...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computational Physics >On reinitializing level set functions
【24h】

On reinitializing level set functions

机译：重新初始化级别设置功能

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we consider reinitializing level functions through equation [phi]t+sgn([phi]~0)([backward difference][phi]-1)=0 [16]. The method of Russo and Smereka [11] is taken in the spatial discretization of the equation. The spatial discretization is, simply speaking, the second order ENO finite difference with subcell resolution near the interface. Our main interest is on the temporal discretization of the equation. We compare the three temporal discretizations: the second order Runge-Kutta method, the forward Euler method, and a Gauss-Seidel iteration of the forward Euler method. The fact that the time in the equation is fictitious makes a hypothesis that all the temporal discretizations result in the same result in their stationary states. The fact that the absolute stability region of the forward Euler method is not wide enough to include all the eigenvalues of the linearized semi-discrete system of the second order ENO spatial discretization makes another hypothesis that the forward Euler temporal discretization should invoke numerical instability. Our results in this paper contradict both the hypotheses. The Runge-Kutta and Gauss-Seidel methods obtain the second order accuracy, and the forward Euler method converges with order between one and two. Examining all their properties, we conclude that the Gauss-Seidel method is the best among the three. Compared to the Runge-Kutta, it is twice faster and requires memory two times less with the same accuracy.
机译：在本文中，我们考虑通过方程φt+ sgn（φ〜0）（后向差φ-1）＝ 0 [16]来重新初始化水平函数。在方程的空间离散化中采用了Russo和Smereka的方法[11]。简单地说，空间离散化是在接口附近具有子单元分辨率的二阶ENO有限差分。我们的主要兴趣是方程的时间离散。我们比较了三个时间离散化：二阶Runge-Kutta方法，正向Euler方法和正向Euler方法的Gauss-Seidel迭代。方程中的时间是虚拟的事实提出了一个假设，即所有时间离散化在其稳态下都产生相同的结果。正向Euler方法的绝对稳定性区域不够宽，无法包含二阶ENO空间离散化线性半离散系统的所有特征值，这一事实提出了另一个假设，即正向Euler时间离散化应该引起数值不稳定性。我们在本文中的结果与两个假设都相矛盾。 Runge-Kutta方法和Gauss-Seidel方法获得二阶精度，正向Euler方法以一到二的阶数收敛。检查它们的所有属性，我们得出结论，高斯-塞德尔方法是这三种方法中最好的。与Runge-Kutta相比，它速度快两倍，并且以相同的精度所需的内存减少了两倍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号