We consider the problem of coarse-graining in finite-volume fluid models. Projecting a variable on a fine grid onto a coarser grid will in general cause some information about the solution to be lost. In particular, horizontal divergences and gradients will not be the same when calculated on the coarse grid from a projected solution as when they are calculated on the fine grid. Therefore, it is necessary to choose the method of coarse-graining carefully via a weighted average so that these properties will be conserved in the resulting field. We derive general conditions on the averaging weights that allow these properties to be preserved. We then take the particular case of a regular triangular mesh in which the fine-grid resolution is some integer multiple $N$ of the coarse-grid resolution. For this case we particular values for the averaging weights preserve the divergence for general $N$, and different weights that preserve the gradient for the case $N = 2$. These coarse-grainings are applied to data from FESOM2 simulations and we demonstrate that using this coarse-graining gives a significant improvement over other methods.
翻译:我们考虑有限体积流体模型粗重重重的问题。 在粗体积网格上投放一个变量, 通常会丢失一些关于解决方案的信息。 特别是, 在粗体格上从预测的解决方案中计算出, 粗体积的差值和梯度与在粗体积模型中计算出时的粗体积差值不同。 因此, 有必要通过加权平均值选择粗体积粗重的方法, 以便在生成的字段中保存这些属性。 我们在允许保存这些属性的平均重量上得出一些一般条件。 我们接着将一个典型的三角网格网格网格图作为典型的例子, 其细体积分辨率是粗体格网格分辨率的整数倍数。 对于这个例子, 我们特别为平均重量的数值, 以普通值计为$=2美元, 以不同的权重来保持这个案例的梯度。 这些粗体积的加分法适用于FESOM2模拟的数据, 我们证明使用这种粗重重重重比其他方法有显著的改进。