Stochastic closure for local averages in the finite-difference discretization of the forced Burgers equation
Stochastic closure for local averages in the finite-difference discretization of the forced Burgers equation
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DOI：
10.1007/s00162-012-0270-1
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发表时间：
2013-06-01
影响因子：
3.4
通讯作者：
Timofeyev, I.
中科院分区：
文献类型：
--
作者：
Dolaptchiev, S. I.;Achatz, U.;Timofeyev, I.
我们提出了一种新的方法来构建随机次网格尺度参数化。从一个高分辨率的有限差分离散的一些模型方程，新的方法是基于分裂成快速，小规模和缓慢，大规模的模式，通过平均模型离散相邻的网格单元的模型变量。在此之后，通过应用随机模式缩减过程来消除快模式。这个过程是Majda，Jenfeyev和Vanden-Eijnden提出的模式缩减策略的推广，因为它允许闭合假设中的振荡。新的参数化被施加到强迫Burgers方程，并与Smagorinsky型次网格尺度封闭进行了比较。
We present a new approach for the construction of stochastic subgrid scale parameterizations. Starting from a high-resolution finite-difference discretization of some model equations, the new approach is based on splitting the model variables into fast, small-scale and slow, large-scale modes by averaging the model discretization over neighboring grid cells. After that, the fast modes are eliminated by applying a stochastic mode reduction procedure. This procedure is a generalization of the mode reduction strategy proposed by Majda, Timofeyev & Vanden-Eijnden, in that it allows for oscillations in the closure assumption. The new parameterization is applied to the forced Burgers equation and is compared with a Smagorinsky-type subgrid scale closure.