2015
DOI: 10.2514/1.j053304
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Improved Point Selection Method for Hybrid-Unstructured Mesh Deformation Using Radial Basis Functions

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Cited by 71 publications
(29 citation statements)
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“…Although this leads to significant speed-up, it also introduces an error at the unused boundary nodes, hence necessitating an additional correction step [31] (e.g., by means of Delaunay graph mapping [32]). Other recent developments in this direction can be found in [33,34,35].…”
Section: Introductionmentioning
confidence: 99%
“…Although this leads to significant speed-up, it also introduces an error at the unused boundary nodes, hence necessitating an additional correction step [31] (e.g., by means of Delaunay graph mapping [32]). Other recent developments in this direction can be found in [33,34,35].…”
Section: Introductionmentioning
confidence: 99%
“…The size of the linear system to be solved is directly related to the number of the moving surface mesh points. To accelerate the computation, data reduction algorithms were proposed by Rendall and Allen [11,12,13], Sheng and Allen [16] and Wang et al [17] to limit the RBF interpolation on a coarsened subset of surface mesh. To decrease the error on the surface points, they applied a greedy algorithm to select the optimum reduced set of surface mesh.…”
Section: Introductionmentioning
confidence: 99%
“…Some techniques are dedicated for structured grids [10,11] while others are developed for unstructured mesh topology [12 -14]. Recently, mesh movement algorithm based on radial basis function (RBF) interpolation [15,16] has gained lot of interest. This technique has several advantages; it is independent of the type of mesh and the nature of the flow solver, the method does not require any mesh connectivity information, the method is computationally efficient and can be readily parallelized.…”
Section: Introductionmentioning
confidence: 99%
“…(7)), is solved for the complete domain it will make the method computationally expensive for very large degrees of freedom problems. Therefore, REF interpolation has been combined with a data reduction "double edged" greedy algorithm [16]. Before starting the greedy cycle a list of surface points is selected as an initial guess.…”
Section: Introductionmentioning
confidence: 99%