We propose a new family of high-order explicit generalized-$\alpha$ methods for hyperbolic problems with the feature of dissipation control. Our approach delivers $2k,\, \left(k \in \mathbb{N}\right)$ accuracy order in time by solving $k$ matrix systems explicitly and updating the other $2k$ variables at each time-step. The user can control the numerical dissipation in the discrete spectrum's high-frequency regions by adjusting the method's coefficients. We study the method's spectrum behaviour and show that the CFL condition is independent of the accuracy order. The stability region remains invariant while we increase the accuracy order. Next, we exploit efficient preconditioners for the isogeometric matrix to minimize the computational cost. These preconditioners use a diagonal-scaled Kronecker product of univariate parametric mass matrices; they have a robust performance with respect to the spline degree and the mesh size, and their decomposition structure implies that their application is faster than a matrix-vector product involving the fully-assembled mass matrix. Our high-order schemes require simple modifications of the available implementations of the generalized-$\alpha$ method. Finally, we present numerical examples demonstrating the methodology's performance regarding single- and multi-patch IGA discretizations.
翻译:我们建议对具有消散控制特征的双曲问题采用高阶直线通用- $- alpha$ 的新组合方法。 我们的方法通过明确解决美元矩阵系统并每时步骤更新其他2K美元变量,来及时提供2k,\\\left( k\ in\ mathb{N ⁇ right)$的准确顺序。 用户可以通过调整方法的系数来控制离散频谱高频区域的数字消散。 我们研究方法的频谱行为, 并显示 CFL 条件独立于精确顺序。 稳定性区域仍然变化不定, 而我们增加精确顺序。 下一步, 我们利用对等离散矩阵的有效先决条件来最大限度地降低计算成本。 这些先决条件使用对等尺度的Kronecker产物, 以非等离散谱参数质量矩阵为单位; 用户通过调整方法的波纹度和中位大小, 其分解结构意味着其应用速度比矩阵驱动器产品更快, 包括全额加固的当前通用质量矩阵。 我们的高要求对目前通用的系统进行简单的矩阵化, 展示我们现有的单一数字矩阵方法。