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arXiv 1312.6971math.APmath-phmath.MP
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多维位势Burgers湍流

Multidimensional potential Burgers turbulence

Alexandre Boritchev

摘要:

在空间周期背景下考虑多维广义随机Burgers方程:$ \partial \mathbf{u}/\partial t+$ $(\nabla f(\mathbf{u}) \cdot \nabla)$ $\mathbf{u} -νΔ\mathbf{u}=$ $\nabla η,\quad t \geq 0,\ \mathbf{x} \in \mathbb{T}^d=(\mathbb{R}/\mathbb{Z})^d,$,其中假设$\mathbf{u}$是一个梯度.这里$f$是强凸的并且满足增长条件,$ν$是小的并且是正的,而$η$是随机强迫项,在空间上光滑并且在时间上是白色的。对于该方程的解$\mathbf{u}$,我们研究了$\mathbf{u}$在时间和系综上平均的Sobolev范数:这些范数中的每一个都表现为$ν$的给定负幂。这些结果产生尖锐的上限和下限自然类似物的数量表征的流体动力学湍流,即平均值的增量和能量谱。这些量表现为相关参数的范数的幂,相关参数分别是物理空间中的间隔$\mathbf{l}$和傅立叶空间中的波数$\mathbf{k}$。我们的边界不依赖于初始条件,并保持在$ν$一致。我们概括的\cite{BorW}一维情况下得到的结果,确认物理预测\cite{BK07,GMN10}。请注意,估计值的形式不依赖于维度:$ν, |\mathbf{k}|, \mathbf{l}$的功效在一维和多维设置中相同。

英文摘要:

We consider the multidimensional generalised stochastic Burgers equation in the space-periodic setting: $ \partial \mathbf{u}/\partial t+$ $(\nabla f(\mathbf{u}) \cdot \nabla)$ $\mathbf{u} -νΔ\mathbf{u}=$ $\nabla η,\quad t \geq 0,\ \mathbf{x} \in \mathbb{T}^d=(\mathbb{R}/\mathbb{Z})^d,$ under the assumption that $\mathbf{u}$ is a gradient. Here $f$ is strongly convex and satisfies a growth condition, $ν$ is small and positive, while $η$ is a random forcing term, smooth in space and white in time. For solutions $\mathbf{u}$ of this equation, we study Sobolev norms of $\mathbf{u}$ averaged in time and in ensemble: each of these norms behaves as a given negative power of $ν$. These results yield sharp upper and lower bounds for natural analogues of quantities characterising the hydrodynamical turbulence, namely the averages of the increments and of the energy spectrum. These quantities behave as a power of the norm of the relevant parameter, which is respectively the separation $\mathbf{l}$ in the physical space and the wavenumber $\mathbf{k}$ in the Fourier space. Our bounds do not depend on the initial condition and hold uniformly in $ν$. We generalise the results obtained for the one-dimensional case in \cite{BorW}, confirming the physical predictions in \cite{BK07,GMN10}. Note that the form of the estimates does not depend on the dimension: the powers of $ν, |\mathbf{k}|, \mathbf{l}$ are the same in the one- and the multi-dimensional setting.

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