Reference : A stochastic Galerkin cell-based smoothed finite element method (SGCS-FEM)
Scientific journals : Article
Engineering, computing & technology : Aerospace & aeronautics engineering
Engineering, computing & technology : Civil engineering
Engineering, computing & technology : Materials science & engineering
Engineering, computing & technology : Mechanical engineering
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Computational Sciences
http://hdl.handle.net/10993/40777
A stochastic Galerkin cell-based smoothed finite element method (SGCS-FEM)
English
Mathew, Tittu [Indian Institute of Technology Madras > Mechanical Engineering]
Beex, Lars mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Bordas, Stéphane mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Natarajan, Sundararajan [Indian Institute of Technology Madras > Mechanical Engineering]
In press
International Journal of Computational Methods
World Scientific Publishing Co.
Yes
International
0219-8762
Singapore
[en] Stochastic Galerkin Cell-based Smoothed Finite Element Method (SGCS- FEM) ; Karhunen-Loeve expansion (KLE) ; Polynomial Chaos Expansion (PLE) ; Random Material Properties ; Free Vibrations
[en] In this paper, the cell based smoothed finite element method is extended to solve stochastic partial diff erential equations with uncertain input parameters. The spatial field of Young's moduli and the corresponding stochastic results are represented by Karhunen-Lo eve expansion and polynomial chaos expansion, respectively. The Young's Modulus of structure is considered to be random for stochastic static as well as free vibration problems. Mathematical expressions and the solution procedure are articulated in detail to evaluate the statistical characteristics of responses in terms of static displacements and free vibration frequencies. The feasibility and eff ectiveness of the proposed SGCS-FEM method in terms of accuracy and lower requirement on the mesh size in the solution domain over that of conventional FEM for stochastic problems are demonstrated by carefully chosen numerical examples. From the numerical study, it is inferred that the proposed framework is computationally less demanding without compromising accuracy.
Researchers ; Professionals
http://hdl.handle.net/10993/40777

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