论文标题
迭代收敛的网格质量度量
Grid Quality Measures for Iterative Convergence
论文作者
论文摘要
在本文中，我们讨论了与隐式非结构化网格Navier-Stokes求解器的迭代收敛有关的两种网格质量措施，F-和G测量。 F量度是最小二乘梯度的下限，它是每个单元格中定义的纯几何量，因此可以针对给定的网格进行计算：对于较低的F量值的网格，预计会更快地收敛。 G量度是每个单元格周围指定函数的最小二乘梯度，最小值为零。对于零的G量度的较小值，预计会更快地收敛。在本文中，我们研究了这些措施在二维中无粘性和粘性问题。
In this paper, we discuss two grid-quality measures, F- and G-measures, in relation to iterative convergence of an implicit unstructured-grid Navier-Stokes solver. The F-measure is a lower bound of a least-squares gradient, which is a purely geometrical quantity defined in each cell and thus can be computed for a given grid: faster convergence is expected for a grid with a lower value of the F-measure. The G-measure is a least-squares gradient of a specified function around each cell, with the minimum value of zero. Faster convergence is expected for a smaller value of the G-measure towards zero. In this paper, we investigate these measures for inviscid and viscous problems with unstructured grids in two dimensions.
