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A radial basis function for reconstructing complex immersed boundaries in ghost cell method 被引量:2
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作者 Jian-jian Xin Ting-qiu Li Fu-long Shi 《Journal of Hydrodynamics》 SCIE EI CSCD 2018年第5期890-897,共8页
It is important to track and reconstruct the complex immersed boundaries for simulating fluid structure interaction problems in an immersed boundary method(IBM). In this paper, a polynomial radial basis function(P... It is important to track and reconstruct the complex immersed boundaries for simulating fluid structure interaction problems in an immersed boundary method(IBM). In this paper, a polynomial radial basis function(PRBF) method is introduced to the ghost cell immersed boundary method for tracking and reconstructing the complex moving boundaries. The body surfaces are fitted with a finite set of sampling points by the PRBF, which is flexible and accurate. The complex or multiple boundaries could be easily represented. A simple treatment is used for identifying the position information about the interfaces on the background grid. Our solver and interface reconstruction method are validated by the case of a cylinder oscillating in the fluid. The accuracy of the present PRBF method is comparable to the analytic function method. In ta flow around an airfoil, the capacity of the proposed method for complex geometries is well demonstrated. 展开更多
关键词 Immersed boundary method (IBM) polynomial radial basis function ghost cell method interface reconstruction
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ON APPROXIMATION BY SPHERICAL REPRODUCING KERNEL HILBERT SPACES
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作者 Zhixiang Chen 《Analysis in Theory and Applications》 2007年第4期325-333,共9页
The spherical approximation between two nested reproducing kernels Hilbert spaces generated from different smooth kernels is investigated. It is shown that the functions of a space can be approximated by that of the s... The spherical approximation between two nested reproducing kernels Hilbert spaces generated from different smooth kernels is investigated. It is shown that the functions of a space can be approximated by that of the subspace with better smoothness. Furthermore, the upper bound of approximation error is given. 展开更多
关键词 spherical harmonic polynomial radial basis function reproducing kernel Hilbert space error estimates
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