2021
DOI: 10.1016/j.cam.2021.113492
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Efficient radial basis function mesh deformation methods for aircraft icing

Abstract: This paper presents an evaluation of efficient radial basis function mesh deformation for complex iced geometries. Given the high computational cost of mesh deformation, state-of-the-art radial basis function techniques are used for data reduction. The principle procedures adopted are multi-level greedy surface point selection and volume point reduction. The multi-level greedy surface

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Cited by 29 publications
(12 citation statements)
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“…The radial basis function can effectively construct the invisible relationship between input and output [ 28 ]. The response surface method [ 29 ] can reduce the number of experiments and improve the overall optimization efficiency.…”
Section: Methodsmentioning
confidence: 99%
“…The radial basis function can effectively construct the invisible relationship between input and output [ 28 ]. The response surface method [ 29 ] can reduce the number of experiments and improve the overall optimization efficiency.…”
Section: Methodsmentioning
confidence: 99%
“…Radial Basis Function (RBF) mesh deformation [35] is used to update mesh after each step of the icing simulation.…”
Section: Fig 2 Schematic Of a Multi-step Icing Simulationmentioning
confidence: 99%
“…RBF techniques are gaining more and more ground in the computer-aided engineering (CAE) field. Their application has already spread in the automotive [29], naval [30], aeronautical [31], energy [32], and medical fields [33]. Their main advantage is their potential to morph the mesh without changing its topology or its consistency while retaining disk usage at low levels.…”
Section: The Mesh Morphing Techniquementioning
confidence: 99%