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Automatic reconstruction of a patient-specific high-order surface representation and its application to mesh generation for CFD calculations

机译:特定于患者的高阶表面表示的自动重建及其在CFD计算网格生成中的应用

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We describe a set of procedures for the shape reconstruction and mesh generation of unstructured high-order spatial discretization of patient-specific geometries from a series of medical images and for the simulation of flows in these meshes using a high-order hp-spectral solver. The reconstruction of the shape of the boundary is based on the interpolation of an implicit function through a set of points obtained from the segmentation of the images. This approach is favoured for its ability of smoothly interpolating between sections of different topology. The boundary of the object is initially represented as an iso-surface of an implicit function defined in terms of radial basis functions. This surface is approximated by a triangulation extracted by the method of marching cubes. The triangulation is then suitably smoothed and refined to improve its quality and permit its approximation by a quilt of bi-variate spline surface patches. Such representation is often the standard input format required for state-of-the-art mesh generators. The generation of the surface patches is based on a partition of the triangulation into Voronoi regions and dual Delaunay triangulations with an even number of triangles. The quality of the triangulation is optimized by imposing that the distortion associated with the energy of deformation by harmonic maps is minimized. Patches are obtained by merging adjacent triangles and this representation is then used to generate a mesh of linear elements using standard generation techniques. Finally, a mesh of high-order elements is generated in a bottom-up fashion by creating the additional points required for the high-order interpolation and projecting them on the edges and surfaces of the quilt of patches. The methodology is illustrated by generating meshes for a by-pass graft geometry and calculating high-order CFD solutions in these meshes.
机译:我们描述了一系列程序,用于从一系列医学图像中对特定于患者的几何形状进行非结构化高阶空间离散的形状重构和网格生成,以及使用高阶hp光谱求解器模拟这些网格中的流动。边界形状的重建基于通过从图像分割获得的一组点对隐式函数进行插值。这种方法因其在不同拓扑的各个部分之间平滑内插的能力而受到青睐。对象的边界最初表示为根据径向基函数定义的隐式函数的等值面。通过行进立方体的方法提取的三角剖分来近似此表面。然后对三角剖分进行适当的平滑和精修,以改善其质量并允许其被二元样条曲面补丁的棉被所近似。这种表示通常是最新的网格生成器所需的标准输入格式。表面斑块的生成基于将三角剖分划分为Voronoi区域和具有偶数个三角形的双重Delaunay三角剖分。通过施加与谐波图变形能量相关的失真最小化,可以优化三角剖分的质量。通过合并相邻的三角形获得补丁,然后使用标准生成技术将此表示形式用于生成线性元素的网格。最后,通过创建高阶插值所需的附加点并将它们投影在面片的边缘和表面上,以自下而上的方式生成高阶元素的网格。通过为旁路移植物几何体生成网格并在这些网格中计算高阶CFD解决方案来说明该方法。

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