble functions; hyperelasticity; mesh distortion
Abstract :
[en] This work presents a locking-free smoothed finite element method (S-FEM) for the simulation of soft matter modelled by the equations of quasi-incompressible hyperelasticity. The proposed method overcomes well-known issues of standard finite element methods (FEM) in the incompressible limit: the over-estimation of stiffness and sensitivity to severely distorted meshes. The concepts of cell-based, edge-based and node-based S-FEMs are extended in this paper to three-dimensions. Additionally, a cubic bubble function is utilized to improve accuracy and stability. For the bubble function, an additional displacement degree of freedom is added at the centroid of the element. Several numerical studies are performed demonstrating the stability and validity of the proposed approach. The obtained results are compared with standard FEM and with analytical solutions to show the effectiveness of the method.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Lee, Changkye
Natarajan, Sundararajan
Hale, Jack  ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Taylor, Zeike A.
Lee, Jurng-Jae
Bordas, Stéphane ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
External co-authors :
yes
Language :
English
Title :
Bubble-Enriched Smoothed Finite Element Methods for Nearly-Incompressible Solids
Publication date :
19 April 2021
Journal title :
Computer Modeling in Engineering and Sciences
ISSN :
1526-1506
Publisher :
Tech Science Press, Palmdale, United States - California
Volume :
127
Issue :
2
Pages :
411-436
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Name of the research project :
2016R1A6A1A03012812
Funders :
NRF Korea
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since 05 February 2021

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