2017
DOI: 10.1016/j.camwa.2016.10.023
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Efficient mesh deformation based on Cartesian background mesh

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Cited by 9 publications
(4 citation statements)
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“…Any approach that has the potential to make grid-based ice accretion simulations for realistic configurations shall have the characteristics of automation, efficiency and robustness (Thompson et al , 2013). The most important approaches for mesh deformation (Fang et al , 2017) include the spring analogy method, linear elasticity analogy method, RBF-based method, background mesh based methods, partial differential equations (PDE)-based methods, finite macro-element mesh deformation methods and optimisation-based methods.…”
Section: Placement Of the Proposed Approach In The Icing Simulation Backgroundmentioning
confidence: 99%
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“…Any approach that has the potential to make grid-based ice accretion simulations for realistic configurations shall have the characteristics of automation, efficiency and robustness (Thompson et al , 2013). The most important approaches for mesh deformation (Fang et al , 2017) include the spring analogy method, linear elasticity analogy method, RBF-based method, background mesh based methods, partial differential equations (PDE)-based methods, finite macro-element mesh deformation methods and optimisation-based methods.…”
Section: Placement Of the Proposed Approach In The Icing Simulation Backgroundmentioning
confidence: 99%
“…As far as the authors ’ knowledge is concerned, this is the first time in scientific literature that RBF are proposed to handle icing simulations. A valuable exception is the Mesh Deformation Method Based on Cartesian Background Mesh (MDCBM) method proposed in Fang et al (2017), in which the use of RBF is foreseen just in the first stage of the methodological procedure. Some applications for evaluating snow accretions, however, are available in literature (Trenker and Payer, 2006; Allain et al , 2014; Zhu et al , 2017).…”
Section: Placement Of the Proposed Approach In The Icing Simulation Backgroundmentioning
confidence: 99%
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“…Jokonsson et al describe suggestions for choosing RBFs, and demonstrate that the computational cost will still grow as the mesh scale increases, even choosing the appropriate functions [1]. Considering the high computational cost, several approaches are proposed with greedy algorithm [21,26,27] or reducing the interpolated nodes [28][29][30]. The improved RBFs method has better computational efficiency and can also preserve the mesh quality well.…”
Section: Introductionmentioning
confidence: 99%