论文标题
通过随机通道剪切流以及Kraichnan-Majda模型与Taylor-Aris分散剂之间的连接,对扩散的被动标量的扩散标量尺寸和不变性度量
Ergodicity and invariant measures for a diffusing passive scalar advected by a random channel shear flow and the connection between the Kraichnan-Majda model and Taylor-Aris Dispersion
论文作者
论文摘要
我们研究了具有随机剪切流的对流扩散方程的长时间行为,该方程取决于固定的Ornstein-uhlenbeck(OU)过程,以实施无升华边界条件的并行板通道中。我们使用基态特征值扰动方法在\ cite {bronski1997scalar}中提出的基态特征值扰动方法的长期渐近均匀渐近学得出了封闭的表单公式。反过来,我们在{shohat1943 -problem}的结论中有吸引力的结论,我们发现了一个扩散方程,具有随机漂移和确定性增强的扩散,在长时间内具有完全相同的概率分布函数。这样的方程式享有许多崇高的属性,这些特性立即转化为原始问题的成真。特别是,我们确定使用随机场的单个实现的前两个ARIS矩可以用于明确构建所有合奏平均矩。同样,前两个合奏平均的时刻明确预测了任何长期以来的ARIS时刻。我们的公式定量描述了确定性有效扩散对流动的空间结构与随机时间波动之间相互作用的依赖性。这与OU盒中的白噪声案例不可或缺。此外,此近似值提供了有关固定OU过程依赖时间积分的许多身份。我们为腐烂的被动标量的长时间限制概率分布函数(PDF)提供了明确的公式(例如,确定性和随机性)。所有结果均通过蒙特卡洛模拟验证。
We study the long time behavior of an advection-diffusion equation with a random shear flow which depends on a stationary Ornstein-Uhlenbeck (OU) process in parallel-plate channels enforcing the no-flux boundary conditions. We derive a closed form formula for the long time asymptotics of the arbitrary $N$-point correlator using the ground state eigenvalue perturbation approach proposed in \cite{bronski1997scalar}. In turn, appealing to the conclusion of the Hausdorff moment problem \cite{shohat1943problem}, we discover a diffusion equation with a random drift and deterministic enhanced diffusion possessing the exact same probability distribution function at long times. Such equations enjoy many ergodic properties which immediately translate to ergodicity results for the original problem. In particular, we establish that the first two Aris moments using a single realization of the random field can be used to explicitly construct all ensemble averaged moments. Also, the first two ensemble averaged moments explicitly predict any long time centered Aris moment. Our formulae quantitatively depict the dependence of the deterministic effective diffusion on the interaction between spatial structure of flow and random temporal fluctuation. This noteably contrasts the white noise case from the OU case. Further, this approximation provides many identities regarding the stationary OU process dependent time integral. We derive explicit formulae for the decaying passive scalar's long time limiting probability distribution function (PDF) for different types of initial conditions (e.g. deterministic and random). All results are verified by Monte-Carlo simulations.