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arXiv 1602.05995math.AP
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二维Navier-斯托克斯方程解的离散数据方案及其统计特性

A discrete data assimilation scheme for the solutions of the 2D Navier-Stokes equations and their statistics

Ciprian Foias, Cecilia F. Mondaini, Edriss S. Titi

摘要:

我们适应了以前介绍的连续时间数据同化(降尺度)算法的二维Navier-斯托克斯方程更现实的情况下,当测量得到离散的时间,并可能被污染的系统误差。我们的算法被设计成与一般类的可观的,如低傅立叶模式和局部空间平均有限体积元。在适当的条件下的松弛(轻推)参数,空间网格分辨率和连续测量之间的时间步长,我们得到一个渐近的时间估计之间的差异的近似解和未知的参考解对应的测量,在适当的规范,这表明指数收敛到一个依赖于误差的大小的术语。我们的离散数据同化算法的平稳统计分析也提供。

英文摘要:

We adapt a previously introduced continuous in time data assimilation (downscaling) algorithm for the 2D Navier-Stokes equations to the more realistic case when the measurements are obtained discretely in time and may be contaminated by systematic errors. Our algorithm is designed to work with a general class of observables, such as low Fourier modes and local spatial averages over finite volume elements. Under suitable conditions on the relaxation (nudging) parameter, the spatial mesh resolution and the time step between successive measurements, we obtain an asymptotic in time estimate of the difference between the approximating solution and the unknown reference solution corresponding to the measurements, in an appropriate norm, which shows exponential convergence up to a term which depends on the size of the errors. A stationary statistical analysis of our discrete data assimilation algorithm is also provided.

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