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Analytical finite element matrix elements and global matrix assembly for hierarchical 3-D vector basis functions within the hybrid finite element boundary integral method

机译:混合有限元边界积分法中层次3-D向量基函数的解析有限元矩阵元素和全局矩阵集合

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A hybrid higher-order finite element boundary integral (FE-BI) technique is discussed where the higher-order FE matrix elements are computed by a fully analytical procedure and where the gobal matrix assembly is organized by a self-identifying procedure of the local to global transformation. This assembly procedure applys to both, the FE part as well as the BI part of the algorithm. The geometry is meshed into three-dimensional tetrahedra as finite elements and nearly orthogonal hierarchical basis functions are employed. The boundary conditions are implemented in a strong sense such that the boundary values of the volume basis functions are directly utilized within the BI, either for the tangential electric and magnetic fields or for the asssociated equivalent surface current densities by applying a cross product with the unit surface normals. The self-identified method for the global matrix assembly automatically discerns the global order of the basis functions for generating the matrix elements. Higher order basis functions do need more unknowns for each single FE, however, fewer FEs are needed to achieve the same satisfiable accuracy. This improvement provides a lot more flexibility for meshing and allows the mesh size to raise up to λ/3. The performance of the implemented system is evaluated in terms of computation time, accuracy and memory occupation, where excellent results with respect to precision and computation times of large scale simulations are found.
机译:讨论了一种混合式高阶有限元边界积分(FE-BI)技术,其中,高阶有限元矩阵元素是通过完全分析程序来计算的,而gobal矩阵组件是通过局部的自识别程序来组织的。全球转型。此组装过程适用于算法的FE部分和BI部分。将该几何体划分为三维四面体作为有限元,并采用几乎正交的层次基础函数。在严格意义上实现了边界条件,因此通过在单元上施加叉积,可以在BI内部直接使用体积基函数的边界值,用于切向电场和磁场或相关的等效表面电流密度表面法线。全局矩阵装配的自识别方法自动识别用于生成矩阵元素的基本函数的全局顺序。对于每个单个有限元,高阶基函数的确需要更多的未知数,但是,为实现相同的可满足精度,需要较少的有限元。此改进为网格划分提供了更大的灵活性,并允许将网格大小提高到λ/ 3。在计算时间,准确性和内存占用方面评估了所实施系统的性能,在大规模仿真的精度和计算时间方面,可以找到出色的结果。

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