Grid-based calculation of the Lagrangian mean
Grid-based calculation of the Lagrangian mean
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基于网格的拉格朗日平均值计算
DOI：
10.1017/jfm.2022.233
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发表时间：
2021
影响因子：
3.7
通讯作者：
H. A. Kafiabad
中科院分区：
文献类型：
--
作者：
H. A. Kafiabad
在两个时间尺度的流动中，拉格朗日平均法比欧拉平均法更能有效地分离波和慢动力学。它也出现在许多简化的模型中，这些模型捕获了慢流上的波反馈。然而，它的计算需要及时跟踪粒子，这在基于网格的数值模拟或从定点测量估计中带来了一些困难。为了克服这些困难，我们提出了一种基于网格的迭代方法来计算拉格朗日平均值，而无需及时跟踪粒子，这也减少了并行数值模型中处理器之间的计算，内存占用和通信。为了评估这种方法的准确性，几个例子进行了检查和讨论。我们还探讨了这种方法的应用在浅水方程的上下文中，通过量化的有效性波平均地转平衡-一种修改形式的地转平衡占慢动力学的影响，强波。
Abstract Lagrangian averaging has been shown to be more effective than the Eulerian mean in separating waves from slow dynamics in two time scale flows. It also appears in many reduced models that capture the wave feedback on the slow flow. Its calculation, however, requires tracking particles in time, which imposes several difficulties in grid-based numerical simulations or estimation from fixed-point measurements. To circumvent these difficulties, we propose a grid-based iterative method to calculate the Lagrangian mean without tracking particles in time, which also reduces computation, memory footprint and communication between processors in parallelised numerical models. To assess the accuracy of this method several examples are examined and discussed. We also explore an application of this method in the context of shallow-water equations by quantifying the validity of wave-averaged geostrophic balance – a modified form of geostrophic balance accounting for the effect of strong waves on slow dynamics.
影响因子：
3.7
作者：
Jin-Han Xie;J. Vanneste
通讯作者：
Jin-Han Xie;J. Vanneste
影响因子：
3.7
作者：
C. Rocha;G. Wagner;W. Young
通讯作者：
C. Rocha;G. Wagner;W. Young
影响因子：
3.5
作者：
Asselin, Olivier;Young, William R.
通讯作者：
Young, William R.