PDF(Pubmed)
Abstract：
Turbulent flows have been used for millennia to mix solutes; a familiar example is stirring cream into coffee. However, many energy, environmental, and industrial processes rely on the mixing of solutes in porous media where confinement suppresses inertial turbulence. As a result, mixing is drastically hindered, requiring fluid to permeate long distances for appreciable mixing and introducing additional steps to drive mixing that can be expensive and environmentally harmful. Here, we demonstrate that this limitation can be overcome just by adding dilute amounts of flexible polymers to the fluid. Flow-driven stretching of the polymers generates an elastic instability, driving turbulent-like chaotic flow fluctuations, despite the pore-scale confinement that prohibits typical inertial turbulence. Using in situ imaging, we show that these fluctuations stretch and fold the fluid within the pores along thin layers (\"lamellae\") characterized by sharp solute concentration gradients, driving mixing by diffusion in the pores. This process results in a [Formula: see text] reduction in the required mixing length, a [Formula: see text] increase in solute transverse dispersivity, and can be harnessed to increase the rate at which chemical compounds react by [Formula: see text]-enhancements that we rationalize using turbulence-inspired modeling of the underlying transport processes. Our work thereby establishes a simple, robust, versatile, and predictive way to mix solutes in porous media, with potential applications ranging from large-scale chemical production to environmental remediation.
摘要：
几千年来，湍流一直被用来混合溶质；一个常见的例子是将奶油搅拌到咖啡中。然而,许多能量，环境,工业过程依赖于多孔介质中溶质的混合，在多孔介质中，约束抑制了惯性湍流。因此,混合受到极大阻碍，需要流体渗透长距离进行明显的混合，并引入额外的步骤来驱动混合，这可能是昂贵且对环境有害的。这里,我们证明了这种限制可以通过向流体中添加稀释量的柔性聚合物来克服。聚合物的流动驱动拉伸会产生弹性不稳定性，驱动湍流状的混沌流动波动，尽管孔隙尺度限制禁止典型的惯性湍流。使用原位成像，我们表明，这些波动拉伸和折叠的流体在孔隙内沿着薄层（\“薄片\”）的特点是尖锐的溶质浓度梯度，通过在孔中扩散驱动混合。此过程导致[公式：见文本]减少所需的混合长度，a[公式：见正文]溶质横向分散性增加，并且可以通过[公式：参见正文]来提高化合物反应的速率-我们使用湍流启发的基础运输过程建模来合理化增强。因此，我们的工作建立了一个简单的，健壮,多才多艺，以及在多孔介质中混合溶质的预测方法，具有从大规模化学品生产到环境修复的潜在应用。
