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首页> 外文期刊>Journal of Scientific Computing >An Efficient High-Order Time Integration Method for Spectral-Element Discontinuous Galerkin Simulations in Electromagnetics
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An Efficient High-Order Time Integration Method for Spectral-Element Discontinuous Galerkin Simulations in Electromagnetics

机译:电磁波谱间断Galerkin模拟的高效高阶时间积分方法

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We investigate efficient algorithms and a practical implementation of an explicit-type high-order timestepping method based on Krylov subspace approximations, for possible application to large-scale engineering problems in electromagnetics. We consider a semi-discrete form of the Maxwell's equations resulting from a high-order spectral-element discontinuous Galerkin discretization in space whose solution can be expressed analytically by a large matrix exponential of dimension k × k. We project the matrix exponential into a small Krylov subspace by the Arnoldi process based on the modified Gram-Schmidt algorithm and perform a matrix exponential operation with a much smaller matrix of dimension m × m (m(《) k). For computing the matrix exponential, we obtain eigenvalues of the m × m matrix using available library packages and compute an ordinary exponential function for the eigenvalues. The scheme involves mainly matrix-vector multiplications, and its convergence rate is generally O(△tm-l) in time so that it allows taking a larger timestep size as m increases. We demonstrate CPU time reduction compared with results from the five-stage fourth-order Runge-Kutta method for a certain accuracy. We also demonstrate error behaviors for long-time simulations. Case studies are also presented, showing loss of orthogonality that can be recovered by adding a low-cost reorthogonalization technique.
机译:我们研究有效的算法和基于Krylov子空间逼近的显式高阶时间步长方法的实际实现,以可能应用于电磁学中的大规模工程问题。我们考虑麦克斯韦方程的半离散形式,这是由空间中的高阶频谱元素不连续伽勒金离散化产生的,其解可以通过尺寸为k×k的大矩阵指数解析地表示。我们基于改进的Gram-Schmidt算法,通过Arnoldi过程将矩阵指数投影到一个小的Krylov子空间中,并使用尺寸更小的m×m(m(《)k)矩阵执行矩阵指数运算。为了计算矩阵指数,我们使用可用的库包获得m×m矩阵的特征值,并为该特征值计算一个普通的指数函数。该方案主要涉及矩阵矢量乘法,其收敛速度通常为O(△tm-1),因此随着m的增加,它可以采用更大的时间步长。与五阶段四阶Runge-Kutta方法的结果相比,我们展示了CPU时间的减少,并且具有一定的准确性。我们还演示了长时间仿真的错误行为。还介绍了一些案例研究,这些案例表明可以通过添加低成本的重新正交化技术来弥补正交性的损失。

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