2018
DOI: 10.1016/j.cma.2018.01.012
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A scaled boundary finite element based node-to-node scheme for 2D frictional contact problems

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Cited by 51 publications
(22 citation statements)
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“…The scaled boundary finite-element method [12,13] is a semi-analytical numerical method that only discretizes the outer boundary of the computational domain, reduces the problem dimension by one, accurately simulates the infinite domain, and does not need a fundamental solution. The SBFEM has been widely used in the fields of fracture mechanics [14][15][16], seepage problems [17][18][19], dam-reservoir interactions [20,21] and contact problems [22][23][24][25][26].…”
Section: Introductionmentioning
confidence: 99%
“…The scaled boundary finite-element method [12,13] is a semi-analytical numerical method that only discretizes the outer boundary of the computational domain, reduces the problem dimension by one, accurately simulates the infinite domain, and does not need a fundamental solution. The SBFEM has been widely used in the fields of fracture mechanics [14][15][16], seepage problems [17][18][19], dam-reservoir interactions [20,21] and contact problems [22][23][24][25][26].…”
Section: Introductionmentioning
confidence: 99%
“…It is worth mentioning that previous investigations have shown that polygonal super elements are quite robust for small and large finite element sizes inside the same mesh [XST18]. This robustness is important if geometric optimization changes the element sizes on the fly.…”
Section: Sbfem Forward Modelmentioning
confidence: 86%
“…This method allows the construction and coupling of star-convex polygonal elements consisting of an arbitrary number of edges, where the type and order of interpolation along each edge can be chosen independently. The SBFEM has been applied successfully to quadtree/octree decompositions [61][62][63][64], as well as polytopal meshes [65,66]. For linear static problems, the SBFEM achieves high order convergence while only requiring nodes on the element edges.…”
Section: Introductionmentioning
confidence: 99%